08_11_audioset_transferlearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa215f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9990e2e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>category</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0001-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0001-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>healthy</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>minus_0614-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>minus_0614-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>minus_0615-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>minus_0615-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>minus_0615-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              filename  category  class\n",
       "0           0001-1.wav  wheezing      1\n",
       "1           0001-2.wav   healthy      0\n",
       "2           0001-3.wav  wheezing      1\n",
       "3           0001-4.wav  wheezing      1\n",
       "4           0002-1.wav   healthy      0\n",
       "...                ...       ...    ...\n",
       "1465  minus_0614-3.wav  wheezing      1\n",
       "1466  minus_0614-4.wav  wheezing      1\n",
       "1467  minus_0615-1.wav  wheezing      1\n",
       "1468  minus_0615-2.wav  wheezing      1\n",
       "1469  minus_0615-4.wav   healthy      0\n",
       "\n",
       "[1470 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file = 'breathdata/augment_train.csv'\n",
    "df = pd.read_csv(Catholic_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "217ae0de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relative_path</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0001-3.wav</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0001-4.wav</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0002-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  relative_path  class\n",
       "0   /0001-1.wav      1\n",
       "1   /0001-2.wav      0\n",
       "2   /0001-3.wav      1\n",
       "3   /0001-4.wav      1\n",
       "4   /0002-1.wav      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relative_path'] = '/' + df['filename'].astype(str)\n",
    "df = df[['relative_path', 'class']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "918677ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'breathdata/train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7a57363",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, random\n",
    "import torch\n",
    "import torchaudio\n",
    "from torchaudio import transforms\n",
    "from IPython.display import Audio\n",
    "\n",
    "class Breath_sound_Util():\n",
    "  \n",
    "  def open(audio_file):\n",
    "    sig, sr = torchaudio.load(audio_file)\n",
    "\n",
    "    return (sig, sr)\n",
    "\n",
    "  def resample(aud, newsr):\n",
    "    sig, sr = aud\n",
    "    \n",
    "    if (sr == newsr):\n",
    "     return aud\n",
    "\n",
    "    num_channels = sig.shape[0]\n",
    "    resig = torchaudio.transforms.Resample(sr, newsr)(sig[:1,:])\n",
    "    if (num_channels > 1):\n",
    "      retwo = torchaudio.transforms.Resample(sr, newsr)(sig[1:,:])\n",
    "      resig = torch.cat([resig, retwo])\n",
    "\n",
    "    return ((resig, newsr))\n",
    "  \n",
    "\n",
    "  def pad(aud, max_ms):\n",
    "    sig, sr = aud\n",
    "    num_rows, sig_len = sig.shape\n",
    "    max_len = sr//1000 * max_ms\n",
    "   \n",
    "    if (sig_len > max_len):\n",
    "      sig = sig[:,:max_len]\n",
    " \n",
    "    elif (sig_len < max_len):\n",
    "\n",
    "      repeated = []\n",
    "      repeated.append(sig)\n",
    "      required_len = max_len - sig_len\n",
    "\n",
    "      while required_len > sig_len : \n",
    "        repeated.append(sig)\n",
    "        require_len -= sig_len\n",
    "      repeated.append(sig[:, :required_len])\n",
    " \n",
    "      sig = torch.cat(repeated, 1)\n",
    "\n",
    "    return (sig, sr)\n",
    "\n",
    "\n",
    "  def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):\n",
    "    sig,sr = aud\n",
    "    top_db = 80\n",
    "    \n",
    "    spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)\n",
    "    spec = transforms.AmplitudeToDB(top_db=top_db)(spec)\n",
    "    return (spec)\n",
    "\n",
    "  def spectro_augment(spec, max_mask_pct=0.1, n_freq_masks=1, n_time_masks=1):\n",
    "    _, n_mels, n_steps = spec.shape\n",
    "    mask_value = spec.mean()\n",
    "    aug_spec = spec\n",
    "\n",
    "    freq_mask_param = max_mask_pct * n_mels\n",
    "    for _ in range(n_freq_masks):\n",
    "      aug_spec = transforms.FrequencyMasking(freq_mask_param)(aug_spec, mask_value)\n",
    "\n",
    "    time_mask_param = max_mask_pct * n_steps\n",
    "    for _ in range(n_time_masks):\n",
    "      aug_spec = transforms.TimeMasking(time_mask_param)(aug_spec, mask_value)\n",
    "\n",
    "    return aug_spec\n",
    "    print(aug_spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fde27747",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset, random_split\n",
    "import torchaudio\n",
    "import torchvision\n",
    "\n",
    "class breathDS(Dataset):\n",
    "    \n",
    "  def __init__(self, df, data_path):\n",
    "    self.df = df\n",
    "    self.data_path = str(data_path)\n",
    "    self.duration = 4000\n",
    "    self.sr = 48000\n",
    "            \n",
    "  def __len__(self):\n",
    "    return len(self.df)    \n",
    "    \n",
    "  def __getitem__(self, idx):\n",
    "    audio_file = self.data_path + self.df.loc[idx, 'relative_path']\n",
    "    class_id = self.df.loc[idx, 'class']\n",
    "    aud = Breath_sound_Util.open(audio_file)\n",
    "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
    "    sgram = Breath_sound_Util.spectro_gram(dur_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
    "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
    "    #aug_sgram = aug_sgram.view([-1, 64])\n",
    "    \n",
    "    \n",
    "    return aug_sgram, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "268605b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import random_split\n",
    "\n",
    "brds = breathDS(df, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21def2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl = torch.utils.data.DataLoader(brds, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c62954b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "369477f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from pytorch_utils import do_mixup, interpolate, pad_framewise_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "69e161ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "PRETRAINED_CHECKPOINT_PATH=\"/home/nextgen/Desktop/braeth/Cnn14_mAP=0.431.pth\"   # Trained by a later code version, achieves higher mAP than the paper.\n",
    "MODEL_TYPE=\"Cnn14\"\n",
    "\n",
    "def init_layer(layer):\n",
    "    \"\"\"Initialize a Linear or Convolutional layer. \"\"\"\n",
    "    nn.init.xavier_uniform_(layer.weight)\n",
    "\n",
    "    if hasattr(layer, 'bias'):\n",
    "        if layer.bias is not None:\n",
    "            layer.bias.data.fill_(0.)\n",
    "            \n",
    "def init_bn(bn):\n",
    "    \"\"\"Initialize a Batchnorm layer. \"\"\"\n",
    "    bn.bias.data.fill_(0.)\n",
    "    bn.weight.data.fill_(1.)\n",
    "    \n",
    "class ConvBlock(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "\n",
    "        super(ConvBlock, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(in_channels=in_channels,\n",
    "                               out_channels=out_channels,\n",
    "                               kernel_size=(3, 3), stride=(1, 1),\n",
    "                               padding=(1, 1), bias=False)\n",
    "\n",
    "        self.conv2 = nn.Conv2d(in_channels=out_channels,\n",
    "                               out_channels=out_channels,\n",
    "                               kernel_size=(3, 3), stride=(1, 1),\n",
    "                               padding=(1, 1), bias=False)\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "\n",
    "        self.init_weight()\n",
    "\n",
    "    def init_weight(self):\n",
    "        init_layer(self.conv1)\n",
    "        init_layer(self.conv2)\n",
    "        init_bn(self.bn1)\n",
    "        init_bn(self.bn2)\n",
    "\n",
    "    def forward(self, input, pool_size=(2, 2), pool_type='avg'):\n",
    "        x = input\n",
    "        x = F.relu_(self.bn1(self.conv1(x)))\n",
    "        x = F.relu_(self.bn2(self.conv2(x)))\n",
    "        if pool_type == 'max':\n",
    "            x = F.max_pool2d(x, kernel_size=pool_size)\n",
    "        elif pool_type == 'avg':\n",
    "            x = F.avg_pool2d(x, kernel_size=pool_size)\n",
    "        elif pool_type == 'avg+max':\n",
    "            x1 = F.avg_pool2d(x, kernel_size=pool_size)\n",
    "            x2 = F.max_pool2d(x, kernel_size=pool_size)\n",
    "            x = x1 + x2\n",
    "        else:\n",
    "            raise Exception('Incorrect argument!')\n",
    "\n",
    "        return x\n",
    "        \n",
    "class Cnn14(nn.Module):\n",
    "    def __init__(self, classes_num=527):\n",
    "\n",
    "        super(Cnn14, self).__init__()\n",
    "\n",
    "        self.bn0 = nn.BatchNorm2d(64)\n",
    "\n",
    "        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)\n",
    "        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)\n",
    "        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)\n",
    "        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)\n",
    "        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)\n",
    "        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)\n",
    "\n",
    "        self.fc1 = nn.Linear(2048, 2048, bias=True)\n",
    "        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)\n",
    "\n",
    "        self.init_weight()\n",
    "\n",
    "    def init_weight(self):\n",
    "        init_bn(self.bn0)\n",
    "\n",
    "    def forward(self, input):\n",
    "\n",
    "        x = input #unsqueeze(1)   # (batch_size, 1, time_steps, mel_bins)\n",
    "        print(x.shape)\n",
    "        #x = torch.unsqueeze(x, 1)\n",
    "        print(x.shape)\n",
    "        x = x.transpose(2, 3)\n",
    "        print(x.shape)\n",
    "        x = x.transpose(1, 3)\n",
    "        x = self.bn0(x)\n",
    "        x = x.transpose(1, 3)\n",
    "        print(x.shape)\n",
    "\n",
    "        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block5(x, pool_size=(1, 1), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)     #(batch_size, 2048, T/16, mel_bins/16)\n",
    "        \n",
    "\n",
    "        return x\n",
    "\n",
    "class Transfer_Cnn14(nn.Module):\n",
    "    def __init__(self, pretrain_checkpoint=None):\n",
    "        super(Transfer_Cnn14, self).__init__()\n",
    "        classes_num = 2\n",
    "        audioset_classes_num = 527\n",
    "        self.base = Cnn14(audioset_classes_num)\n",
    "\n",
    "        #self.init_weights()\n",
    "\n",
    "        if pretrain_checkpoint:\n",
    "            self.load_from_pretrain(pretrain_checkpoint)\n",
    "            \n",
    "        self.base.fc_audioset = nn.Linear(2048, classes_num, bias=True)\n",
    "        # 안쓰이는이유는 multi-class clasification을 생략하기 때문,\n",
    "\n",
    "\n",
    "    #def init_weights(self):\n",
    "        #init_layer(self.fc_transfer)\n",
    "\n",
    "    def load_from_pretrain(self, pretrained_checkpoint):\n",
    "        pretrained_checkpoint=\"/home/nextgen/Desktop/braeth/Cnn14_mAP=0.431.pth\"\n",
    "        checkpoint = torch.load(pretrained_checkpoint)\n",
    "        self.base.load_state_dict(checkpoint['model'])\n",
    "\n",
    "    def forward(self, input):\n",
    "        \"\"\"Input: (batch_size, data_length)\n",
    "        \"\"\"\n",
    "        output = self.base(input)\n",
    "\n",
    "        #embedding = output_dict['embedding']\n",
    "        #clipwise_output = output_dict['clipwise_output']\n",
    "\n",
    "        return output #, clipwise_output\n",
    "    \n",
    "Model1 = Transfer_Cnn14()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "Model1 = Model1.to(device)\n",
    "next(Model1.parameters()).device'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45215e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "PRETRAINED_CHECKPOINT_PATH=\"/home/nextgen/Desktop/braeth/Cnn14_mAP=0.431.pth\"   # Trained by a later code version, achieves higher mAP than the paper.\n",
    "MODEL_TYPE=\"Cnn14\"\n",
    "\n",
    "def init_layer(layer):\n",
    "    \"\"\"Initialize a Linear or Convolutional layer. \"\"\"\n",
    "    nn.init.xavier_uniform_(layer.weight)\n",
    "\n",
    "    if hasattr(layer, 'bias'):\n",
    "        if layer.bias is not None:\n",
    "            layer.bias.data.fill_(0.)\n",
    "            \n",
    "def init_bn(bn):\n",
    "    \"\"\"Initialize a Batchnorm layer. \"\"\"\n",
    "    bn.bias.data.fill_(0.)\n",
    "    bn.weight.data.fill_(1.)\n",
    "    \n",
    "class ConvBlock(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "\n",
    "        super(ConvBlock, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(in_channels=in_channels,\n",
    "                               out_channels=out_channels,\n",
    "                               kernel_size=(3, 3), stride=(1, 1),\n",
    "                               padding=(1, 1), bias=False)\n",
    "\n",
    "        self.conv2 = nn.Conv2d(in_channels=out_channels,\n",
    "                               out_channels=out_channels,\n",
    "                               kernel_size=(3, 3), stride=(1, 1),\n",
    "                               padding=(1, 1), bias=False)\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "\n",
    "        self.init_weight()\n",
    "\n",
    "    def init_weight(self):\n",
    "        init_layer(self.conv1)\n",
    "        init_layer(self.conv2)\n",
    "        init_bn(self.bn1)\n",
    "        init_bn(self.bn2)\n",
    "\n",
    "    def forward(self, input, pool_size=(2, 2), pool_type='avg'):\n",
    "        x = input\n",
    "        x = F.relu_(self.bn1(self.conv1(x)))\n",
    "        x = F.relu_(self.bn2(self.conv2(x)))\n",
    "        if pool_type == 'max':\n",
    "            x = F.max_pool2d(x, kernel_size=pool_size)\n",
    "        elif pool_type == 'avg':\n",
    "            x = F.avg_pool2d(x, kernel_size=pool_size)\n",
    "        elif pool_type == 'avg+max':\n",
    "            x1 = F.avg_pool2d(x, kernel_size=pool_size)\n",
    "            x2 = F.max_pool2d(x, kernel_size=pool_size)\n",
    "            x = x1 + x2\n",
    "        else:\n",
    "            raise Exception('Incorrect argument!')\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1917924",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cnn14(nn.Module):\n",
    "    def __init__(self, classes_num):\n",
    "        \n",
    "        super(Cnn14, self).__init__()\n",
    "\n",
    "        self.bn0 = nn.BatchNorm2d(64)\n",
    "\n",
    "        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)\n",
    "        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)\n",
    "        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)\n",
    "        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)\n",
    "        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)\n",
    "        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)\n",
    "\n",
    "        self.fc1 = nn.Linear(2048, 2048, bias=True)\n",
    "        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)\n",
    "        \n",
    "        self.init_weight()\n",
    "\n",
    "    def init_weight(self):\n",
    "        init_bn(self.bn0)\n",
    "        init_layer(self.fc1)\n",
    "        init_layer(self.fc_audioset)\n",
    " \n",
    "    def forward(self, input, mixup_lambda=None):\n",
    "        \"\"\"\n",
    "        Input: (batch_size, data_length)\"\"\"\n",
    "\n",
    "        #x = self.spectrogram_extractor(input)   # (batch_size, 1, time_steps, freq_bins)\n",
    "        #x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)\n",
    "        \n",
    "        #x = input.unsqueeze(0)\n",
    "        x = input.transpose(1, 3)\n",
    "        print(x.shape)\n",
    "        x = x.transpose(1, 2)\n",
    "        x = self.bn0(x)\n",
    "        print(x.shape)\n",
    "        x = x.transpose(1, 3)\n",
    "        print(x.shape)\n",
    "\n",
    "\n",
    "        # Mixup on spectrogram\n",
    "        if self.training and mixup_lambda is not None:\n",
    "            x = do_mixup(x, mixup_lambda)\n",
    "\n",
    "        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')\n",
    "        x = F.dropout(x, p=0.2, training=self.training)\n",
    "        x = torch.mean(x, dim=3)\n",
    "        \n",
    "        (x1, _) = torch.max(x, dim=2)\n",
    "        x2 = torch.mean(x, dim=2)\n",
    "        x = x1 + x2\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = F.relu_(self.fc1(x))\n",
    "        embedding = F.dropout(x, p=0.5, training=self.training)\n",
    "        clipwise_output = torch.sigmoid(self.fc_audioset(x))\n",
    "        \n",
    "        output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}\n",
    "\n",
    "        return output_dict\n",
    "\n",
    "\n",
    "class Transfer_Cnn14(nn.Module):\n",
    "    def __init__(self,classes_num, pretrain_checkpoint=None):\n",
    "        super(Transfer_Cnn14, self).__init__()\n",
    "        classes_num = 2\n",
    "        audioset_classes_num = 527\n",
    "        self.base = Cnn14(audioset_classes_num)\n",
    "\n",
    "        #self.init_weights()\n",
    "\n",
    "        if pretrain_checkpoint:\n",
    "            self.load_from_pretrain(pretrain_checkpoint)\n",
    "            \n",
    "        self.base.fc_audioset = nn.Linear(2048, classes_num, bias=True)\n",
    "        # 안쓰이는이유는 multi-class clasification을 생략하기 때문,\n",
    "\n",
    "\n",
    "    #def init_weights(self):\n",
    "        #init_layer(self.fc_transfer)\n",
    "\n",
    "    def load_from_pretrain(self, pretrained_checkpoint):\n",
    "        pretrained_checkpoint=\"Cnn14_mAP=0.431.pth\"\n",
    "        checkpoint = torch.load(pretrained_checkpoint)\n",
    "        checkpoint['model'].pop('spectrogram_extractor.stft.conv_imag.weight')\n",
    "        checkpoint['model'].pop('spectrogram_extractor.stft.conv_real.weight')\n",
    "        checkpoint['model'].pop('logmel_extractor.melW')\n",
    "        self.base.load_state_dict(checkpoint['model'])\n",
    "    \n",
    "    def forward(self, input, mixup_lambda=None):\n",
    "        \"\"\"Input: (batch_size, data_length)\n",
    "        \"\"\"\n",
    "        output_dict = self.base(input, mixup_lambda)\n",
    "        embedding = output_dict['embedding']\n",
    "\n",
    "        clipwise_output =  torch.log_softmax(self.base.fc_audioset(embedding), dim=-1)\n",
    "        #output_dict['clipwise_output'] = clipwise_output\n",
    " \n",
    "        return clipwise_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4ec2d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transfer_Cnn14(\n",
       "  (base): Cnn14(\n",
       "    (bn0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (conv_block1): ConvBlock(\n",
       "      (conv1): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_block2): ConvBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_block3): ConvBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_block4): ConvBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_block5): ConvBlock(\n",
       "      (conv1): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (conv2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (conv_block6): ConvBlock(\n",
       "      (conv1): Conv2d(1024, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (conv2): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (bn2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (fc1): Linear(in_features=2048, out_features=2048, bias=True)\n",
       "    (fc_audioset): Linear(in_features=2048, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transfer_Cnn14(classes_num=2,pretrain_checkpoint=\"Cnn14_mAP=0.431.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e3328edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model1=Transfer_Cnn14(classes_num=2,pretrain_checkpoint=\"Cnn14_mAP=0.431.pth\")\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "Model1 = Model1.to(device)\n",
    "next(Model1.parameters()).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81c6f4a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 0 | Inputs [[[[-40.754097  -11.720562  -18.38048   ...  -6.837266   -4.8647327\n",
      "     -5.074279 ]\n",
      "   [-25.343134  -16.523022  -23.623121  ...  -9.705797   -9.1601\n",
      "     -9.577418 ]\n",
      "   [-22.40943   -23.710709  -30.940493  ... -15.608305  -15.211991\n",
      "    -15.489355 ]\n",
      "   ...\n",
      "   [-67.5783    -67.5783    -67.5783    ... -67.5783    -67.5783\n",
      "    -67.5783   ]\n",
      "   [-67.5783    -67.5783    -67.5783    ... -67.5783    -67.5783\n",
      "    -67.5783   ]\n",
      "   [-67.5783    -67.5783    -67.5783    ... -67.5783    -67.5783\n",
      "    -67.5783   ]]]\n",
      "\n",
      "\n",
      " [[[-12.040726   -4.9154253  -1.4111542 ... -14.54794    -3.8779423\n",
      "     -4.230362 ]\n",
      "   [-18.054626   -8.84503    -7.1323757 ... -19.79359    -9.698307\n",
      "    -10.837201 ]\n",
      "   [-30.159636  -16.00459   -17.282757  ... -21.935055  -15.883933\n",
      "    -17.058924 ]\n",
      "   ...\n",
      "   [-61.761513  -61.761513  -61.761513  ... -61.761513  -61.761513\n",
      "    -61.761513 ]\n",
      "   [-61.761513  -61.761513  -61.761513  ... -61.761513  -61.761513\n",
      "    -61.761513 ]\n",
      "   [-77.98657   -75.64758   -70.67766   ... -81.411156  -73.3544\n",
      "    -71.96181  ]]]\n",
      "\n",
      "\n",
      " [[[ -8.9105835  -8.490377  -10.553757  ... -10.020189  -11.762261\n",
      "    -30.299109 ]\n",
      "   [-15.515791  -12.975424  -15.24104   ... -14.578007  -16.914436\n",
      "    -14.165737 ]\n",
      "   [-20.517593  -20.724342  -23.254814  ... -21.485044  -25.251886\n",
      "    -16.753181 ]\n",
      "   ...\n",
      "   [-79.00873   -79.00873   -79.00873   ... -79.00873   -79.00873\n",
      "    -79.00873  ]\n",
      "   [-79.00873   -79.00873   -79.00873   ... -79.00873   -79.00873\n",
      "    -79.00873  ]\n",
      "   [-79.00873   -79.00873   -79.00873   ... -79.00873   -79.00873\n",
      "    -79.00873  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-58.257713  -58.257713  -58.257713  ...  -9.911016   -9.617231\n",
      "     -1.5132855]\n",
      "   [-58.257713  -58.257713  -58.257713  ... -10.727754  -12.945844\n",
      "     -5.6208014]\n",
      "   [-58.257713  -58.257713  -58.257713  ... -14.052195  -16.949217\n",
      "    -12.243976 ]\n",
      "   ...\n",
      "   [-58.257713  -58.257713  -58.257713  ... -58.257713  -58.257713\n",
      "    -58.257713 ]\n",
      "   [-58.257713  -58.257713  -58.257713  ... -58.257713  -58.257713\n",
      "    -58.257713 ]\n",
      "   [-58.257713  -58.257713  -58.257713  ... -58.257713  -58.257713\n",
      "    -58.257713 ]]]\n",
      "\n",
      "\n",
      " [[[-66.74974   -66.74974   -66.74974   ... -14.098979   -8.067211\n",
      "     -5.100564 ]\n",
      "   [-66.74974   -66.74974   -66.74974   ... -20.258972  -14.556884\n",
      "    -11.72051  ]\n",
      "   [-66.74974   -66.74974   -66.74974   ... -31.155918  -29.388802\n",
      "    -28.307306 ]\n",
      "   ...\n",
      "   [-66.74974   -66.74974   -66.74974   ... -66.74974   -66.74974\n",
      "    -66.74974  ]\n",
      "   [-66.74974   -66.74974   -66.74974   ... -66.74974   -66.74974\n",
      "    -66.74974  ]\n",
      "   [-66.74974   -66.74974   -66.74974   ... -66.74974   -66.74974\n",
      "    -66.74974  ]]]\n",
      "\n",
      "\n",
      " [[[-35.061756   -7.384983   -1.4682934 ...  -9.26969   -14.843469\n",
      "     -5.783269 ]\n",
      "   [-16.918547  -11.134444   -5.770774  ... -12.299778  -14.9765\n",
      "    -11.567638 ]\n",
      "   [-19.21162   -18.200386  -13.314915  ... -18.04744   -19.551048\n",
      "    -21.306913 ]\n",
      "   ...\n",
      "   [-72.99309   -72.99309   -72.99309   ... -72.99309   -72.99309\n",
      "    -72.99309  ]\n",
      "   [-72.99309   -72.99309   -72.99309   ... -72.99309   -72.99309\n",
      "    -72.99309  ]\n",
      "   [-72.99309   -72.99309   -72.99309   ... -72.99309   -72.99309\n",
      "    -72.99309  ]]]] | Labels tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 1 | Inputs [[[[ 13.945934    12.503269    14.86809    ...   3.0312223\n",
      "      2.0887592  -18.81947   ]\n",
      "   [ 16.37031     15.185579    14.702749   ...   2.6855512\n",
      "      3.207145    -3.318752  ]\n",
      "   [ 13.137297    11.319098    10.122639   ...  -1.0728964\n",
      "     -0.7115791   -4.0044923 ]\n",
      "   ...\n",
      "   [-44.91208    -47.21557    -47.21557    ... -47.21557\n",
      "    -47.21557    -47.21557   ]\n",
      "   [-43.706085   -47.21557    -47.21557    ... -47.21557\n",
      "    -47.21557    -47.21557   ]\n",
      "   [-43.46749    -47.21557    -47.21557    ... -47.21557\n",
      "    -47.21557    -47.21557   ]]]\n",
      "\n",
      "\n",
      " [[[-20.345852    -6.079896   -10.695988   ... -33.16539\n",
      "    -27.626957   -36.4646    ]\n",
      "   [-18.245737   -11.966478   -13.714901   ... -31.806944\n",
      "    -31.38536    -41.37319   ]\n",
      "   [-22.519587   -23.73727    -20.006802   ... -34.73865\n",
      "    -35.117786   -50.007767  ]\n",
      "   ...\n",
      "   [-71.37117    -71.37117    -71.37117    ... -71.37117\n",
      "    -71.37117    -71.37117   ]\n",
      "   [-71.37117    -71.37117    -71.37117    ... -71.37117\n",
      "    -71.37117    -71.37117   ]\n",
      "   [-71.37117    -71.37117    -71.37117    ... -71.37117\n",
      "    -71.37117    -71.37117   ]]]\n",
      "\n",
      "\n",
      " [[[-19.172665   -17.244993    -6.150449   ...  -8.541705\n",
      "     -7.604822   -13.62039   ]\n",
      "   [-11.889698    -9.446079    -5.055786   ...  -1.976269\n",
      "     -1.139944    -8.160557  ]\n",
      "   [-15.413845    -9.648277    -6.2371025  ...  -2.6246893\n",
      "     -0.44574228 -11.261452  ]\n",
      "   ...\n",
      "   [-61.194008   -61.38008    -61.38008    ... -61.38008\n",
      "    -61.38008    -61.38008   ]\n",
      "   [-61.06779    -61.38008    -61.38008    ... -52.788086\n",
      "    -56.92267    -57.248505  ]\n",
      "   [-61.2679     -61.38008    -61.38008    ... -57.23468\n",
      "    -60.28965    -60.15693   ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[  6.8109455    1.6794162   -4.1261945  ... -12.268953\n",
      "    -10.84722    -20.332756  ]\n",
      "   [  2.266176    -0.09201509  -2.2073574  ...  -6.569522\n",
      "    -15.4794445  -13.502165  ]\n",
      "   [ -4.245471    -4.1696453    0.72655153 ...  -7.2627573\n",
      "    -18.968925   -13.466919  ]\n",
      "   ...\n",
      "   [-46.285023   -45.115124   -45.153534   ... -47.56966\n",
      "    -47.923584   -47.812595  ]\n",
      "   [-43.670277   -46.213814   -45.060383   ... -45.765743\n",
      "    -45.36598    -47.503525  ]\n",
      "   [-44.07524    -48.180885   -48.615154   ... -47.7956\n",
      "    -46.449127   -47.23043   ]]]\n",
      "\n",
      "\n",
      " [[[  4.231811    -0.83956254  -5.01111    ... -11.579494\n",
      "    -12.740665   -24.37107   ]\n",
      "   [-63.79394    -63.79394    -63.79394    ... -63.79394\n",
      "    -63.79394    -63.79394   ]\n",
      "   [-63.79394    -63.79394    -63.79394    ... -63.79394\n",
      "    -63.79394    -63.79394   ]\n",
      "   ...\n",
      "   [-70.17741    -75.42631    -75.42631    ... -75.42631\n",
      "    -75.42631    -75.42631   ]\n",
      "   [-69.953255   -75.42631    -75.42631    ... -75.42631\n",
      "    -75.42631    -75.42631   ]\n",
      "   [-69.74349    -75.42631    -75.42631    ... -75.42631\n",
      "    -75.42631    -75.42631   ]]]\n",
      "\n",
      "\n",
      " [[[-12.006065    -2.4498405   -6.3449664  ... -17.534357\n",
      "    -32.32678    -43.566776  ]\n",
      "   [-10.995356    -8.848913   -11.802303   ... -22.699276\n",
      "    -24.53017    -27.404446  ]\n",
      "   [-14.244624   -22.504421   -21.758179   ... -29.03078\n",
      "    -24.926683   -20.676517  ]\n",
      "   ...\n",
      "   [-77.32866    -77.32866    -77.32866    ... -77.32866\n",
      "    -77.32866    -77.32866   ]\n",
      "   [-75.291245   -70.88341    -76.61659    ... -77.32866\n",
      "    -77.32866    -77.32866   ]\n",
      "   [-75.46611    -71.02934    -76.78464    ... -77.32866\n",
      "    -77.32866    -77.32866   ]]]] | Labels tensor([1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 2 | Inputs [[[[-57.093      -57.093      -57.093      ... -57.093\n",
      "    -57.093      -57.093     ]\n",
      "   [-64.553276   -59.476616   -37.751995   ... -29.581245\n",
      "    -22.763403   -17.271673  ]\n",
      "   [-64.553276   -62.34739    -38.89762    ... -32.897858\n",
      "    -30.808868   -34.21951   ]\n",
      "   ...\n",
      "   [-64.553276   -64.553276   -64.553276   ... -64.553276\n",
      "    -64.553276   -64.553276  ]\n",
      "   [-64.553276   -64.553276   -64.553276   ... -64.553276\n",
      "    -64.553276   -64.553276  ]\n",
      "   [-64.553276   -64.553276   -64.553276   ... -64.553276\n",
      "    -64.553276   -64.553276  ]]]\n",
      "\n",
      "\n",
      " [[[-26.220818   -37.34215    -44.393997   ...   1.7780579\n",
      "      0.23760095 -18.168533  ]\n",
      "   [-24.040937   -31.919418   -40.908665   ...   1.4455316\n",
      "     -0.9537039   -7.202777  ]\n",
      "   [-26.982555   -33.502502   -36.454132   ...  -3.296835\n",
      "     -6.3613296   -9.69911   ]\n",
      "   ...\n",
      "   [-54.318287   -54.318287   -54.318287   ... -54.318287\n",
      "    -54.318287   -54.318287  ]\n",
      "   [-54.318287   -54.318287   -54.318287   ... -54.318287\n",
      "    -54.318287   -54.318287  ]\n",
      "   [-54.318287   -54.318287   -54.318287   ... -54.318287\n",
      "    -54.318287   -54.318287  ]]]\n",
      "\n",
      "\n",
      " [[[-20.123535    -5.5046425   -2.4525075  ... -15.105662\n",
      "    -19.961994   -23.42932   ]\n",
      "   [-12.671586    -9.781458    -8.794078   ... -19.804726\n",
      "    -26.268322   -23.62175   ]\n",
      "   [-15.226015   -17.495308   -22.89795    ... -28.152298\n",
      "    -29.527645   -25.443647  ]\n",
      "   ...\n",
      "   [-79.232574   -79.576996   -79.576996   ... -79.576996\n",
      "    -79.576996   -66.96503   ]\n",
      "   [-76.01728    -76.960785   -71.40079    ... -79.576996\n",
      "    -79.576996   -67.29228   ]\n",
      "   [-76.18207    -77.16819    -71.55845    ... -79.576996\n",
      "    -79.576996   -67.550644  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[ -9.442899   -11.285015    -7.656011   ...  -0.92980355\n",
      "     -8.43734     -8.681878  ]\n",
      "   [ -3.4946656   -9.292788    -0.48320687 ...   0.6142887\n",
      "     -8.109424   -12.614856  ]\n",
      "   [ -2.7777033  -11.111206     1.7016076  ...  -2.2473006\n",
      "     -4.513873   -18.219578  ]\n",
      "   ...\n",
      "   [-44.321667   -64.20862    -64.20862    ... -64.20862\n",
      "    -64.20862    -64.20862   ]\n",
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      "    -64.20862    -64.20862   ]\n",
      "   [-44.18052    -64.20862    -64.20862    ... -64.20862\n",
      "    -64.20862    -64.20862   ]]]\n",
      "\n",
      "\n",
      " [[[-33.551308    -3.074687    -0.86283803 ... -10.295533\n",
      "     -5.021152    -6.6536846 ]\n",
      "   [-20.035727    -4.4948378   -5.69853    ...  -8.216488\n",
      "     -5.1303215   -2.8816876 ]\n",
      "   [-22.590717    -8.382445   -13.735506   ... -10.405556\n",
      "     -7.366514    -3.7626605 ]\n",
      "   ...\n",
      "   [-61.616787   -65.97201    -65.97201    ... -65.97201\n",
      "    -65.97201    -56.45153   ]\n",
      "   [-61.734653   -65.97201    -65.97201    ... -65.97201\n",
      "    -65.97201    -55.93049   ]\n",
      "   [-61.695133   -65.97201    -65.97201    ... -65.97201\n",
      "    -65.97201    -56.206303  ]]]\n",
      "\n",
      "\n",
      " [[[  4.912309    -0.9281525  -11.734732   ... -10.280782\n",
      "     -0.31663984 -21.325064  ]\n",
      "   [ -1.5826824    0.90674615 -12.813328   ...  -6.6595435\n",
      "      3.2477612  -27.27914   ]\n",
      "   [ -0.9008383   -2.1120138  -12.0162525  ...  -3.2779517\n",
      "      4.3370647    2.7099876 ]\n",
      "   ...\n",
      "   [-54.449135   -54.449135   -54.449135   ... -54.449135\n",
      "    -54.449135   -54.449135  ]\n",
      "   [-54.449135   -54.449135   -54.449135   ... -54.449135\n",
      "    -54.449135   -54.449135  ]\n",
      "   [-54.449135   -54.449135   -54.449135   ... -54.449135\n",
      "    -54.449135   -54.449135  ]]]] | Labels tensor([0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 3 | Inputs [[[[-4.57946587e+01 -3.81000671e+01 -3.81000671e+01 ... -3.64739132e+00\n",
      "     2.98208761e+00  7.44584918e-01]\n",
      "   [-4.57946587e+01 -3.81000671e+01 -3.81000671e+01 ... -6.95054913e+00\n",
      "     9.32911225e-03 -4.93973207e+00]\n",
      "   [-4.57946587e+01 -3.81000671e+01 -3.81000671e+01 ... -9.10247421e+00\n",
      "    -4.23427296e+00 -1.20279350e+01]\n",
      "   ...\n",
      "   [-4.57946587e+01 -3.81000671e+01 -3.81000671e+01 ... -4.57946587e+01\n",
      "    -4.57946587e+01 -4.57946587e+01]\n",
      "   [-4.57946587e+01 -3.81000671e+01 -3.81000671e+01 ... -4.57946587e+01\n",
      "    -4.57946587e+01 -4.57946587e+01]\n",
      "   [-4.57946587e+01 -3.81000671e+01 -3.81000671e+01 ... -4.57946587e+01\n",
      "    -4.57946587e+01 -4.57946587e+01]]]\n",
      "\n",
      "\n",
      " [[[-9.55676842e+00 -1.01056337e+01 -3.71691656e+00 ... -1.88539467e+01\n",
      "    -2.37118835e+01 -4.43067360e+01]\n",
      "   [-9.68588924e+00 -1.38018942e+01 -9.23295307e+00 ... -2.19663525e+01\n",
      "    -2.91933804e+01 -2.73299427e+01]\n",
      "   [-7.69474936e+00 -2.01803207e+01 -1.75408268e+01 ... -1.98979874e+01\n",
      "    -1.84887524e+01 -2.09625607e+01]\n",
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      "   [-4.35939407e+01 -4.35939407e+01 -4.35939407e+01 ... -4.35939407e+01\n",
      "    -4.35939407e+01 -4.35939407e+01]\n",
      "   [-6.32157288e+01 -6.34100037e+01 -6.33111954e+01 ... -6.34100037e+01\n",
      "    -6.34100037e+01 -6.34100037e+01]]]\n",
      "\n",
      "\n",
      " [[[ 1.90276480e+00  4.71876955e+00  1.02656136e+01 ... -1.61148281e+01\n",
      "    -1.45206776e+01 -5.90632248e+00]\n",
      "   [-2.80163097e+00 -8.89490604e-01  4.96568727e+00 ... -1.72323952e+01\n",
      "    -1.86080971e+01 -1.24819031e+01]\n",
      "   [-6.71483135e+00 -1.09289351e+01 -4.69569016e+00 ... -2.04498653e+01\n",
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      "   ...\n",
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      "   [-6.97343903e+01 -6.97343903e+01 -6.97343903e+01 ... -6.97343903e+01\n",
      "    -6.97343903e+01 -6.97343903e+01]\n",
      "   [-6.93423157e+01 -6.97343903e+01 -6.97343903e+01 ... -6.97343903e+01\n",
      "    -6.97343903e+01 -6.97343903e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-1.41845217e+01 -7.74728727e+00 -3.91562438e+00 ... -1.19669142e+01\n",
      "    -1.17703800e+01 -2.33484001e+01]\n",
      "   [-1.83640938e+01 -1.27028351e+01 -8.46476364e+00 ... -1.70747852e+01\n",
      "    -1.80040913e+01 -2.99437332e+01]\n",
      "   [-2.36002159e+01 -2.13632431e+01 -1.63065701e+01 ... -2.59036541e+01\n",
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      "    -7.45069809e+01 -7.45069809e+01]\n",
      "   [-7.45069809e+01 -7.45069809e+01 -7.37541809e+01 ... -7.45069809e+01\n",
      "    -7.45069809e+01 -7.45069809e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.44021702e+01 -1.55165663e+01 -6.03754997e+00 ... -6.41188431e+00\n",
      "    -1.14517841e+01 -1.79708843e+01]\n",
      "   [-2.91936207e+01 -1.14404821e+01 -6.16207886e+00 ... -1.07545033e+01\n",
      "    -1.76597881e+01 -2.40428619e+01]\n",
      "   [-2.76314335e+01 -1.06070576e+01 -9.57472610e+00 ... -1.80846939e+01\n",
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      "   [-6.51837158e+01 -6.51837158e+01 -6.51837158e+01 ... -6.51837158e+01\n",
      "    -6.51837158e+01 -6.18623199e+01]\n",
      "   [-6.51837158e+01 -6.51837158e+01 -6.51837158e+01 ... -6.51837158e+01\n",
      "    -6.51837158e+01 -6.18027687e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.56160889e+01 -1.24418335e+01 -1.19389906e+01 ... -1.23737221e+01\n",
      "    -1.44039326e+01 -2.69840050e+01]\n",
      "   [-3.20066109e+01 -1.59016361e+01 -1.55074787e+01 ... -7.77594471e+00\n",
      "    -1.38445663e+01 -3.01693649e+01]\n",
      "   [-1.81998539e+01 -1.68193607e+01 -1.21952686e+01 ... -6.16148281e+00\n",
      "    -1.00061235e+01 -1.63781223e+01]\n",
      "   ...\n",
      "   [-6.31032143e+01 -7.34630661e+01 -7.34630661e+01 ... -7.34630661e+01\n",
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      "   [-6.33607445e+01 -7.34630661e+01 -7.34630661e+01 ... -7.11891479e+01\n",
      "    -7.16851501e+01 -7.34630661e+01]\n",
      "   [-6.34893227e+01 -7.34630661e+01 -7.34630661e+01 ... -7.22662048e+01\n",
      "    -7.26425476e+01 -7.34630661e+01]]]] | Labels tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 4 | Inputs [[[[-54.313282  -17.491909  -14.409614  ... -21.535398  -22.188795\n",
      "    -24.001396 ]\n",
      "   [-26.904427  -15.887119  -19.919788  ... -21.983723  -22.435524\n",
      "    -30.63793  ]\n",
      "   [-20.958147  -17.163372  -26.362925  ... -25.038635  -21.511696\n",
      "    -28.07542  ]\n",
      "   ...\n",
      "   [-78.86351   -78.86351   -78.86351   ... -78.86351   -78.86351\n",
      "    -78.86351  ]\n",
      "   [-78.86351   -78.86351   -78.86351   ... -78.86351   -78.86351\n",
      "    -78.86351  ]\n",
      "   [-78.86351   -78.86351   -78.86351   ... -78.86351   -78.86351\n",
      "    -78.86351  ]]]\n",
      "\n",
      "\n",
      " [[[-25.405434   -9.792915  -36.530174  ...  -9.418368   -7.761237\n",
      "    -13.546924 ]\n",
      "   [-20.37628   -14.535421  -36.530174  ... -11.621847  -11.150988\n",
      "    -18.755934 ]\n",
      "   [-23.222069  -21.224766  -36.530174  ... -17.229778  -17.804142\n",
      "    -26.558578 ]\n",
      "   ...\n",
      "   [-67.08595   -67.08595   -36.530174  ... -67.08595   -67.08595\n",
      "    -54.53119  ]\n",
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      "    -54.77893  ]\n",
      "   [-67.08595   -67.08595   -36.530174  ... -67.08595   -67.08595\n",
      "    -54.78638  ]]]\n",
      "\n",
      "\n",
      " [[[  8.780977   15.792534   16.852135  ...  11.683718   19.472725\n",
      "      6.616256 ]\n",
      "   [  8.440521   19.469734   21.939869  ...  15.572132   16.463978\n",
      "     15.64723  ]\n",
      "   [  4.262091   19.967846   20.838135  ...  12.696942   10.744101\n",
      "     11.588631 ]\n",
      "   ...\n",
      "   [-25.81644   -46.473007  -46.473007  ... -46.473007  -46.473007\n",
      "    -46.473007 ]\n",
      "   [-25.768013  -46.473007  -46.473007  ... -46.473007  -46.473007\n",
      "    -46.473007 ]\n",
      "   [-25.700878  -46.473007  -46.473007  ... -46.473007  -46.473007\n",
      "    -46.473007 ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[  4.4337587  10.238      10.878656  ...   0.7335495   3.8196278\n",
      "     -2.1833758]\n",
      "   [  2.8224077   4.0377417  13.34689   ...  10.016223   10.830764\n",
      "      8.845433 ]\n",
      "   [  5.5929327  17.86569    15.115423  ...  15.332023   13.553323\n",
      "      8.040592 ]\n",
      "   ...\n",
      "   [-47.29722   -47.29722   -47.29722   ... -47.29722   -47.29722\n",
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      "    -47.29722  ]\n",
      "   [-47.29722   -47.29722   -47.29722   ... -47.29722   -47.29722\n",
      "    -47.29722  ]]]\n",
      "\n",
      "\n",
      " [[[-42.69111   -19.84741   -20.235626  ...   7.4225693   5.858619\n",
      "     15.134197 ]\n",
      "   [-42.69111   -15.691867  -10.694432  ...  15.207987   10.822949\n",
      "     18.381477 ]\n",
      "   [-42.69111   -12.5827675 -11.8903265 ...  19.740788   16.200388\n",
      "     21.734585 ]\n",
      "   ...\n",
      "   [-42.69111   -42.69111   -42.69111   ... -42.69111   -42.69111\n",
      "    -40.139145 ]\n",
      "   [-42.69111   -42.69111   -42.69111   ... -42.69111   -42.69111\n",
      "    -41.38614  ]\n",
      "   [-42.69111   -42.69111   -42.69111   ... -42.69111   -42.69111\n",
      "    -40.57744  ]]]\n",
      "\n",
      "\n",
      " [[[ 13.926861   12.511015   14.88254   ...   3.0425138   2.048552\n",
      "    -20.060482 ]\n",
      "   [ 16.382431   15.183016   14.726149  ...   2.6506348   3.1991796\n",
      "     -3.0680084]\n",
      "   [ 13.168936   11.311928   10.150908  ...  -1.1959673  -0.7440519\n",
      "     -3.8149664]\n",
      "   ...\n",
      "   [-44.879932  -47.21339   -47.21339   ... -47.21339   -47.21339\n",
      "    -47.21339  ]\n",
      "   [-43.674183  -47.21339   -47.21339   ... -47.21339   -47.21339\n",
      "    -47.21339  ]\n",
      "   [-43.43389   -47.21339   -47.21339   ... -47.21339   -47.21339\n",
      "    -47.21339  ]]]] | Labels tensor([0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 5 | Inputs [[[[-18.005138   -12.3033695  -12.1325245  ... -14.623995\n",
      "    -15.998421    -1.7072015 ]\n",
      "   [ -5.8337502  -16.874352    -1.2226804  ...   1.474113\n",
      "     -4.4668393   -7.3823094 ]\n",
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      "     -3.2920778   -2.6652546 ]\n",
      "   ...\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      " [[[  9.456142    11.232701     5.8436065  ...   5.8296847\n",
      "      0.7208613   -4.276208  ]\n",
      "   [  7.9418097    9.224933     2.7138827  ...   5.0582275\n",
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      "   [-44.682804   -44.682804   -44.682804   ... -44.682804\n",
      "    -44.682804   -44.682804  ]]]] | Labels tensor([1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 6 | Inputs [[[[-25.161219  -16.474657   -9.639441  ... -24.847755   -8.630866\n",
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      "    -17.331028 ]\n",
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      "\n",
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      "    -66.812256 ]]]] | Labels tensor([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 7 | Inputs [[[[-11.360701    -2.1932135   -0.985235   ... -14.766514\n",
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      "     12.73547      2.3495088 ]\n",
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      "(16, 1, 64, 376)\n",
      "Epoch: 8 | Inputs [[[[-24.549963   -18.902771   -28.920284   ...  -7.4786477\n",
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      "      0.39354366   3.0317786 ]\n",
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      "\n",
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      " [[[-10.952551    -7.8785915   -7.1214843  ...  -4.6907263\n",
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      "   [-66.45743    -66.45743    -66.45743    ... -66.45743\n",
      "    -66.45743    -55.13713   ]]]] | Labels tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 9 | Inputs [[[[ -2.5348413  -10.839921   -10.522876   ...   1.2485204\n",
      "    -10.256349    -7.425893  ]\n",
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      "\n",
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      "      3.0527835    3.0603375 ]\n",
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      "\n",
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      "\n",
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      "    -66.84989    -66.84989   ]]]] | Labels tensor([0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 10 | Inputs [[[[-44.253     -44.253     -44.253     ...   7.1930547   7.1537075\n",
      "     -1.2553787]\n",
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      "\n",
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      "(16, 1, 64, 376)\n",
      "Epoch: 11 | Inputs [[[[  3.2752628  -22.403376   -30.130787   ...  -8.031157\n",
      "     -2.116926     3.6795595 ]\n",
      "   [  2.3592427  -17.3031     -12.211437   ...   6.680136\n",
      "      5.0535507    5.3654485 ]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 12 | Inputs [[[[  2.4698148    1.7191416    2.9808717  ...   2.839027\n",
      "     -8.538731    -1.8642043 ]\n",
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      " [[[-27.88504     -8.136229   -17.632612   ...  -9.068838\n",
      "    -13.792525   -27.33131   ]\n",
      "   [-32.89926    -13.447712   -23.85535    ... -14.577366\n",
      "    -16.451614   -24.382956  ]\n",
      "   [-24.761675   -23.085987   -32.986755   ... -24.075874\n",
      "    -22.550846   -28.317184  ]\n",
      "   ...\n",
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      "    -70.71689    -70.71689   ]\n",
      "   [-70.71689    -70.71689    -70.71689    ... -70.71689\n",
      "    -70.71689    -70.71689   ]\n",
      "   [-70.71689    -70.71689    -70.71689    ... -70.71689\n",
      "    -70.71689    -70.71689   ]]]\n",
      "\n",
      "\n",
      " [[[ -5.215202    -2.5847447   -5.3680754  ... -28.682476\n",
      "    -21.776524   -31.96447   ]\n",
      "   [-11.0496435   -6.5325522   -9.996432   ... -32.03603\n",
      "    -25.579927   -34.398277  ]\n",
      "   [-12.272572   -12.216742   -17.710308   ... -36.526035\n",
      "    -27.911182   -26.939304  ]\n",
      "   ...\n",
      "   [-73.63339    -73.63339    -73.63339    ... -73.63339\n",
      "    -73.63339    -73.63339   ]\n",
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      "    -73.63339    -73.63339   ]\n",
      "   [-73.63339    -73.63339    -73.63339    ... -73.63339\n",
      "    -73.63339    -73.63339   ]]]\n",
      "\n",
      "\n",
      " [[[ 11.31913     19.706226    13.424554   ...   4.928709\n",
      "     -4.3963113  -10.828137  ]\n",
      "   [ 18.587627    23.574888    11.488238   ...   3.2812696\n",
      "     -0.89776725  -3.834834  ]\n",
      "   [ 14.755093    23.038385    13.053276   ...  -0.85626113\n",
      "     -4.9579597   -1.1871568 ]\n",
      "   ...\n",
      "   [-32.29145    -52.194344   -52.194344   ... -52.194344\n",
      "    -52.194344   -52.194344  ]\n",
      "   [-32.20929    -45.533096   -51.38786    ... -52.194344\n",
      "    -52.194344   -52.12059   ]\n",
      "   [-32.114594   -45.879993   -52.194344   ... -52.194344\n",
      "    -52.194344   -52.194344  ]]]] | Labels tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 13 | Inputs [[[[-5.2082535e+01 -2.8061888e+01 -4.5548210e+01 ...  1.2491661e-01\n",
      "     2.9361315e+00 -8.7701350e-01]\n",
      "   [-2.9108284e+01 -3.1428926e+01 -3.2023872e+01 ... -2.0072815e+00\n",
      "     2.8541860e-01 -3.0177393e+00]\n",
      "   [-3.0753132e+01 -3.1662449e+01 -3.0138844e+01 ... -7.7721496e+00\n",
      "    -5.7592559e+00 -4.8555908e+00]\n",
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      "   [-6.6139725e+01 -6.6139725e+01 -6.6139725e+01 ... -6.6139725e+01\n",
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      "   [-6.6139725e+01 -6.6139725e+01 -6.6139725e+01 ... -6.6139725e+01\n",
      "    -6.6139725e+01 -6.6139725e+01]\n",
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      "    -6.6139725e+01 -6.6139725e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.4312923e+01 -1.7177494e+01 -2.1855133e+01 ... -2.6249247e+00\n",
      "     9.3470782e-01 -2.7124331e+01]\n",
      "   [-2.1037327e+01 -2.3586119e+01 -2.6996805e+01 ... -8.2084341e+00\n",
      "    -2.9939842e+00 -3.2707367e+00]\n",
      "   [-5.8400860e+01 -5.8400860e+01 -5.8400860e+01 ... -5.8400860e+01\n",
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      "    -6.7289261e+01 -6.8402313e+01]\n",
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      "    -6.8208290e+01 -6.8402313e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.5640039e+01 -1.9259598e+01 -3.6977306e+01 ... -9.2812262e+00\n",
      "    -1.8434641e+01 -2.4295893e+01]\n",
      "   [-3.0036388e+01 -2.4616726e+01 -3.8090168e+01 ... -1.5402054e+01\n",
      "    -2.3278286e+01 -3.0107857e+01]\n",
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      "    -7.5095497e+01 -7.5095497e+01]\n",
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      "    -7.5095497e+01 -7.5095497e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-2.6940954e+01 -2.3426802e+01 -2.1936171e+01 ... -3.7797546e+01\n",
      "    -3.0514225e+01 -4.1127075e+01]\n",
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      "    -3.3519161e+01 -3.2714279e+01]\n",
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      "    -7.0057869e+01 -7.0057869e+01]\n",
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      "    -7.0057869e+01 -7.0057869e+01]]]\n",
      "\n",
      "\n",
      " [[[ 3.7005730e+00  3.0016587e+00  5.0578647e+00 ...  4.0830135e+00\n",
      "     4.2159467e+00  2.3775120e+00]\n",
      "   [-5.7522082e-01  4.7858329e+00  8.1925068e+00 ...  2.1741988e-02\n",
      "     1.6536008e+00 -4.1828766e+00]\n",
      "   [-1.1754982e+00  4.3810396e+00  6.2512255e+00 ... -1.5218594e+00\n",
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      "   [-3.2311703e+01 -5.1088703e+01 -5.1088703e+01 ... -5.1088703e+01\n",
      "    -5.1088703e+01 -5.1088703e+01]\n",
      "   [-3.2245605e+01 -5.1088703e+01 -5.1088703e+01 ... -5.1088703e+01\n",
      "    -5.1088703e+01 -5.1088703e+01]]]\n",
      "\n",
      "\n",
      " [[[-1.2954727e+01 -4.2971258e+00 -4.8338575e+00 ... -1.6719947e+00\n",
      "    -1.2221283e+01 -6.1593699e+00]\n",
      "   [-5.7325773e+00 -4.4763632e+00 -6.1194839e+00 ...  1.7617426e+00\n",
      "    -4.8250513e+00 -1.1892415e+01]\n",
      "   [-5.9384766e+00 -9.0034485e+00 -1.0443735e+01 ...  1.5738397e+00\n",
      "     3.3134742e+00 -2.2829256e+01]\n",
      "   ...\n",
      "   [-5.0084000e+01 -5.6582108e+01 -5.6582108e+01 ... -5.6582108e+01\n",
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      "   [-5.0114059e+01 -5.6582108e+01 -5.6582108e+01 ... -5.6582108e+01\n",
      "    -5.6582108e+01 -5.6582108e+01]\n",
      "   [-5.0080322e+01 -5.6582108e+01 -5.6582108e+01 ... -5.6582108e+01\n",
      "    -5.6582108e+01 -5.6582108e+01]]]] | Labels tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 14 | Inputs [[[[-16.510796  -10.257481  -15.677141  ...  -7.906414   -0.9582609\n",
      "      5.075324 ]\n",
      "   [-17.9314     -9.732386  -10.971596  ...  -9.303343   -2.5310774\n",
      "     -1.3735616]\n",
      "   [-21.723476  -11.022349  -15.214721  ... -12.804121   -7.77982\n",
      "    -14.140001 ]\n",
      "   ...\n",
      "   [-68.3483    -73.79291   -73.79291   ... -73.79291   -73.79291\n",
      "    -73.79291  ]\n",
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      "    -63.808586 ]\n",
      "   [-68.212585  -73.79291   -73.79291   ... -73.79291   -71.56298\n",
      "    -64.36179  ]]]\n",
      "\n",
      "\n",
      " [[[-15.259344  -11.020367   -5.611231  ... -12.560698   -9.252268\n",
      "     -4.8552756]\n",
      "   [-14.33806   -14.669575   -7.9550886 ... -12.363289  -10.926983\n",
      "     -8.775451 ]\n",
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      "    -51.780617 ]\n",
      "   [-51.780617  -51.780617  -51.780617  ... -51.780617  -51.780617\n",
      "    -51.780617 ]]]\n",
      "\n",
      "\n",
      " [[[ -8.903371  -12.814011  -17.771122  ... -15.701763  -14.217277\n",
      "    -24.933174 ]\n",
      "   [-14.632637  -15.731909  -23.620184  ... -21.128918  -19.93747\n",
      "    -24.04007  ]\n",
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      "    -27.281199 ]\n",
      "   ...\n",
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      "    -80.30208  ]\n",
      "   [-80.30208   -80.30208   -80.30208   ... -80.30208   -80.30208\n",
      "    -80.30208  ]]]\n",
      "\n",
      "\n",
      " ...\n",
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      "\n",
      " [[[-23.35051    -6.8358235  -9.429863  ...  -8.395369   -5.1588464\n",
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      "   [-13.226286  -11.591262  -15.831364  ... -12.371468   -8.211605\n",
      "    -20.447924 ]\n",
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      "    -64.771904 ]\n",
      "   [-64.771904  -64.771904  -64.771904  ... -64.771904  -64.771904\n",
      "    -64.771904 ]]]\n",
      "\n",
      "\n",
      " [[[-11.381007   -2.4188828  -0.9306396 ...  -4.0928993  -2.2337346\n",
      "      1.4639698]\n",
      "   [-15.707363   -5.919799   -7.1198626 ...  -9.85762    -7.2106867\n",
      "     -2.9363525]\n",
      "   [-23.327494  -12.610226  -17.288101  ... -20.989502  -15.670203\n",
      "    -10.376926 ]\n",
      "   ...\n",
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      "    -77.15289  ]\n",
      "   [-77.15289   -77.15289   -77.15289   ... -77.15289   -77.15289\n",
      "    -77.15289  ]]]\n",
      "\n",
      "\n",
      " [[[-15.641561  -17.784504  -26.80649   ... -14.05994   -22.140537\n",
      "    -12.87233  ]\n",
      "   [-22.171324  -22.619371  -28.50906   ... -16.776588  -28.106716\n",
      "    -18.79417  ]\n",
      "   [-39.45924   -31.171955  -30.370495  ... -21.00941   -16.14695\n",
      "    -12.916465 ]\n",
      "   ...\n",
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      "    -72.633965 ]\n",
      "   [-65.62141   -72.633965  -72.633965  ... -72.633965  -72.633965\n",
      "    -72.633965 ]]]] | Labels tensor([0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 15 | Inputs [[[[ -9.730418   -55.658764   -55.658764   ...  -2.6794925\n",
      "     -3.711351   -21.209213  ]\n",
      "   [ -1.124747   -55.658764   -55.658764   ...  -7.444434\n",
      "     -4.0628      -3.569758  ]\n",
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      "     -7.169273    -2.3824553 ]\n",
      "   ...\n",
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      "    -72.62455    -70.65586   ]\n",
      "   [-51.883884   -55.658764   -55.658764   ... -72.15117\n",
      "    -73.036026   -71.26336   ]]]\n",
      "\n",
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      " [[[ -9.641322   -15.667826    -3.287631   ... -14.533744\n",
      "    -14.798468   -13.320762  ]\n",
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      "    -55.88915    -61.568382  ]]]\n",
      "\n",
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      "     -6.327396    -5.0426617 ]\n",
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      "\n",
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      " ...\n",
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      "\n",
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      "\n",
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      "    -65.69803    -65.69803   ]\n",
      "   [-65.69803    -65.69803    -65.69803    ... -65.69803\n",
      "    -65.69803    -65.69803   ]]]] | Labels tensor([1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 16 | Inputs [[[[-2.95788193e+01 -1.73979950e+01 -2.00600433e+01 ... -1.64697247e+01\n",
      "    -7.36536884e+00 -1.74540782e+00]\n",
      "   [-3.33763428e+01 -1.76365185e+01 -2.49444542e+01 ... -1.82716522e+01\n",
      "    -1.03100147e+01 -8.23786068e+00]\n",
      "   [-3.86399498e+01 -2.01048279e+01 -3.30672646e+01 ... -2.08422451e+01\n",
      "    -1.66118279e+01 -2.01000595e+01]\n",
      "   ...\n",
      "   [-7.81892014e+01 -7.81892014e+01 -7.81892014e+01 ... -7.81892014e+01\n",
      "    -7.81892014e+01 -7.81892014e+01]\n",
      "   [-7.81892014e+01 -7.81892014e+01 -7.81892014e+01 ... -7.81892014e+01\n",
      "    -7.81892014e+01 -7.09220047e+01]\n",
      "   [-7.81892014e+01 -7.81892014e+01 -7.81892014e+01 ... -7.81892014e+01\n",
      "    -7.81892014e+01 -7.10119019e+01]]]\n",
      "\n",
      "\n",
      " [[[-4.57027960e+00 -4.24349451e+00 -4.81648779e+00 ... -3.26369743e+01\n",
      "    -2.46137695e+01 -3.13910961e+01]\n",
      "   [-1.12073784e+01 -9.01263905e+00 -9.77103901e+00 ... -3.89647903e+01\n",
      "    -2.93897648e+01 -2.87879543e+01]\n",
      "   [-2.07475109e+01 -1.73703918e+01 -1.86128120e+01 ... -4.55858078e+01\n",
      "    -3.66783447e+01 -3.22667961e+01]\n",
      "   ...\n",
      "   [-8.01269379e+01 -8.01269379e+01 -8.01269379e+01 ... -8.01269379e+01\n",
      "    -8.01269379e+01 -8.01269379e+01]\n",
      "   [-7.27200241e+01 -7.59004669e+01 -7.42228775e+01 ... -8.01269379e+01\n",
      "    -8.01269379e+01 -8.01269379e+01]\n",
      "   [-7.28702927e+01 -7.60885696e+01 -7.44132080e+01 ... -8.01269379e+01\n",
      "    -8.01269379e+01 -8.01269379e+01]]]\n",
      "\n",
      "\n",
      " [[[-4.76490784e+01 -3.15665436e+00 -8.35734844e-01 ... -1.08283329e+01\n",
      "    -5.01960659e+00 -6.67109108e+00]\n",
      "   [-1.91700935e+01 -4.39332867e+00 -5.65084696e+00 ... -8.52320862e+00\n",
      "    -5.15581799e+00 -2.90972257e+00]\n",
      "   [-2.24014568e+01 -8.11080837e+00 -1.36875620e+01 ... -1.06161423e+01\n",
      "    -7.20723915e+00 -3.70897794e+00]\n",
      "   ...\n",
      "   [-6.59875565e+01 -6.59875565e+01 -6.59875565e+01 ... -6.59875565e+01\n",
      "    -6.59875565e+01 -6.59875565e+01]\n",
      "   [-6.59875565e+01 -6.59875565e+01 -6.59875565e+01 ... -6.59875565e+01\n",
      "    -6.59875565e+01 -6.59875565e+01]\n",
      "   [-6.59875565e+01 -6.59875565e+01 -6.59875565e+01 ... -6.59875565e+01\n",
      "    -6.59875565e+01 -6.59875565e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-7.82788181e+00 -7.32812929e+00 -1.16258373e+01 ... -2.62376904e+00\n",
      "    -3.45895100e+00 -7.49246550e+00]\n",
      "   [-1.43956089e+01 -1.36094227e+01 -1.59101315e+01 ... -6.42929268e+00\n",
      "    -8.56344414e+00 -1.19477501e+01]\n",
      "   [-3.61876221e+01 -1.75022049e+01 -1.77737598e+01 ... -1.31932430e+01\n",
      "    -1.75256767e+01 -1.75183907e+01]\n",
      "   ...\n",
      "   [-7.26153946e+01 -7.26153946e+01 -7.26153946e+01 ... -7.26153946e+01\n",
      "    -7.26153946e+01 -7.26153946e+01]\n",
      "   [-7.26153946e+01 -7.26153946e+01 -7.26153946e+01 ... -7.21973572e+01\n",
      "    -7.26153946e+01 -7.26153946e+01]\n",
      "   [-7.26153946e+01 -7.26153946e+01 -7.26153946e+01 ... -7.23649521e+01\n",
      "    -7.26153946e+01 -7.26153946e+01]]]\n",
      "\n",
      "\n",
      " [[[-8.14465427e+00 -7.67381763e+00 -8.01552486e+00 ... -2.13710632e+01\n",
      "    -1.99533539e+01 -3.04226513e+01]\n",
      "   [-1.32234974e+01 -1.35630789e+01 -1.44474277e+01 ... -2.49081268e+01\n",
      "    -2.37938347e+01 -3.11823463e+01]\n",
      "   [-1.65519886e+01 -2.39888153e+01 -2.85086288e+01 ... -2.68715668e+01\n",
      "    -2.58792534e+01 -3.59820442e+01]\n",
      "   ...\n",
      "   [-6.58112030e+01 -6.58112030e+01 -6.58112030e+01 ... -6.58112030e+01\n",
      "    -6.58112030e+01 -6.58112030e+01]\n",
      "   [-6.58112030e+01 -6.58112030e+01 -6.58112030e+01 ... -6.58112030e+01\n",
      "    -6.58112030e+01 -6.58112030e+01]\n",
      "   [-6.58112030e+01 -6.58112030e+01 -6.58112030e+01 ... -6.58112030e+01\n",
      "    -6.58112030e+01 -6.58112030e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.49954338e+01  3.41409254e+00 -2.97201157e+00 ... -7.36299455e-02\n",
      "    -6.07144654e-01 -7.51667070e+00]\n",
      "   [ 8.82159710e+00 -2.50320387e+00 -1.17357409e+00 ...  4.75771666e+00\n",
      "    -1.45284995e-01 -3.90647435e+00]\n",
      "   [ 1.10638275e+01 -5.59366608e+00  8.84480286e+00 ...  4.61591148e+00\n",
      "    -4.71831989e+00 -6.49268198e+00]\n",
      "   ...\n",
      "   [-3.40794907e+01 -6.10007820e+01 -6.10007820e+01 ... -6.10007820e+01\n",
      "    -6.10007820e+01 -6.10007820e+01]\n",
      "   [-3.40209923e+01 -6.10007820e+01 -5.71873703e+01 ... -6.10007820e+01\n",
      "    -6.10007820e+01 -6.10007820e+01]\n",
      "   [-3.39421844e+01 -6.10007820e+01 -5.84248009e+01 ... -6.10007820e+01\n",
      "    -6.10007820e+01 -6.10007820e+01]]]] | Labels tensor([0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 17 | Inputs [[[[ -4.0645213   -3.9302201   -5.1224003  ...   5.2102947\n",
      "     -1.2993984   -0.8645368 ]\n",
      "   [ -9.970625    -5.2447267    2.5541635  ...   2.4692411\n",
      "     -3.5721235   -4.257923  ]\n",
      "   [-16.05296     -3.924553     1.5514594  ...   3.2349827\n",
      "     -9.302386    -9.597078  ]\n",
      "   ...\n",
      "   [-27.056389   -27.056389   -27.056389   ... -27.056389\n",
      "    -27.056389   -27.056389  ]\n",
      "   [-50.902725   -50.902725   -50.902725   ... -50.902725\n",
      "    -50.902725   -50.902725  ]\n",
      "   [-50.902725   -50.902725   -50.902725   ... -50.902725\n",
      "    -50.902725   -50.902725  ]]]\n",
      "\n",
      "\n",
      " [[[-25.521872   -22.180235   -16.684732   ... -28.888653\n",
      "    -20.157887   -15.574112  ]\n",
      "   [-24.101227   -18.73825    -20.84234    ... -33.290478\n",
      "    -25.58383    -22.08      ]\n",
      "   [-28.299416   -21.211117   -28.038034   ... -34.354263\n",
      "    -35.640484   -38.98592   ]\n",
      "   ...\n",
      "   [-71.92418    -71.92418    -71.92418    ... -71.92418\n",
      "    -71.92418    -71.92418   ]\n",
      "   [-71.92418    -71.92418    -71.92418    ... -71.92418\n",
      "    -71.92418    -71.92418   ]\n",
      "   [-71.92418    -71.92418    -71.92418    ... -71.92418\n",
      "    -71.92418    -71.92418   ]]]\n",
      "\n",
      "\n",
      " [[[-38.099068   -36.352955   -35.72775    ... -24.310644\n",
      "    -18.759872   -21.216156  ]\n",
      "   [-44.460556   -39.257362   -40.77157    ... -24.686832\n",
      "    -22.133163   -26.161201  ]\n",
      "   [-56.10721    -42.052345   -48.8874     ... -29.713232\n",
      "    -28.77595    -34.991436  ]\n",
      "   ...\n",
      "   [-66.575424   -66.575424   -66.575424   ... -66.575424\n",
      "    -66.575424   -66.575424  ]\n",
      "   [-66.575424   -66.575424   -66.575424   ... -66.575424\n",
      "    -66.575424   -66.575424  ]\n",
      "   [-66.575424   -66.575424   -66.575424   ... -66.575424\n",
      "    -66.575424   -66.575424  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[  1.0332234    3.8481865    6.5531893  ... -12.83408\n",
      "    -19.469936   -10.644513  ]\n",
      "   [ -2.8916073   -2.509582     1.168414   ... -17.826962\n",
      "    -25.94641    -15.804386  ]\n",
      "   [ -6.916489   -17.583332    -8.673892   ... -26.620752\n",
      "    -31.770088   -23.12894   ]\n",
      "   ...\n",
      "   [-65.25937    -71.382675   -71.382675   ... -71.382675\n",
      "    -71.382675   -71.382675  ]\n",
      "   [-61.93444    -65.27942    -63.652466   ... -71.382675\n",
      "    -71.382675   -71.382675  ]\n",
      "   [-61.81626    -65.406136   -63.788685   ... -71.382675\n",
      "    -71.382675   -71.382675  ]]]\n",
      "\n",
      "\n",
      " [[[-12.411328     5.627717     0.57318777 ...  13.91912\n",
      "     15.3698635   24.780434  ]\n",
      "   [  7.824478     1.5270232    8.688404   ...  26.256311\n",
      "     19.590427    21.54685   ]\n",
      "   [  5.7279687    2.7117646    7.543423   ...  25.9678\n",
      "     16.347778    22.268019  ]\n",
      "   ...\n",
      "   [-39.482723   -49.465694   -49.465694   ... -49.465694\n",
      "    -49.465694   -49.465694  ]\n",
      "   [-39.429604   -49.465694   -49.465694   ... -42.416718\n",
      "    -43.62312    -41.580185  ]\n",
      "   [-39.385506   -49.465694   -49.465694   ... -42.94773\n",
      "    -44.497528   -42.070946  ]]]\n",
      "\n",
      "\n",
      " [[[ -7.900736   -10.255365   -33.736248   ... -19.94271\n",
      "    -21.662128   -12.189257  ]\n",
      "   [-13.918554   -14.9689045  -28.362366   ... -22.76651\n",
      "    -24.89066    -18.650127  ]\n",
      "   [-25.826502   -21.031227   -29.889091   ... -28.59104\n",
      "    -31.428297   -33.48567   ]\n",
      "   ...\n",
      "   [-77.54185    -82.06877    -82.06877    ... -82.06877\n",
      "    -82.06877    -82.06877   ]\n",
      "   [-73.50339    -79.21169    -82.06877    ... -82.06877\n",
      "    -82.06877    -80.96106   ]\n",
      "   [-73.32897    -79.38814    -82.06877    ... -82.06877\n",
      "    -82.06877    -81.18035   ]]]] | Labels tensor([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 18 | Inputs [[[[-4.34064445e+01 -4.34064445e+01 -4.34064445e+01 ... -4.34064445e+01\n",
      "    -4.34064445e+01 -4.34064445e+01]\n",
      "   [ 5.57919407e+00  1.16077816e+00  9.83468819e+00 ... -1.38687086e+00\n",
      "    -1.77283108e+00 -1.02208366e+01]\n",
      "   [ 2.69044375e+00 -3.48737359e+00  9.99441433e+00 ...  1.25525570e+00\n",
      "    -5.57877159e+00 -1.89026966e+01]\n",
      "   ...\n",
      "   [-4.41523781e+01 -5.67686234e+01 -5.67686234e+01 ... -5.67686234e+01\n",
      "    -5.67686234e+01 -5.67686234e+01]\n",
      "   [-4.40919876e+01 -5.67686234e+01 -5.67686234e+01 ... -5.67686234e+01\n",
      "    -5.67686234e+01 -5.67686234e+01]\n",
      "   [-4.40032196e+01 -5.67686234e+01 -5.67686234e+01 ... -5.67686234e+01\n",
      "    -5.67686234e+01 -5.67686234e+01]]]\n",
      "\n",
      "\n",
      " [[[-3.15684605e+00  2.26740098e+00 -2.58548999e+00 ...  6.38722229e+00\n",
      "     6.35872269e+00  8.37951303e-01]\n",
      "   [-6.21073127e-01  5.20429564e+00 -3.62694561e-01 ...  8.38004208e+00\n",
      "     3.49174476e+00  9.12737942e+00]\n",
      "   [-5.11069822e+00  8.41401958e+00 -2.63333440e+00 ...  6.09069061e+00\n",
      "     1.19920826e+00  1.34465227e+01]\n",
      "   ...\n",
      "   [-4.75163994e+01 -5.17937088e+01 -5.17937088e+01 ... -5.17937088e+01\n",
      "    -5.17937088e+01 -5.17937088e+01]\n",
      "   [-4.72866898e+01 -5.17937088e+01 -5.17937088e+01 ... -5.17937088e+01\n",
      "    -5.17937088e+01 -5.17937088e+01]\n",
      "   [-4.72386780e+01 -5.17937088e+01 -5.17937088e+01 ... -5.17937088e+01\n",
      "    -5.17937088e+01 -5.17937088e+01]]]\n",
      "\n",
      "\n",
      " [[[ 8.10335064e+00  1.16323338e+01  6.71934175e+00 ... -1.63041725e+01\n",
      "    -3.41409349e+00  1.97127724e+00]\n",
      "   [ 8.84235287e+00  1.17878714e+01  1.13677387e+01 ... -1.53658333e+01\n",
      "    -4.66823483e+00  1.43987304e-02]\n",
      "   [ 4.90611172e+00  7.81267691e+00  9.43453789e+00 ... -1.28102398e+01\n",
      "    -5.66593409e+00 -3.90750551e+00]\n",
      "   ...\n",
      "   [-4.24902344e+01 -6.04718513e+01 -6.04718513e+01 ... -6.04718513e+01\n",
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      "   [-4.23963165e+01 -5.77053032e+01 -5.84553185e+01 ... -6.04718513e+01\n",
      "    -6.04718513e+01 -6.04718513e+01]\n",
      "   [-4.23116760e+01 -5.79388008e+01 -5.87241135e+01 ... -6.04718513e+01\n",
      "    -6.04718513e+01 -6.04718513e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-7.23654327e+01 -7.23654327e+01 -5.29228554e+01 ... -2.11759605e+01\n",
      "    -1.80431728e+01 -8.56529713e+00]\n",
      "   [-7.23654327e+01 -7.23654327e+01 -5.55167618e+01 ... -2.61550159e+01\n",
      "    -2.21067505e+01 -1.43467789e+01]\n",
      "   [-7.23654327e+01 -7.23654327e+01 -5.93972206e+01 ... -3.29577026e+01\n",
      "    -2.91682796e+01 -2.50319176e+01]\n",
      "   ...\n",
      "   [-7.23654327e+01 -7.23654327e+01 -7.23654327e+01 ... -7.23654327e+01\n",
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      "   [-7.23654327e+01 -7.23654327e+01 -7.23654327e+01 ... -7.23654327e+01\n",
      "    -7.23654327e+01 -7.23654327e+01]\n",
      "   [-7.23654327e+01 -7.23654327e+01 -7.23654327e+01 ... -7.23654327e+01\n",
      "    -7.23654327e+01 -7.23654327e+01]]]\n",
      "\n",
      "\n",
      " [[[-1.43843699e+01  6.25058365e+00  2.04422522e+00 ... -1.01429043e+01\n",
      "    -1.08384705e+01 -7.00738955e+00]\n",
      "   [-1.99210620e+00  4.08438540e+00 -2.36351681e+00 ... -1.22475824e+01\n",
      "    -1.59330473e+01 -7.95803833e+00]\n",
      "   [-4.95389175e+00 -1.57305539e+00 -7.86128569e+00 ... -1.74350243e+01\n",
      "    -2.15310898e+01 -1.15415325e+01]\n",
      "   ...\n",
      "   [-7.22630081e+01 -7.22630081e+01 -7.22630081e+01 ... -7.22630081e+01\n",
      "    -7.22630081e+01 -7.22630081e+01]\n",
      "   [-7.22630081e+01 -7.22630081e+01 -7.22630081e+01 ... -7.22630081e+01\n",
      "    -7.22630081e+01 -7.22630081e+01]\n",
      "   [-7.22630081e+01 -7.22630081e+01 -7.22630081e+01 ... -7.22630081e+01\n",
      "    -7.22630081e+01 -7.22630081e+01]]]\n",
      "\n",
      "\n",
      " [[[-1.70937848e+00 -2.97510171e+00 -4.20516539e+00 ... -6.24469185e+01\n",
      "    -6.24469185e+01 -5.15078259e+00]\n",
      "   [-8.01928997e+00 -8.15344334e+00 -1.02637711e+01 ... -6.24469185e+01\n",
      "    -6.24469185e+01 -8.77363968e+00]\n",
      "   [-1.52027893e+01 -1.74497566e+01 -2.29133949e+01 ... -6.24469185e+01\n",
      "    -6.24469185e+01 -1.55188332e+01]\n",
      "   ...\n",
      "   [-7.11870651e+01 -7.67358246e+01 -7.67358246e+01 ... -6.24469185e+01\n",
      "    -6.24469185e+01 -7.67358246e+01]\n",
      "   [-6.69015198e+01 -7.44855652e+01 -7.30663376e+01 ... -6.24469185e+01\n",
      "    -6.24469185e+01 -7.48753052e+01]\n",
      "   [-6.67437668e+01 -7.46326981e+01 -7.31940384e+01 ... -6.24469185e+01\n",
      "    -6.24469185e+01 -7.52867050e+01]]]] | Labels tensor([0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 19 | Inputs [[[[-13.212777     5.909047     0.28343147 ...   9.572443\n",
      "      7.0702243   -1.9502959 ]\n",
      "   [  8.229617     1.2596266    8.745477   ...   8.240314\n",
      "      7.243674    -8.276473  ]\n",
      "   [  5.989133     2.2641344    7.703207   ...   6.745824\n",
      "      3.8842359   -2.8166723 ]\n",
      "   ...\n",
      "   [-39.040268   -59.159817   -59.159817   ... -59.159817\n",
      "    -59.159817   -59.159817  ]\n",
      "   [-38.983494   -59.159817   -59.159817   ... -59.159817\n",
      "    -59.159817   -59.159817  ]\n",
      "   [-38.93809    -59.159817   -59.159817   ... -59.159817\n",
      "    -59.159817   -59.159817  ]]]\n",
      "\n",
      "\n",
      " [[[ -4.698165    11.220765     6.229055   ...   0.86850417\n",
      "     -3.753027    -2.285778  ]\n",
      "   [ 11.175251     9.536914     2.5259066  ...  -4.839255\n",
      "     -1.3706844   -8.345663  ]\n",
      "   [ 10.7563305    6.117239     0.11173356 ...  -1.4629991\n",
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      "   ...\n",
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      "    -47.242477   -46.389572  ]\n",
      "   [-46.804005   -44.323288   -47.888874   ... -46.051033\n",
      "    -48.878323   -48.727604  ]]]\n",
      "\n",
      "\n",
      " [[[  6.7221904    7.933446    11.325426   ...   8.217535\n",
      "      3.1421103    9.427959  ]\n",
      "   [  4.1397095    5.4110503    9.3496475  ...  10.315144\n",
      "      5.3172436   13.976036  ]\n",
      "   [  2.2110527    2.5271628    4.33394    ...   9.707285\n",
      "     14.602078    13.567034  ]\n",
      "   ...\n",
      "   [-54.77836    -54.77836    -54.77836    ... -54.77836\n",
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      "   [-54.77836    -54.77836    -54.77836    ... -53.3448\n",
      "    -51.38933    -49.479515  ]\n",
      "   [-54.77836    -54.77836    -54.77836    ... -54.35963\n",
      "    -52.24308    -51.499462  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-15.685296    -7.422551    -1.7910404  ...  -4.0452714\n",
      "      2.0389316    0.72365654]\n",
      "   [-22.311653    -9.96081     -6.1780624  ...  -7.820031\n",
      "     -0.44456613  -5.594757  ]\n",
      "   [-28.656868   -15.829639   -14.043927   ... -13.89\n",
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      "   ...\n",
      "   [-46.03899    -68.05468    -68.05468    ... -68.05468\n",
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      "    -67.95341    -68.05468   ]\n",
      "   [-45.956924   -68.05468    -68.05468    ... -68.05468\n",
      "    -68.05468    -68.05468   ]]]\n",
      "\n",
      "\n",
      " [[[-10.402097    -9.974919    -8.6798525  ...  -5.01327\n",
      "    -15.783395   -25.78373   ]\n",
      "   [-17.027927   -16.080816   -14.307671   ...  -9.1481695\n",
      "    -15.234587   -14.252304  ]\n",
      "   [-46.659817   -23.717209   -19.556583   ... -16.61017\n",
      "    -18.847412   -16.49305   ]\n",
      "   ...\n",
      "   [-67.822815   -67.85975    -67.85975    ... -67.85975\n",
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      "   [-66.19269    -67.85975    -67.85975    ... -67.85975\n",
      "    -67.85975    -67.85975   ]\n",
      "   [-67.85975    -67.85975    -67.85975    ... -67.85975\n",
      "    -67.85975    -67.85975   ]]]\n",
      "\n",
      "\n",
      " [[[  2.5596068    2.4503582    4.3403916  ... -17.2177\n",
      "    -13.3946085  -11.757436  ]\n",
      "   [ -3.7776995    4.150829     7.179488   ... -15.94489\n",
      "    -10.2702465  -16.270592  ]\n",
      "   [ 10.968168     6.140309     5.722987   ... -17.086235\n",
      "    -11.254987   -21.628057  ]\n",
      "   ...\n",
      "   [-48.243317   -63.950676   -63.950676   ... -63.950676\n",
      "    -63.950676   -63.950676  ]\n",
      "   [-47.856293   -57.161976   -57.75703    ... -63.950676\n",
      "    -63.950676   -63.950676  ]\n",
      "   [-47.97531    -58.56876    -58.810097   ... -63.950676\n",
      "    -63.950676   -63.950676  ]]]] | Labels tensor([1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 20 | Inputs [[[[-1.9234255e+01 -1.2201213e+01 -1.3344152e+01 ... -1.1929306e+01\n",
      "    -1.8558233e+01 -1.2637538e+01]\n",
      "   [-5.6644197e+00 -1.6171125e+01 -4.4602613e+00 ... -1.0371408e+01\n",
      "    -1.4315266e+01 -1.0433259e+01]\n",
      "   [-4.9073009e+00 -1.2283355e+01 -3.6967671e+00 ... -8.7074976e+00\n",
      "    -1.3708710e+01 -5.2519102e+00]\n",
      "   ...\n",
      "   [-4.7187199e+01 -4.7187199e+01 -4.7187199e+01 ... -4.7187199e+01\n",
      "    -4.7187199e+01 -4.7187199e+01]\n",
      "   [-4.4975086e+01 -5.8059456e+01 -5.8059456e+01 ... -5.8059456e+01\n",
      "    -5.8059456e+01 -5.8059456e+01]\n",
      "   [-4.4903084e+01 -5.8059456e+01 -5.8059456e+01 ... -5.8059456e+01\n",
      "    -5.8059456e+01 -5.8059456e+01]]]\n",
      "\n",
      "\n",
      " [[[ 3.5437360e-02 -3.0728662e+00  5.4010206e-01 ... -8.1946726e+00\n",
      "    -1.0051166e+00 -3.7375212e-01]\n",
      "   [-5.6308942e+00 -8.2504282e+00 -5.4666705e+00 ... -7.5071855e+00\n",
      "    -4.2335639e+00 -6.9436927e+00]\n",
      "   [-1.6471043e+01 -1.5147179e+01 -1.6477468e+01 ... -1.1147127e+01\n",
      "    -1.0512661e+01 -1.8562769e+01]\n",
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      "   [-6.4644806e+01 -7.1589661e+01 -6.8187134e+01 ... -7.1589661e+01\n",
      "    -7.1589661e+01 -6.3620281e+01]\n",
      "   [-6.5220497e+01 -7.1589661e+01 -6.8470627e+01 ... -7.1589661e+01\n",
      "    -7.1589661e+01 -6.3438080e+01]]]\n",
      "\n",
      "\n",
      " [[[-7.5048912e+01 -5.8430099e+01 -4.0602894e+01 ... -1.0566749e+01\n",
      "    -1.1431711e+01 -1.6260853e+01]\n",
      "   [-7.5048912e+01 -5.7299084e+01 -3.7896843e+01 ... -1.6556326e+01\n",
      "    -1.4726815e+01 -1.3408815e+01]\n",
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      "    -7.5048912e+01 -7.5048912e+01]\n",
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      "    -7.5048912e+01 -7.5048912e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-2.7698265e+01 -1.7667910e+01 -1.7845886e+01 ... -1.4808851e+01\n",
      "    -1.7118563e+01 -1.1828230e+01]\n",
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      "    -1.9107861e+01 -1.6002193e+01]\n",
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      "    -6.9508423e+01 -6.9508423e+01]\n",
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      "    -6.9508423e+01 -6.9508423e+01]]]\n",
      "\n",
      "\n",
      " [[[-3.0986650e+01 -8.4747572e+00  5.0740681e+00 ... -9.1506310e+00\n",
      "    -5.0210290e+00 -3.6307147e+00]\n",
      "   [-2.8751345e+01 -9.6895504e+00  3.8709500e+00 ... -6.9929438e+00\n",
      "    -4.9175854e+00 -5.8196239e+00]\n",
      "   [-3.2969788e+01 -1.2546144e+01 -6.0679191e-01 ... -1.0983374e+01\n",
      "    -7.9758844e+00 -7.9327531e+00]\n",
      "   ...\n",
      "   [-7.4654221e+01 -7.4654221e+01 -7.4654221e+01 ... -7.4654221e+01\n",
      "    -7.4654221e+01 -6.4742935e+01]\n",
      "   [-7.4654221e+01 -7.4654221e+01 -6.4611275e+01 ... -7.4654221e+01\n",
      "    -7.3708351e+01 -6.2970272e+01]\n",
      "   [-7.4654221e+01 -7.4654221e+01 -6.4805634e+01 ... -7.4654221e+01\n",
      "    -7.4067017e+01 -6.3572586e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.1976757e+01 -8.7182999e+00 -1.2068266e+01 ... -2.0398102e+01\n",
      "    -1.3954564e+01 -1.3632978e+01]\n",
      "   [-2.5038704e+01 -1.4310008e+01 -1.7336182e+01 ... -2.4986660e+01\n",
      "    -2.0313908e+01 -1.9030731e+01]\n",
      "   [-2.3267799e+01 -2.4626762e+01 -2.6110153e+01 ... -3.2813293e+01\n",
      "    -3.3375694e+01 -2.5898335e+01]\n",
      "   ...\n",
      "   [-6.4643578e+01 -7.5231613e+01 -7.5231613e+01 ... -7.5231613e+01\n",
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      "   [-6.4287529e+01 -7.5231613e+01 -7.5231613e+01 ... -7.5231613e+01\n",
      "    -7.5231613e+01 -7.5231613e+01]\n",
      "   [-6.4310593e+01 -7.5231613e+01 -7.5231613e+01 ... -7.5231613e+01\n",
      "    -7.5231613e+01 -7.5231613e+01]]]] | Labels tensor([1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 21 | Inputs [[[[-14.452514   -13.877572   -20.733393   ... -12.466842\n",
      "    -10.318547    -4.149617  ]\n",
      "   [-18.443104   -19.94717    -23.953846   ... -17.509907\n",
      "    -15.804611   -10.78059   ]\n",
      "   [-20.225822   -32.19274    -29.325962   ... -25.90363\n",
      "    -24.393198   -29.807323  ]\n",
      "   ...\n",
      "   [-63.07714    -63.07714    -63.07714    ... -63.07714\n",
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      "    -63.07714    -63.07714   ]\n",
      "   [-63.07714    -63.07714    -63.07714    ... -63.07714\n",
      "    -63.07714    -63.07714   ]]]\n",
      "\n",
      "\n",
      " [[[-47.958584   -39.81129    -24.975304   ... -18.314777\n",
      "    -21.354391   -29.67957   ]\n",
      "   [-54.585552   -41.26484    -25.40092    ... -23.942848\n",
      "    -25.469505   -33.8737    ]\n",
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      "    -68.78519    -68.78519   ]\n",
      "   [-68.78519    -68.78519    -68.78519    ... -68.78519\n",
      "    -68.78519    -68.78519   ]]]\n",
      "\n",
      "\n",
      " [[[-38.092182   -36.95436    -38.496193   ...  -6.475935\n",
      "     -4.462906    -4.1328373 ]\n",
      "   [-44.463276   -43.59146    -45.012493   ... -10.986422\n",
      "     -8.570741   -10.278292  ]\n",
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      "    -62.69474    -62.69474   ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-56.89015    -56.89015    -56.89015    ... -56.89015\n",
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      "    -64.81792    -64.81792   ]]]\n",
      "\n",
      "\n",
      " [[[-30.868397    -1.9451048   -3.9176707  ... -48.826225\n",
      "     -0.60405207  -6.147599  ]\n",
      "   [ -4.60935     -6.5030737   -6.031645   ... -48.826225\n",
      "      0.08736221 -12.079864  ]\n",
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      "      0.23907754  -8.109981  ]\n",
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      "    -60.06555    -60.06555   ]\n",
      "   [-54.017178   -60.06555    -60.06555    ... -48.826225\n",
      "    -60.06555    -60.06555   ]]]\n",
      "\n",
      "\n",
      " [[[-68.365585   -68.365585   -68.365585   ... -12.861081\n",
      "     -5.9229655   -9.593348  ]\n",
      "   [-68.365585   -68.365585   -68.365585   ... -19.176208\n",
      "     -8.361105   -12.941973  ]\n",
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      "   ...\n",
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      "    -68.365585   -68.365585  ]\n",
      "   [-68.365585   -68.365585   -68.365585   ... -68.365585\n",
      "    -68.365585   -68.365585  ]]]] | Labels tensor([0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 22 | Inputs [[[[-39.25834    -30.288902   -27.534029   ... -12.103449\n",
      "    -20.779781    -6.565489  ]\n",
      "   [-45.894833   -28.06916    -30.015541   ... -13.723355\n",
      "    -18.213354    -5.7751427 ]\n",
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      "    -16.34507     -7.3324013 ]\n",
      "   ...\n",
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      "    -70.45359    -70.45359   ]]]\n",
      "\n",
      "\n",
      " [[[ -3.9841046   -3.8102536   -4.88109    ...   5.1971893\n",
      "     -1.392734    -0.6990739 ]\n",
      "   [-10.108977    -5.2770476    2.5571325  ...   2.4550776\n",
      "     -3.765055    -4.2291007 ]\n",
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      "\n",
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      " [[[  6.193273     7.30304      6.581359   ...   1.0045595\n",
      "      3.2603679   -6.0166626 ]\n",
      "   [ 10.943612     9.81772      8.156892   ...  -3.235159\n",
      "      4.065016    -8.938547  ]\n",
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      "    -58.12072    -61.245255  ]]]\n",
      "\n",
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      " ...\n",
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      " [[[ -5.0468884   -1.8668542   -3.7831414  ... -29.44815\n",
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      "   ...\n",
      "   [-75.7707     -75.7707     -75.7707     ... -75.7707\n",
      "    -75.7707     -75.7707    ]\n",
      "   [-71.60273    -73.26228    -72.35661    ... -75.7707\n",
      "    -75.7707     -75.7707    ]\n",
      "   [-71.94254    -73.47181    -72.508224   ... -75.7707\n",
      "    -75.7707     -75.7707    ]]]\n",
      "\n",
      "\n",
      " [[[ -7.4262896   -8.780018    -6.7636795  ...  -2.9995263\n",
      "    -17.230608    -7.262557  ]\n",
      "   [ -9.703893    -4.504526    -7.2639904  ...  -0.93092537\n",
      "    -13.250426   -13.295059  ]\n",
      "   [ -6.2057185   -2.7596927   -8.524521   ...  -3.4436467\n",
      "     -7.689056   -14.315085  ]\n",
      "   ...\n",
      "   [-55.276276   -55.276276   -55.276276   ... -55.276276\n",
      "    -55.276276   -55.276276  ]\n",
      "   [-55.276276   -55.276276   -55.276276   ... -55.276276\n",
      "    -55.276276   -55.276276  ]\n",
      "   [-55.276276   -55.276276   -55.276276   ... -55.276276\n",
      "    -55.276276   -55.276276  ]]]\n",
      "\n",
      "\n",
      " [[[  5.5680885    6.5978646   -0.82750255 ...  -4.1197515\n",
      "      2.8740282   -3.818067  ]\n",
      "   [ -0.6332919    4.242605    -1.7113862  ...  -1.5325685\n",
      "     -0.36856616  -2.010646  ]\n",
      "   [ -7.196352    -1.0238912   -6.2122145  ...  -3.8712316\n",
      "     -5.8554926    2.787898  ]\n",
      "   ...\n",
      "   [-45.758827   -55.13343    -55.13343    ... -55.13343\n",
      "    -55.13343    -55.13343   ]\n",
      "   [-45.69162    -55.13343    -55.13343    ... -55.13343\n",
      "    -55.13343    -54.64966   ]\n",
      "   [-45.603897   -55.13343    -55.13343    ... -55.13343\n",
      "    -55.13343    -55.13343   ]]]] | Labels tensor([1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 23 | Inputs [[[[-2.52609634e+01 -2.34871655e+01 -2.23569107e+01 ... -2.22059021e+01\n",
      "    -2.08044167e+01 -3.18089409e+01]\n",
      "   [-2.68071537e+01 -2.39106636e+01 -2.75288582e+01 ... -2.72131042e+01\n",
      "    -2.47595749e+01 -3.52061424e+01]\n",
      "   [-3.21218071e+01 -2.65107384e+01 -3.65412216e+01 ... -3.49871368e+01\n",
      "    -3.16935177e+01 -4.14237785e+01]\n",
      "   ...\n",
      "   [-6.61715240e+01 -6.61715240e+01 -6.61715240e+01 ... -6.61715240e+01\n",
      "    -6.61715240e+01 -6.61715240e+01]\n",
      "   [-6.61715240e+01 -6.61715240e+01 -6.61715240e+01 ... -6.61715240e+01\n",
      "    -6.61715240e+01 -6.61715240e+01]\n",
      "   [-6.61715240e+01 -6.61715240e+01 -6.61715240e+01 ... -6.61715240e+01\n",
      "    -6.61715240e+01 -6.61715240e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.29683235e-01 -8.30013514e-01  4.97945309e+00 ... -1.55153856e+01\n",
      "    -5.87763596e+00 -3.34277272e+00]\n",
      "   [-3.32478070e+00 -6.82168055e+00  3.36514807e+00 ... -3.83395100e+00\n",
      "    -2.09329748e+00 -8.42636681e+00]\n",
      "   [-3.81385565e+00 -6.45233727e+00 -8.92384589e-01 ...  1.15035701e+00\n",
      "     2.82061434e+00 -1.73458345e-02]\n",
      "   ...\n",
      "   [-5.68570099e+01 -6.91091690e+01 -6.91091690e+01 ... -6.91091690e+01\n",
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      "   [-5.64301643e+01 -6.83929596e+01 -6.43883057e+01 ... -6.86506577e+01\n",
      "    -6.61965408e+01 -6.64168167e+01]\n",
      "   [-5.65025635e+01 -6.90466385e+01 -6.47223969e+01 ... -6.91091690e+01\n",
      "    -6.67472610e+01 -6.74771500e+01]]]\n",
      "\n",
      "\n",
      " [[[ 8.55624676e+00  2.42334890e+00  1.57839119e+00 ...  4.57472754e+00\n",
      "     1.10615444e+01  5.95545101e+00]\n",
      "   [ 6.06520414e+00  9.77823794e-01 -4.91487265e+00 ...  2.35390276e-01\n",
      "     7.90746880e+00  8.48184299e+00]\n",
      "   [ 1.79980099e+00 -3.98082542e+00 -1.55598173e+01 ...  1.39545584e+00\n",
      "     2.93589354e+00  7.83018160e+00]\n",
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      "    -6.01887093e+01 -5.74007874e+01]\n",
      "   [-3.54519615e+01 -6.24638977e+01 -6.24638977e+01 ... -6.24638977e+01\n",
      "    -6.03831940e+01 -5.76374245e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-8.21072006e+00 -7.71805382e+00 -7.93318796e+00 ... -2.10613155e+01\n",
      "    -2.01676559e+01 -2.94280224e+01]\n",
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      "    -2.38651371e+01 -3.06338997e+01]\n",
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      "    -6.57983322e+01 -6.57983322e+01]\n",
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      "    -6.57983322e+01 -6.57983322e+01]]]\n",
      "\n",
      "\n",
      " [[[-1.05636654e+01 -2.28617592e+01 -2.26512127e+01 ... -9.80605412e+00\n",
      "    -1.07817211e+01 -2.49556046e+01]\n",
      "   [-1.71038227e+01 -2.41427631e+01 -2.63304615e+01 ... -1.43613329e+01\n",
      "    -1.49448862e+01 -2.01977501e+01]\n",
      "   [-3.25145531e+01 -2.82260933e+01 -2.58595200e+01 ... -2.18363228e+01\n",
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      "   ...\n",
      "   [-5.98557320e+01 -7.47835236e+01 -7.47835236e+01 ... -7.47835236e+01\n",
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      "   [-5.97552643e+01 -7.47835236e+01 -7.47835236e+01 ... -7.47835236e+01\n",
      "    -7.47835236e+01 -7.47835236e+01]\n",
      "   [-5.96609268e+01 -7.47835236e+01 -7.47835236e+01 ... -7.47835236e+01\n",
      "    -7.47835236e+01 -7.47835236e+01]]]\n",
      "\n",
      "\n",
      " [[[-1.24638710e+01 -4.98419266e+01 -4.98419266e+01 ... -1.13557329e+01\n",
      "    -1.45071507e+01 -1.36757936e+01]\n",
      "   [-1.55731306e+01 -4.98419266e+01 -4.98419266e+01 ... -1.67003860e+01\n",
      "    -1.81546364e+01 -1.12925539e+01]\n",
      "   [-4.98419266e+01 -4.98419266e+01 -4.98419266e+01 ... -4.98419266e+01\n",
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      "   ...\n",
      "   [-7.00810394e+01 -4.98419266e+01 -4.98419266e+01 ... -7.00810394e+01\n",
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      "   [-7.00810394e+01 -4.98419266e+01 -4.98419266e+01 ... -7.00810394e+01\n",
      "    -7.00810394e+01 -6.86407242e+01]\n",
      "   [-7.00810394e+01 -4.98419266e+01 -4.98419266e+01 ... -7.00810394e+01\n",
      "    -7.00810394e+01 -6.85804825e+01]]]] | Labels tensor([0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 24 | Inputs [[[[-25.239079  -17.444866  -18.14126   ... -30.531458    8.380593\n",
      "     -1.9674104]\n",
      "   [-31.514101  -13.696995  -21.516155  ... -30.531458   13.327028\n",
      "     -2.811101 ]\n",
      "   [-29.887764  -14.593192  -28.2234    ... -30.531458   13.882687\n",
      "     -1.9540602]\n",
      "   ...\n",
      "   [-40.806107  -40.806107  -40.806107  ... -30.531458  -40.806107\n",
      "    -40.806107 ]\n",
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      "    -40.806107 ]\n",
      "   [-40.806107  -40.806107  -40.806107  ... -30.531458  -40.806107\n",
      "    -40.806107 ]]]\n",
      "\n",
      "\n",
      " [[[ -9.384649  -10.116751   -8.024872  ... -27.524689  -17.27675\n",
      "    -33.45287  ]\n",
      "   [-15.513863  -15.554407  -14.489     ... -21.59054   -18.362362\n",
      "    -22.618683 ]\n",
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      "   ...\n",
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      "    -84.057755 ]\n",
      "   [-75.185616  -82.51995   -77.05965   ... -84.057755  -84.057755\n",
      "    -84.057755 ]]]\n",
      "\n",
      "\n",
      " [[[ -8.3601055 -46.8369    -27.673758  ... -15.15093   -14.122081\n",
      "     -9.015162 ]\n",
      "   [ -6.4620023 -44.252274  -32.835354  ... -20.737104  -15.46546\n",
      "    -14.949581 ]\n",
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      "   [-61.11286   -64.23474   -64.2479    ... -70.944336  -70.944336\n",
      "    -70.944336 ]]]\n",
      "\n",
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      " ...\n",
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      " [[[ -5.6143103 -12.687444   -7.1842914 ... -15.086602   -7.6361704\n",
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      "   [ -8.413419  -12.321825  -11.891074  ... -14.231397   -9.935158\n",
      "     -8.342431 ]\n",
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      "    -68.26778  ]]]\n",
      "\n",
      "\n",
      " [[[-41.941303  -25.647776  -10.267154  ... -23.591385  -30.199392\n",
      "    -39.967175 ]\n",
      "   [-62.51429   -62.51429   -62.51429   ... -62.51429   -62.51429\n",
      "    -62.51429  ]\n",
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      "    -85.25673  ]]]\n",
      "\n",
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      " [[[-32.32706    -2.0076346  -2.5051024 ...   6.924744    6.4908953\n",
      "     -2.156668 ]\n",
      "   [-10.498019   -6.881133   -4.2264886 ...   7.0109844   6.3302145\n",
      "      1.3690536]\n",
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      "     -2.751501 ]\n",
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      "    -58.490036 ]\n",
      "   [-58.490036  -58.490036  -58.490036  ... -58.490036  -58.490036\n",
      "    -58.490036 ]]]] | Labels tensor([1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 25 | Inputs [[[[ -4.915656    4.046179    0.3271166 ...   4.410712    5.3430367\n",
      "     13.868273 ]\n",
      "   [  8.6376      6.431734    4.3136783 ...   4.9434977   0.6384284\n",
      "     11.727182 ]\n",
      "   [  9.728214    3.897905    5.2951026 ...   3.1878633  -7.626482\n",
      "      7.3635535]\n",
      "   ...\n",
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      "    -51.421093 ]\n",
      "   [-50.800545  -58.420704  -58.420704  ... -58.420704  -58.420704\n",
      "    -52.136646 ]]]\n",
      "\n",
      "\n",
      " [[[-19.685623  -12.186508   -7.701906  ... -29.476595  -29.678802\n",
      "    -22.532167 ]\n",
      "   [-25.566708  -13.67557   -10.2578125 ... -32.977478  -33.140438\n",
      "    -28.507368 ]\n",
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      "\n",
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      "\n",
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      "    -32.72574  ]\n",
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      "\n",
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      "\n",
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      "   [-45.799778  -45.799778  -45.799778  ... -45.799778  -45.799778\n",
      "    -45.799778 ]]]] | Labels tensor([1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 26 | Inputs [[[[-38.014317   -36.96529    -23.834803   ...  -8.073913\n",
      "     -8.165735    -9.315495  ]\n",
      "   [-44.420322   -43.60243    -22.489614   ...  -9.193682\n",
      "    -11.222611   -11.978018  ]\n",
      "   [-56.877754   -72.729      -22.324017   ... -13.730753\n",
      "    -14.491944   -16.071245  ]\n",
      "   ...\n",
      "   [-72.729      -72.729      -72.729      ... -72.729\n",
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      "    -72.729      -72.729     ]\n",
      "   [-72.729      -72.729      -72.729      ... -72.729\n",
      "    -72.729      -72.729     ]]]\n",
      "\n",
      "\n",
      " [[[ -2.7586925   -2.0039325    0.65577394 ...   3.339783\n",
      "     -9.114015    10.706171  ]\n",
      "   [ -2.0400121   -0.7262308    4.8907814  ...  11.941518\n",
      "     -2.173854     9.423643  ]\n",
      "   [ -4.4098516    4.05554      6.5334244  ...  15.340355\n",
      "      7.560586     4.5678554 ]\n",
      "   ...\n",
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      "    -49.3434     -49.3434    ]]]\n",
      "\n",
      "\n",
      " [[[-61.427322   -61.427322   -13.865021   ... -22.3148\n",
      "     -5.794982    -1.4642607 ]\n",
      "   [-61.427322   -61.427322   -13.515528   ... -12.043773\n",
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      "   ...\n",
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      "\n",
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      " ...\n",
      "\n",
      "\n",
      " [[[-73.84825    -73.84825    -60.782104   ... -25.093164\n",
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      "    -73.84825    -73.84825   ]]]\n",
      "\n",
      "\n",
      " [[[ 16.745834     4.664535     8.164939   ...   9.415941\n",
      "      0.9169203    9.805801  ]\n",
      "   [ 22.472822    13.501477    12.528329   ...   8.973457\n",
      "      9.557217     5.503042  ]\n",
      "   [ 22.199379    14.601398    17.120758   ...   8.948311\n",
      "      9.078557     9.767635  ]\n",
      "   ...\n",
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      "   [-44.761883   -47.12531    -47.12531    ... -47.12531\n",
      "    -47.12531    -47.12531   ]]]\n",
      "\n",
      "\n",
      " [[[ -5.128228    -8.279062   -11.990657   ... -21.241179\n",
      "    -20.523502   -40.364872  ]\n",
      "   [-11.652802   -12.575558   -18.600763   ... -22.393332\n",
      "    -23.549973   -25.421717  ]\n",
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      "    -74.105835   -74.105835  ]\n",
      "   [-71.33837    -74.105835   -74.105835   ... -74.105835\n",
      "    -74.105835   -74.105835  ]]]] | Labels tensor([0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 27 | Inputs [[[[-12.847762   -2.7118373  -6.944193  ...  -7.714038   -9.500627\n",
      "    -46.511856 ]\n",
      "   [-10.553944   -9.115487  -12.162384  ... -12.510739  -15.778235\n",
      "    -23.142607 ]\n",
      "   [-13.33322   -23.34518   -21.411438  ... -20.879309  -30.096903\n",
      "    -26.188772 ]\n",
      "   ...\n",
      "   [-61.26019   -72.06116   -72.06116   ... -72.06116   -72.06116\n",
      "    -63.72281  ]\n",
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      "    -63.628887 ]\n",
      "   [-61.236496  -71.320984  -72.06116   ... -72.06116   -72.06116\n",
      "    -63.539825 ]]]\n",
      "\n",
      "\n",
      " [[[ -9.345214  -13.5322075 -14.276966  ... -55.19029    -7.6142797\n",
      "     -3.8213902]\n",
      "   [-15.970327  -19.632647  -18.374985  ... -55.19029   -11.503105\n",
      "    -10.395485 ]\n",
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      "    -63.15442  ]]]\n",
      "\n",
      "\n",
      " [[[-56.485176  -56.485176  -56.485176  ... -56.485176  -56.485176\n",
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      "    -69.241875 ]]]\n",
      "\n",
      "\n",
      " ...\n",
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      "      0.9427533]\n",
      "   [  2.408759    4.0315146   4.8754616 ...   4.273311   -4.1933784\n",
      "     -5.336753 ]\n",
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      "    -55.357574 ]]]\n",
      "\n",
      "\n",
      " [[[-24.889536  -18.247683  -20.151161  ...  -3.9409404 -21.070692\n",
      "    -26.723667 ]\n",
      "   [-25.545118  -20.522427  -26.509808  ...  -9.540623  -20.934578\n",
      "    -31.327572 ]\n",
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      "    -66.09828  ]]]\n",
      "\n",
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      " [[[-10.711984   -8.205277    4.3989177 ...  -4.926275    1.9748251\n",
      "     -5.548196 ]\n",
      "   [-13.044375  -13.819538   -1.1482503 ...  -9.31226    -3.5941072\n",
      "     -8.233713 ]\n",
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      "   [-57.342773  -67.24716   -64.98029   ... -67.24716   -67.24716\n",
      "    -67.24716  ]]]] | Labels tensor([0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 28 | Inputs [[[[-44.423447   -39.41631    -47.303757   ... -26.85996\n",
      "    -30.649174   -44.741432  ]\n",
      "   [-47.95696    -43.93791    -44.04081    ... -32.809883\n",
      "    -34.596485   -47.799232  ]\n",
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      "    -77.53222    -77.53222   ]]]\n",
      "\n",
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      " [[[-42.679688   -42.679688   -42.679688   ...  13.447578\n",
      "     21.906578    17.458399  ]\n",
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      "     29.777288    23.423637  ]\n",
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      "    -37.064045   -37.29842   ]]]\n",
      "\n",
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      "\n",
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      " ...\n",
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      "\n",
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      " [[[-28.621931   -13.035913   -12.427139   ...   0.58599067\n",
      "     -7.380101     1.3990681 ]\n",
      "   [-32.6358     -16.534782   -15.649437   ...   0.66406834\n",
      "     -3.154721    -4.209961  ]\n",
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      "   [-58.912643   -69.81658    -69.81658    ... -67.77667\n",
      "    -69.81658    -68.77059   ]]]\n",
      "\n",
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      "   [-70.70228    -70.70228    -70.70228    ... -70.70228\n",
      "    -70.70228    -61.71181   ]]]] | Labels tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 29 | Inputs [[[[-4.15600508e-01  2.27504349e+00 -2.82462705e-02 ...  4.38731909e+00\n",
      "     7.86712229e-01 -9.33654690e+00]\n",
      "   [ 4.15983009e+00  2.65757442e+00  1.10238755e+00 ...  6.53617191e+00\n",
      "     1.06462383e+00 -9.27306938e+00]\n",
      "   [ 1.68370932e-01  1.20623922e+00 -2.18451098e-01 ...  5.26944542e+00\n",
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      "    -6.12578087e+01 -6.01413040e+01]]]\n",
      "\n",
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      "\n",
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      "\n",
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      " ...\n",
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      "    -7.23046265e+01 -5.58684769e+01]]]\n",
      "\n",
      "\n",
      " [[[-2.43396072e+01 -1.91274967e+01 -2.38149452e+00 ... -1.05202246e+01\n",
      "    -1.02346058e+01 -6.76846409e+00]\n",
      "   [-2.63539429e+01 -1.95666618e+01 -4.93295145e+00 ... -1.69983978e+01\n",
      "    -1.51837530e+01 -1.33875809e+01]\n",
      "   [-2.63048439e+01 -2.19846745e+01 -1.00879335e+01 ... -3.38688812e+01\n",
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      "   ...\n",
      "   [-6.62176743e+01 -7.13403168e+01 -7.13403168e+01 ... -7.13403168e+01\n",
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      "   [-6.57458572e+01 -7.13403168e+01 -7.13403168e+01 ... -7.13403168e+01\n",
      "    -7.13403168e+01 -7.13403168e+01]\n",
      "   [-6.58488312e+01 -7.13403168e+01 -7.13403168e+01 ... -7.13403168e+01\n",
      "    -7.13403168e+01 -7.13403168e+01]]]\n",
      "\n",
      "\n",
      " [[[ 5.99427223e+00 -8.86424637e+00  2.99324059e+00 ... -9.82544518e+00\n",
      "    -1.41234789e+01 -1.28708115e+01]\n",
      "   [-6.26310229e-01 -6.09844685e+00  6.77933574e-01 ... -1.60921364e+01\n",
      "    -1.94291801e+01 -1.94890404e+01]\n",
      "   [-1.61011600e+00 -1.04095974e+01 -2.19891500e+00 ... -2.91124153e+01\n",
      "    -2.91320515e+01 -3.14345150e+01]\n",
      "   ...\n",
      "   [-6.68541641e+01 -6.68541641e+01 -6.68541641e+01 ... -6.68541641e+01\n",
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      "   [-6.24335709e+01 -6.68541641e+01 -6.60068207e+01 ... -6.68541641e+01\n",
      "    -6.68541641e+01 -6.68541641e+01]\n",
      "   [-6.28031197e+01 -6.68541641e+01 -6.63143234e+01 ... -6.68541641e+01\n",
      "    -6.68541641e+01 -6.68541641e+01]]]] | Labels tensor([1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 30 | Inputs [[[[-23.827656  -12.852584  -21.948895  ... -20.898808  -18.676659\n",
      "    -13.945236 ]\n",
      "   [-26.81074   -18.007204  -25.92772   ... -26.080223  -25.043972\n",
      "    -20.137976 ]\n",
      "   [-32.73073   -26.967222  -31.89371   ... -33.99554   -40.46681\n",
      "    -34.18703  ]\n",
      "   ...\n",
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      "    -72.57472  ]\n",
      "   [-72.57472   -72.57472   -72.57472   ... -72.57472   -72.57472\n",
      "    -72.57472  ]]]\n",
      "\n",
      "\n",
      " [[[-14.337039   -1.4975243  -2.674047  ...   1.2560842   3.9704525\n",
      "     -4.1061244]\n",
      "   [ -6.3989706   1.1458797  -6.161603  ...   9.10933    -2.2421954\n",
      "      6.6079826]\n",
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      "    -35.159504 ]\n",
      "   [-45.068665  -45.068665  -45.068665  ... -45.068665  -45.068665\n",
      "    -45.068665 ]]]\n",
      "\n",
      "\n",
      " [[[  6.0923266   7.0186872   4.6587434 ...   7.378941    5.755454\n",
      "     -2.4959893]\n",
      "   [ 10.703619    9.427518    6.418257  ...   7.403088    7.2982116\n",
      "      6.893707 ]\n",
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      "    -44.52133  ]]]\n",
      "\n",
      "\n",
      " ...\n",
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      " [[[-38.312347  -16.7349    -14.974117  ... -14.749489  -17.72547\n",
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      "    -13.712176 ]\n",
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      "    -59.81638  ]]]\n",
      "\n",
      "\n",
      " [[[-14.469425  -26.463118  -49.02271   ... -12.029439   -5.5213833\n",
      "     -9.874524 ]\n",
      "   [-14.871807  -27.134935  -39.836575  ... -16.887634   -8.289263\n",
      "    -10.817637 ]\n",
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      "    -57.404655 ]\n",
      "   [-64.59884   -65.345695  -65.35935   ... -69.97322   -65.7014\n",
      "    -57.408405 ]]]\n",
      "\n",
      "\n",
      " [[[-22.252056  -18.41132   -16.315233  ... -81.80445   -81.80445\n",
      "    -81.80445  ]\n",
      "   [-21.868889  -20.35346   -22.161541  ... -81.80445   -81.80445\n",
      "    -81.80445  ]\n",
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      "    -81.80445  ]\n",
      "   [-81.80445   -81.80445   -81.80445   ... -81.80445   -81.80445\n",
      "    -81.80445  ]]]] | Labels tensor([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 31 | Inputs [[[[-49.58278    -49.58278    -49.58278    ...  -1.6184859\n",
      "     -5.7231007    3.7081928 ]\n",
      "   [-49.58278    -49.58278    -49.58278    ...  -1.4953179\n",
      "     -8.657373     1.4211112 ]\n",
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      "     -7.4938035   -1.9147943 ]\n",
      "   ...\n",
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      "    -49.58278    -49.58278   ]\n",
      "   [-49.58278    -49.58278    -49.58278    ... -49.58278\n",
      "    -49.58278    -49.58278   ]]]\n",
      "\n",
      "\n",
      " [[[-48.04347    -48.04347    -48.04347    ...   8.3977165\n",
      "      6.8859777   16.70243   ]\n",
      "   [-48.04347    -48.04347    -48.04347    ...  20.75037\n",
      "     19.813763    10.223171  ]\n",
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      "    -47.25696    -47.444077  ]]]\n",
      "\n",
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      " [[[ 15.675756    15.4862795    9.730915   ...   6.2081046\n",
      "     -0.40751842   4.008108  ]\n",
      "   [  9.14143     13.500781     7.23722    ...   4.2782726\n",
      "      2.807461     3.839319  ]\n",
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      "      5.1099195    3.4602735 ]\n",
      "   ...\n",
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      "    -44.172348   -44.172348  ]\n",
      "   [-39.677864   -44.172348   -44.172348   ... -44.172348\n",
      "    -44.172348   -44.172348  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
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      "      6.1655025  -25.580881  ]\n",
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      "      1.8773503    0.743306  ]\n",
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      "     -5.803746    -2.3296165 ]\n",
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      "    -64.63163    -65.396034  ]]]\n",
      "\n",
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      "    -53.072754   -53.072754  ]\n",
      "   [-53.072754   -53.072754   -53.072754   ... -53.072754\n",
      "    -53.072754   -53.072754  ]]]\n",
      "\n",
      "\n",
      " [[[-11.588887   -27.737041   -31.942528   ... -11.383704\n",
      "     -2.775124   -21.819962  ]\n",
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      "    -65.583336   -61.340378  ]\n",
      "   [-62.124844   -64.03003    -64.13257    ... -70.235214\n",
      "    -67.56095    -62.946518  ]]]] | Labels tensor([1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 32 | Inputs [[[[-18.911186   -17.79163     -5.9895163  ...   2.4860075\n",
      "      3.5906446  -28.896196  ]\n",
      "   [-12.277834    -9.829051    -5.21081    ...   5.382965\n",
      "      5.325976     4.822591  ]\n",
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      "      4.748987     6.215608  ]\n",
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      "   [-56.527943   -56.527943   -56.527943   ... -56.527943\n",
      "    -56.527943   -56.527943  ]]]\n",
      "\n",
      "\n",
      " [[[ -2.006472     2.1203904    1.1968881  ... -15.015423\n",
      "    -17.000544    -7.637884  ]\n",
      "   [  0.3455873    0.54229045   4.2203593  ... -21.589325\n",
      "     -7.100391    -6.814823  ]\n",
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      "    -66.97623    -66.97623   ]]]\n",
      "\n",
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      " [[[-69.83841    -69.83841    -69.83841    ... -23.634531\n",
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      "    -69.83841    -69.83841   ]]]\n",
      "\n",
      "\n",
      " ...\n",
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      " [[[-10.812054   -17.761465   -26.365295   ... -14.867297\n",
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      "    -70.63804    -70.63804   ]]]\n",
      "\n",
      "\n",
      " [[[ 10.938958     3.3662677    4.3617187  ...  -4.7407866\n",
      "     -6.867284   -14.422688  ]\n",
      "   [  5.2299366    1.2541724    4.6774635  ...  -9.031998\n",
      "     -9.502266   -10.342879  ]\n",
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      "    -68.412      -68.412     ]\n",
      "   [-42.68786    -67.23003    -64.37812    ... -68.412\n",
      "    -68.412      -68.412     ]]]\n",
      "\n",
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      " [[[-40.371803   -40.371803   -40.371803   ...  13.833442\n",
      "     19.76657     16.85868   ]\n",
      "   [-40.371803   -40.371803   -40.371803   ...  14.012937\n",
      "     20.544498    10.895771  ]\n",
      "   [-40.371803   -40.371803   -40.371803   ...   9.580909\n",
      "     17.911757     2.6238978 ]\n",
      "   ...\n",
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      "    -48.782345   -51.254326  ]\n",
      "   [-40.371803   -40.371803   -40.371803   ... -51.91192\n",
      "    -49.071846   -51.072006  ]]]] | Labels tensor([1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 33 | Inputs [[[[-4.7235332e+00  3.0324175e+00 -1.2558192e+01 ...  4.1933417e+00\n",
      "     8.0241770e-01 -1.5807114e+01]\n",
      "   [-7.3935261e+00 -1.8438092e-01 -1.5368923e+01 ... -1.8477863e+00\n",
      "    -4.4220920e+00 -6.7941098e+00]\n",
      "   [-6.4531355e+00 -6.6027660e+00 -1.6381184e+01 ... -1.4708127e+01\n",
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      "    -6.6267593e+01 -6.6267593e+01]\n",
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      "    -6.6267593e+01 -6.6267593e+01]]]\n",
      "\n",
      "\n",
      " [[[-5.0322418e+01 -3.3480450e+01 -3.2550198e+01 ... -2.1566618e+01\n",
      "    -3.2808556e+01 -2.0468769e+01]\n",
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      "    -3.3324932e+01 -2.4501663e+01]\n",
      "   [-5.7819206e+01 -3.4187614e+01 -2.2229023e+01 ... -3.0439468e+01\n",
      "    -3.2937279e+01 -3.1886143e+01]\n",
      "   ...\n",
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      "   [-7.3931480e+01 -7.3931480e+01 -7.3931480e+01 ... -7.3931480e+01\n",
      "    -7.3931480e+01 -7.3931480e+01]\n",
      "   [-7.3931480e+01 -7.3931480e+01 -7.3931480e+01 ... -7.3931480e+01\n",
      "    -7.3931480e+01 -7.3931480e+01]]]\n",
      "\n",
      "\n",
      " [[[ 6.2651367e+00  5.0544453e-01 -4.1097527e+00 ...  3.2816193e+00\n",
      "    -8.4527378e+00  2.3122376e-01]\n",
      "   [ 1.1621364e+00  4.4552212e+00 -3.3893731e+00 ...  4.6522560e+00\n",
      "    -8.1854284e-01 -2.1527865e+00]\n",
      "   [ 7.7360244e+00  5.3602266e+00 -5.9979577e+00 ...  4.8519388e-02\n",
      "     1.1286590e+00  4.8303218e+00]\n",
      "   ...\n",
      "   [-3.5848930e+01 -6.0380096e+01 -6.0380096e+01 ... -6.0380096e+01\n",
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      "   [-3.5755432e+01 -5.5153740e+01 -6.0380096e+01 ... -6.0380096e+01\n",
      "    -6.0380096e+01 -6.0380096e+01]\n",
      "   [-3.5770920e+01 -5.6909058e+01 -6.0380096e+01 ... -6.0380096e+01\n",
      "    -6.0380096e+01 -6.0380096e+01]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[ 3.1335583e+00  5.9720273e+00  5.5200839e+00 ... -1.5479057e+01\n",
      "    -8.4287205e+00 -1.5255579e-01]\n",
      "   [-3.5000553e+00  2.0043535e+00  1.3044538e+00 ... -1.7127802e+01\n",
      "    -1.1974984e+01  1.4975232e+00]\n",
      "   [-1.5284401e+01 -5.0313325e+00 -5.8916116e+00 ... -2.2308969e+01\n",
      "    -1.5808022e+01 -1.5229006e+00]\n",
      "   ...\n",
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      "   [-4.5650841e+01 -6.5030968e+01 -6.5124542e+01 ... -6.5124542e+01\n",
      "    -6.5124542e+01 -6.5124542e+01]\n",
      "   [-4.5557556e+01 -6.5124542e+01 -6.5124542e+01 ... -6.5124542e+01\n",
      "    -6.5124542e+01 -6.5124542e+01]]]\n",
      "\n",
      "\n",
      " [[[-6.3446213e+01 -6.3446213e+01 -6.3446213e+01 ... -6.3446213e+01\n",
      "    -6.3446213e+01 -6.3446213e+01]\n",
      "   [-6.3446213e+01 -6.3446213e+01 -6.3446213e+01 ... -6.3446213e+01\n",
      "    -6.3446213e+01 -6.3446213e+01]\n",
      "   [-6.3446213e+01 -6.3446213e+01 -6.3446213e+01 ... -6.3446213e+01\n",
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      "   ...\n",
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      "   [-8.0208672e+01 -7.7064369e+01 -7.8160141e+01 ... -8.0208672e+01\n",
      "    -8.0208672e+01 -8.0208672e+01]\n",
      "   [-8.0208672e+01 -7.7190689e+01 -7.8308609e+01 ... -8.0208672e+01\n",
      "    -8.0208672e+01 -8.0208672e+01]]]\n",
      "\n",
      "\n",
      " [[[ 2.3045533e+00  8.6691074e+00  6.3952341e+00 ...  8.7338428e+00\n",
      "     1.8390116e-01  4.1655774e+00]\n",
      "   [ 5.4632640e+00  1.5022110e+01  6.5720606e+00 ...  1.0990716e+01\n",
      "     8.3188782e+00 -1.5299063e+00]\n",
      "   [ 6.4001031e+00  1.4772343e+01  1.7826304e+00 ...  1.0061338e+01\n",
      "     8.7952614e+00 -1.9174196e+00]\n",
      "   ...\n",
      "   [-5.5079140e+01 -5.5079140e+01 -5.5079140e+01 ... -5.5079140e+01\n",
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      "   [-5.4340778e+01 -5.4475342e+01 -5.5079140e+01 ... -5.5079140e+01\n",
      "    -5.5079140e+01 -5.5079140e+01]\n",
      "   [-5.4401756e+01 -5.4827782e+01 -5.5079140e+01 ... -5.5079140e+01\n",
      "    -5.5079140e+01 -5.5079140e+01]]]] | Labels tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 34 | Inputs [[[[-43.62561    -38.92353    -48.12689    ...  -9.188843\n",
      "     -6.969077    -3.1716056 ]\n",
      "   [-36.775536   -40.311626   -41.69252    ... -13.8942795\n",
      "    -10.884733    -9.602389  ]\n",
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      "    -18.020655   -18.70723   ]\n",
      "   ...\n",
      "   [-57.71035    -57.71035    -57.71035    ... -57.71035\n",
      "    -57.71035    -57.71035   ]\n",
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      "    -57.71035    -57.71035   ]\n",
      "   [-57.71035    -57.71035    -57.71035    ... -57.71035\n",
      "    -57.71035    -57.71035   ]]]\n",
      "\n",
      "\n",
      " [[[-16.486614   -10.972221   -15.681673   ... -13.327728\n",
      "    -10.225733   -23.499777  ]\n",
      "   [-23.1204     -16.547335   -21.758125   ... -16.48072\n",
      "    -15.352686   -29.795343  ]\n",
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      "    -23.28314    -27.317097  ]\n",
      "   ...\n",
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      "    -68.699104   -49.063053  ]\n",
      "   [-65.22845    -68.699104   -68.699104   ... -68.699104\n",
      "    -68.699104   -49.10652   ]]]\n",
      "\n",
      "\n",
      " [[[-38.183903   -46.98066    -66.3832     ... -22.894547\n",
      "    -17.546875   -26.709335  ]\n",
      "   [-44.818615   -47.30287    -55.62377    ... -25.973017\n",
      "    -21.655193   -28.211754  ]\n",
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      "   ...\n",
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      "    -69.564095   -69.564095  ]\n",
      "   [-69.564095   -69.564095   -69.564095   ... -69.564095\n",
      "    -69.564095   -69.564095  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-43.93633     -7.624564    -4.058109   ...   1.8747983\n",
      "      4.8525615  -17.752298  ]\n",
      "   [-27.244675   -12.614134    -9.862791   ...  -3.8724542\n",
      "     -0.89076865 -11.804623  ]\n",
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      "   ...\n",
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      "    -64.286995   -65.72239   ]\n",
      "   [-65.72239    -65.72239    -65.72239    ... -65.72239\n",
      "    -64.4315     -65.72239   ]]]\n",
      "\n",
      "\n",
      " [[[ -3.5896688   10.520994     9.463745   ... -11.75584\n",
      "     -6.7224097   -2.2349067 ]\n",
      "   [ 14.443179    15.012409    14.252168   ...   2.907332\n",
      "      1.0234505   -8.3026285 ]\n",
      "   [ 22.208332    19.64848     21.184587   ...   7.119483\n",
      "      2.717176    -4.822946  ]\n",
      "   ...\n",
      "   [-22.92435    -56.623566   -56.623566   ... -56.623566\n",
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      "    -56.623566   -56.623566  ]\n",
      "   [-22.811853   -51.024834   -49.324795   ... -56.623566\n",
      "    -56.623566   -56.623566  ]]]\n",
      "\n",
      "\n",
      " [[[-20.25709    -17.648727    -6.207096   ...   2.4644544\n",
      "      3.543925   -29.454868  ]\n",
      "   [-13.003739    -9.456232    -5.2040095  ...   5.37119\n",
      "      5.3765254    4.931839  ]\n",
      "   [-16.743294    -9.261165    -6.39154    ...   4.2311916\n",
      "      4.8488517    6.240995  ]\n",
      "   ...\n",
      "   [-56.529766   -56.529766   -56.529766   ... -56.529766\n",
      "    -56.529766   -56.529766  ]\n",
      "   [-56.529766   -56.529766   -56.529766   ... -56.529766\n",
      "    -56.529766   -56.529766  ]\n",
      "   [-56.529766   -56.529766   -56.529766   ... -56.529766\n",
      "    -56.529766   -56.529766  ]]]] | Labels tensor([0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 35 | Inputs [[[[-14.923306   -13.017425   -12.77952    ... -21.795303\n",
      "    -14.042618   -14.564103  ]\n",
      "   [-19.129793   -17.450188   -17.76996    ... -23.547798\n",
      "    -18.705715   -17.493881  ]\n",
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      "    -26.81164    -23.847998  ]\n",
      "   ...\n",
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      "    -68.145515   -68.145515  ]\n",
      "   [-68.145515   -68.145515   -68.145515   ... -68.145515\n",
      "    -68.145515   -68.145515  ]]]\n",
      "\n",
      "\n",
      " [[[-37.90523    -38.34475    -39.68437    ... -20.198393\n",
      "    -15.256071   -17.115902  ]\n",
      "   [-44.31916    -44.91307    -46.30945    ... -26.598133\n",
      "    -19.919155   -23.116667  ]\n",
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      "   ...\n",
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      "    -66.699      -66.699     ]\n",
      "   [-66.699      -66.699      -66.699      ... -66.699\n",
      "    -66.699      -66.699     ]]]\n",
      "\n",
      "\n",
      " [[[-20.169037    -2.4889634  -11.631049   ... -14.554932\n",
      "    -17.369404   -24.284803  ]\n",
      "   [ -4.782899    -5.648334   -11.915263   ... -18.291084\n",
      "    -19.663574   -27.955284  ]\n",
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      "    -79.32342    -79.32342   ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[ -7.2417574   -6.3047266   -9.434206   ... -12.928764\n",
      "    -14.669754    -7.651664  ]\n",
      "   [-13.787464   -10.208601   -13.767601   ... -16.720427\n",
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      "    -70.226585   -70.226585  ]]]\n",
      "\n",
      "\n",
      " [[[  6.5857086   15.32531     13.322485   ...  -4.7218547\n",
      "    -10.035005   -13.35576   ]\n",
      "   [  2.3802717   12.671022    10.560942   ...  -2.4323072\n",
      "    -10.341563    -9.926067  ]\n",
      "   [  5.099594     7.2055583    4.719861   ...  -3.5629404\n",
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      "   ...\n",
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      "    -63.458412   -63.458412  ]\n",
      "   [-58.198906   -54.75542    -56.98445    ... -63.458412\n",
      "    -63.458412   -63.458412  ]]]\n",
      "\n",
      "\n",
      " [[[  3.762449     5.791373    -4.9492483  ...   2.5660796\n",
      "      9.639287     3.834093  ]\n",
      "   [ 11.389372     0.7927677    4.3115916  ...   1.7377031\n",
      "      9.494196     4.1094475 ]\n",
      "   [ 10.18313     -0.5686646    8.658036   ...  -0.10649727\n",
      "      7.467924     2.729835  ]\n",
      "   ...\n",
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      "    -58.79703    -62.15708   ]\n",
      "   [-34.445637   -61.883553   -60.856686   ... -62.15708\n",
      "    -59.049713   -62.15708   ]]]] | Labels tensor([0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 36 | Inputs [[[[-16.800472  -17.866217  -16.859266  ... -15.565301  -13.144173\n",
      "    -14.058127 ]\n",
      "   [-22.890646  -23.804947  -19.455765  ... -21.86956   -15.674686\n",
      "    -20.286049 ]\n",
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      "    -25.579033 ]\n",
      "   ...\n",
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      "    -67.35894  ]]]\n",
      "\n",
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      " [[[-76.14468   -64.85238   -49.237118  ... -11.768962  -15.311148\n",
      "    -13.407572 ]\n",
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      "\n",
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      " ...\n",
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      "    -18.448105 ]\n",
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      "\n",
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      "    -72.46033  ]]]] | Labels tensor([0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 37 | Inputs [[[[ 13.342415     9.98185     11.4248295  ...  -4.8115234\n",
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      "   [  7.0437627   10.904613    13.60341    ...  -4.292906\n",
      "      0.3194153    4.394511  ]\n",
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      "    -51.972996   -51.972996  ]]]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "    -74.16951    -74.16951   ]]]] | Labels tensor([1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 38 | Inputs [[[[-46.796894   -20.150183   -20.862583   ... -15.341919\n",
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      "\n",
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      "\n",
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      "    -64.671715   -63.916683  ]]]] | Labels tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 1, 64, 376)\n",
      "Epoch: 39 | Inputs [[[[-64.9833     -54.107353   -48.52987    ...  -4.3750005\n",
      "     -3.3697777  -27.239561  ]\n",
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      "(16, 1, 64, 376)\n",
      "Epoch: 40 | Inputs [[[[-45.67627    -45.67627    -45.67627    ...   5.280381\n",
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      "    -57.252502   -62.976395  ]\n",
      "   [-61.996445   -73.15392    -73.15392    ... -73.15392\n",
      "    -57.16342    -62.906395  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[ 11.89439      6.6992607   12.77856    ...  -5.6983767\n",
      "     -0.67705506   6.506769  ]\n",
      "   [  5.769446     3.090478     8.652073   ...   1.6954098\n",
      "     -0.5926649    4.85763   ]\n",
      "   [ -5.730652     7.611091     6.1126604  ...  -1.7619071\n",
      "     -5.2011294    4.3761773 ]\n",
      "   ...\n",
      "   [-47.810585   -52.09137    -52.09137    ... -52.09137\n",
      "    -52.09137    -52.09137   ]\n",
      "   [-47.243866   -52.09137    -52.09137    ... -52.09137\n",
      "    -52.09137    -52.09137   ]\n",
      "   [-47.43277    -52.09137    -52.09137    ... -52.09137\n",
      "    -52.09137    -52.09137   ]]]\n",
      "\n",
      "\n",
      " [[[ -5.3567414   -4.6948485   -3.9461865  ...  -5.892365\n",
      "     -6.1473074   -6.3545265 ]\n",
      "   [-11.456879    -8.736723    -7.144261   ... -11.814154\n",
      "    -12.575272   -10.66362   ]\n",
      "   [-14.9593115  -14.73392    -13.053094   ... -17.88675\n",
      "    -17.352558   -14.114347  ]\n",
      "   ...\n",
      "   [-61.077103   -74.39771    -74.39771    ... -74.39771\n",
      "    -74.39771    -51.689945  ]\n",
      "   [-60.889336   -74.39771    -74.39771    ... -74.39771\n",
      "    -74.39771    -51.886383  ]\n",
      "   [-60.751976   -74.39771    -74.39771    ... -74.39771\n",
      "    -74.39771    -51.877304  ]]]\n",
      "\n",
      "\n",
      " [[[-16.628553   -18.784092   -18.360003   ... -16.116283\n",
      "    -11.811213   -13.327254  ]\n",
      "   [-20.503798   -23.490067   -23.50258    ... -16.269535\n",
      "    -15.425878   -18.37088   ]\n",
      "   [-21.367233   -29.491962   -32.188694   ... -16.544333\n",
      "    -21.130848   -18.72646   ]\n",
      "   ...\n",
      "   [-65.65658    -65.65658    -65.65658    ... -65.65658\n",
      "    -65.65658    -65.65658   ]\n",
      "   [-65.65658    -65.65658    -65.65658    ... -65.65658\n",
      "    -65.65658    -65.65658   ]\n",
      "   [-65.65658    -65.65658    -65.65658    ... -65.65658\n",
      "    -65.65658    -65.65658   ]]]] | Labels tensor([0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0])\n",
      "(16, 1, 64, 376)\n",
      "Epoch: 41 | Inputs [[[[-10.117361   -6.08718    -2.7424903 ... -15.110987  -12.486479\n",
      "    -21.697323 ]\n",
      "   [-11.052568  -11.077384   -7.6137166 ... -21.452892  -13.437676\n",
      "    -25.585583 ]\n",
      "   [-12.739421  -11.135952   -9.019031  ... -22.457836  -17.25165\n",
      "    -30.200579 ]\n",
      "   ...\n",
      "   [-72.28973   -72.28973   -72.28973   ... -72.28973   -72.28973\n",
      "    -72.28973  ]\n",
      "   [-72.28973   -72.28973   -70.8689    ... -72.28973   -72.28973\n",
      "    -72.28973  ]\n",
      "   [-72.28973   -72.28973   -71.06852   ... -72.28973   -72.28973\n",
      "    -72.28973  ]]]\n",
      "\n",
      "\n",
      " [[[-23.169628  -12.935221   -7.3644204 ... -26.480501  -20.677927\n",
      "    -14.253614 ]\n",
      "   [-14.990152  -17.65628   -13.047466  ... -32.859447  -26.805285\n",
      "    -18.102547 ]\n",
      "   [-14.097206  -25.891378  -23.989485  ... -47.040733  -39.019608\n",
      "    -18.885567 ]\n",
      "   ...\n",
      "   [-70.27538   -70.27538   -70.27538   ... -70.27538   -70.27538\n",
      "    -68.51017  ]\n",
      "   [-64.11279   -65.45322   -63.338608  ... -70.27538   -70.27538\n",
      "    -67.79023  ]\n",
      "   [-65.71265   -69.0806    -66.79166   ... -70.27538   -70.27538\n",
      "    -68.41283  ]]]\n",
      "\n",
      "\n",
      " [[[-15.91304   -13.496123   -9.278683  ... -26.269657  -16.220955\n",
      "    -21.773088 ]\n",
      "   [-22.55018   -18.468418  -15.718169  ... -23.612375  -22.562325\n",
      "    -21.547262 ]\n",
      "   [-31.343307  -27.332022  -33.10879   ... -21.203743  -21.637842\n",
      "    -17.320824 ]\n",
      "   ...\n",
      "   [-65.46236   -66.86919   -66.86919   ... -66.86919   -66.86919\n",
      "    -58.77081  ]\n",
      "   [-65.40607   -66.86919   -66.86919   ... -66.86919   -66.86919\n",
      "    -58.764923 ]\n",
      "   [-65.43794   -66.86919   -66.86919   ... -66.86919   -66.86919\n",
      "    -58.69213  ]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[-18.642986  -19.66601   -19.68635   ...  -7.394614   -9.703095\n",
      "    -18.273174 ]\n",
      "   [-25.273281  -26.205256  -24.122532  ... -11.28759   -14.333576\n",
      "    -18.149183 ]\n",
      "   [-38.588978  -45.40377   -29.938644  ... -18.467243  -22.588995\n",
      "    -21.792408 ]\n",
      "   ...\n",
      "   [-65.22633   -65.22633   -65.22633   ... -65.22633   -65.22633\n",
      "    -65.22633  ]\n",
      "   [-65.22633   -65.22633   -65.22633   ... -65.22633   -65.22633\n",
      "    -65.22633  ]\n",
      "   [-65.22633   -65.22633   -65.22633   ... -65.22633   -65.22633\n",
      "    -65.22633  ]]]\n",
      "\n",
      "\n",
      " [[[-24.681786   -7.857199   -6.2435837 ... -21.21629   -18.716629\n",
      "    -21.290173 ]\n",
      "   [-29.238585  -11.864161   -9.687673  ... -23.753891  -25.274529\n",
      "    -19.93682  ]\n",
      "   [-30.043745  -19.236015  -16.139399  ... -26.912146  -40.000557\n",
      "    -23.642155 ]\n",
      "   ...\n",
      "   [-57.97829   -57.97829   -57.97829   ... -57.97829   -57.97829\n",
      "    -57.97829  ]\n",
      "   [-57.97829   -57.97829   -57.97829   ... -57.97829   -57.97829\n",
      "    -57.97829  ]\n",
      "   [-74.15959   -74.15959   -74.15959   ... -74.15959   -74.15959\n",
      "    -74.15959  ]]]\n",
      "\n",
      "\n",
      " [[[  7.81018     8.543282   -6.629014  ... -12.371895   -1.3173072\n",
      "     -1.7864134]\n",
      "   [ 19.358591    7.753696    4.47613   ...  -9.53432    -0.5927833\n",
      "     -8.361173 ]\n",
      "   [ 22.587723   16.634209    6.201251  ... -13.711332   -3.5208771\n",
      "    -10.546972 ]\n",
      "   ...\n",
      "   [-49.32573   -49.32573   -49.32573   ... -49.32573   -49.32573\n",
      "    -49.32573  ]\n",
      "   [-43.733627  -46.86169   -48.824417  ... -49.32573   -49.32573\n",
      "    -49.32573  ]\n",
      "   [-44.973663  -49.027176  -49.32573   ... -49.32573   -49.32573\n",
      "    -49.32573  ]]]] | Labels tensor([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1])\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-a1858ef39a75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kbs_breath/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kbs_breath/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kbs_breath/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kbs_breath/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-43ef28e09c4a>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mclass_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0maud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBreath_sound_Util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mreaud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBreath_sound_Util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maud\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0mdur_aud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBreath_sound_Util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreaud\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0msgram\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBreath_sound_Util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspectro_gram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdur_aud\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_mels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhop_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-406be919b128>\u001b[0m in \u001b[0;36mresample\u001b[0;34m(aud, newsr)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mnum_channels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mresig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchaudio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewsr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnum_channels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m       \u001b[0mretwo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchaudio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewsr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kbs_breath/lib/python3.7/site-packages/torchaudio/transforms.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, orig_freq, new_freq, resampling_method, lowpass_filter_width, rolloff, beta, dtype)\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morig_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlowpass_filter_width\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolloff\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m                 self.resampling_method, beta, dtype=dtype)\n\u001b[0m\u001b[1;32m    704\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'kernel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/kbs_breath/lib/python3.7/site-packages/torchaudio/functional/functional.py\u001b[0m in \u001b[0;36m_get_sinc_resample_kernel\u001b[0;34m(orig_freq, new_freq, gcd, lowpass_filter_width, rolloff, resampling_method, beta, device, dtype)\u001b[0m\n\u001b[1;32m   1349\u001b[0m         \u001b[0;31m# at specific positions, not over a regular grid.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresampling_method\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"sinc_interpolation\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1351\u001b[0;31m             \u001b[0mwindow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlowpass_filter_width\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1352\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1353\u001b[0m             \u001b[0;31m# kaiser_window\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "for epoch in range(2):\n",
    "    for i, data in enumerate(train_dl, 0):\n",
    "        inputs, labels = data\n",
    "        inputs = np.array(inputs)\n",
    "        print(inputs.shape)\n",
    "        print(f'Epoch: {i} | Inputs {inputs} | Labels {labels}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20703fd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/52 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  2%|▏         | 1/52 [00:38<32:58, 38.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 0, Loss: 0.6560, Accuracy: 0.6435\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  4%|▍         | 2/52 [01:16<31:57, 38.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 1, Loss: 0.3968, Accuracy: 0.8463\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  6%|▌         | 3/52 [01:54<31:13, 38.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 2, Loss: 0.2771, Accuracy: 0.9027\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 3, Loss: 0.2295, Accuracy: 0.9177\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 10%|▉         | 5/52 [03:11<29:54, 38.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 4, Loss: 0.1833, Accuracy: 0.9293\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 12%|█▏        | 6/52 [03:49<29:15, 38.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 5, Loss: 0.1651, Accuracy: 0.9435\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 6, Loss: 0.1583, Accuracy: 0.9367\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 15%|█▌        | 8/52 [05:05<27:58, 38.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 7, Loss: 0.1173, Accuracy: 0.9558\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 17%|█▋        | 9/52 [05:43<27:19, 38.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 8, Loss: 0.1183, Accuracy: 0.9599\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 9, Loss: 0.1192, Accuracy: 0.9524\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 21%|██        | 11/52 [07:00<26:04, 38.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 10, Loss: 0.2411, Accuracy: 0.9224\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 23%|██▎       | 12/52 [07:38<25:25, 38.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 11, Loss: 0.1387, Accuracy: 0.9565\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 25%|██▌       | 13/52 [08:16<24:46, 38.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 12, Loss: 0.1647, Accuracy: 0.9456\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 27%|██▋       | 14/52 [08:54<24:07, 38.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 13, Loss: 0.1561, Accuracy: 0.9537\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 29%|██▉       | 15/52 [09:32<23:29, 38.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 14, Loss: 0.1459, Accuracy: 0.9517\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 31%|███       | 16/52 [10:10<22:51, 38.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 15, Loss: 0.1164, Accuracy: 0.9673\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 33%|███▎      | 17/52 [10:48<22:13, 38.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 16, Loss: 0.1584, Accuracy: 0.9605\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 35%|███▍      | 18/52 [11:26<21:36, 38.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 17, Loss: 0.1006, Accuracy: 0.9653\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 37%|███▋      | 19/52 [12:05<20:58, 38.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 18, Loss: 0.1449, Accuracy: 0.9646\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 38%|███▊      | 20/52 [12:43<20:21, 38.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 19, Loss: 0.0927, Accuracy: 0.9714\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|████      | 21/52 [13:21<19:42, 38.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 20, Loss: 0.1347, Accuracy: 0.9796\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 42%|████▏     | 22/52 [14:00<19:12, 38.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 21, Loss: 0.1082, Accuracy: 0.9667\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 44%|████▍     | 23/52 [14:39<18:40, 38.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 22, Loss: 0.1131, Accuracy: 0.9741\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 46%|████▌     | 24/52 [15:18<18:05, 38.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 23, Loss: 0.0734, Accuracy: 0.9782\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 48%|████▊     | 25/52 [15:57<17:29, 38.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 24, Loss: 0.0536, Accuracy: 0.9844\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 26/52 [16:36<16:46, 38.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 25, Loss: 0.0482, Accuracy: 0.9864\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 52%|█████▏    | 27/52 [17:14<16:04, 38.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 26, Loss: 0.0424, Accuracy: 0.9891\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 54%|█████▍    | 28/52 [17:52<15:20, 38.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 27, Loss: 0.0439, Accuracy: 0.9878\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 56%|█████▌    | 29/52 [18:30<14:39, 38.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 28, Loss: 0.0207, Accuracy: 0.9925\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 58%|█████▊    | 30/52 [19:08<13:59, 38.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 29, Loss: 0.0561, Accuracy: 0.9905\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|█████▉    | 31/52 [19:45<13:19, 38.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 30, Loss: 0.0254, Accuracy: 0.9912\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 62%|██████▏   | 32/52 [20:23<12:40, 38.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 31, Loss: 0.0130, Accuracy: 0.9952\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 63%|██████▎   | 33/52 [21:01<12:01, 38.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 32, Loss: 0.0163, Accuracy: 0.9980\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 65%|██████▌   | 34/52 [21:39<11:23, 37.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 33, Loss: 0.0058, Accuracy: 0.9986\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 67%|██████▋   | 35/52 [22:17<10:45, 37.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 34, Loss: 0.0201, Accuracy: 0.9946\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 69%|██████▉   | 36/52 [22:55<10:07, 37.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 35, Loss: 0.0274, Accuracy: 0.9952\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 71%|███████   | 37/52 [23:33<09:29, 37.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 36, Loss: 0.0073, Accuracy: 0.9966\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 73%|███████▎  | 38/52 [24:11<08:51, 37.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 37, Loss: 0.0134, Accuracy: 0.9946\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 75%|███████▌  | 39/52 [24:49<08:12, 37.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 38, Loss: 0.0050, Accuracy: 0.9980\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 39, Loss: 0.0145, Accuracy: 0.9952\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 79%|███████▉  | 41/52 [26:05<06:57, 38.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 40, Loss: 0.0087, Accuracy: 0.9966\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 81%|████████  | 42/52 [26:43<06:19, 37.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 41, Loss: 0.0084, Accuracy: 0.9966\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 83%|████████▎ | 43/52 [27:21<05:43, 38.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 42, Loss: 0.0096, Accuracy: 0.9966\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 85%|████████▍ | 44/52 [28:00<05:07, 38.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 43, Loss: 0.0085, Accuracy: 0.9980\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 87%|████████▋ | 45/52 [28:39<04:30, 38.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 44, Loss: 0.0072, Accuracy: 0.9980\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 88%|████████▊ | 46/52 [29:19<03:53, 38.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 45, Loss: 0.0131, Accuracy: 0.9959\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|█████████ | 47/52 [29:58<03:14, 38.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 46, Loss: 0.0032, Accuracy: 0.9986\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 92%|█████████▏| 48/52 [30:37<02:35, 38.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 47, Loss: 0.0106, Accuracy: 0.9986\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 94%|█████████▍| 49/52 [31:16<01:56, 38.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 48, Loss: 0.0082, Accuracy: 0.9973\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 96%|█████████▌| 50/52 [31:55<01:17, 38.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 49, Loss: 0.0030, Accuracy: 1.0000\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|█████████▊| 51/52 [32:35<00:39, 39.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 50, Loss: 0.0019, Accuracy: 0.9993\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
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      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 52/52 [33:14<00:00, 38.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([14, 376, 64, 1])\n",
      "torch.Size([14, 64, 376, 1])\n",
      "torch.Size([14, 1, 376, 64])\n",
      "Epoch: 51, Loss: 0.0048, Accuracy: 0.9986\n",
      "Finished Training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "from tqdm import tqdm\n",
    "\n",
    "def training(model, train_dl, num_epochs):\n",
    "  criterion = nn.CrossEntropyLoss()\n",
    "  optimizer = torch.optim.Adam(model.parameters(),lr=0.001)\n",
    "  scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.001,\n",
    "                                                steps_per_epoch=int(len(train_dl)),\n",
    "                                                epochs=num_epochs,\n",
    "                                                anneal_strategy='linear')\n",
    "\n",
    "  for epoch in tqdm(range(num_epochs)):\n",
    "    running_loss = 0.0\n",
    "    correct_prediction = 0\n",
    "    total_prediction = 0\n",
    "\n",
    "    for i, data in enumerate(train_dl):\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "        inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "        inputs = (inputs - inputs_m) / inputs_s\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "        running_loss += loss.item()\n",
    "        _, prediction = torch.max(outputs,1)\n",
    "        correct_prediction += (prediction == labels).sum().item()\n",
    "        total_prediction += prediction.shape[0]\n",
    "        \n",
    "\n",
    "    num_batches = len(train_dl)\n",
    "    avg_loss = running_loss / num_batches\n",
    "    acc = correct_prediction/total_prediction\n",
    "    print(f'Epoch: {epoch}, Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}')\n",
    "\n",
    "  print('Finished Training')\n",
    "    \n",
    "num_epochs=52\n",
    "training(Model1, train_dl, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88c18ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0003-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0004-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0007-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0009-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0009-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0012-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0013-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0015-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0020-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0020-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0020-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0021-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0023-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0024-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0025-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0029-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0030-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0030-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0031-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0032-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0034-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0036-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0036-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0036-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0037-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0037-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0037-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0038-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0041-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0042-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0043-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0044-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0044-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0063-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0063-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0070-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0107-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0526-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0533-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0534-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0559-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0581-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0588-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0588-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0593-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0594-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0597-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0600-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0602-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0611-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0612-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0613-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0614-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      filename  category  class\n",
       "0   0003-1.wav   healthy      0\n",
       "1   0004-1.wav   healthy      0\n",
       "2   0007-1.wav   healthy      0\n",
       "3   0009-1.wav   healthy      0\n",
       "4   0009-4.wav   healthy      0\n",
       "5   0012-2.wav   healthy      0\n",
       "6   0013-2.wav   healthy      0\n",
       "7   0015-1.wav   healthy      0\n",
       "8   0020-1.wav  wheezing      1\n",
       "9   0020-3.wav  wheezing      1\n",
       "10  0020-4.wav  wheezing      1\n",
       "11  0021-2.wav   healthy      0\n",
       "12  0023-1.wav   healthy      0\n",
       "13  0024-3.wav   healthy      0\n",
       "14  0025-3.wav  wheezing      1\n",
       "15  0029-2.wav   healthy      0\n",
       "16  0030-3.wav   healthy      0\n",
       "17  0030-4.wav   healthy      0\n",
       "18  0031-1.wav   healthy      0\n",
       "19  0032-2.wav   healthy      0\n",
       "20  0034-1.wav   healthy      0\n",
       "21  0036-2.wav   healthy      0\n",
       "22  0036-3.wav   healthy      0\n",
       "23  0036-4.wav   healthy      0\n",
       "24  0037-1.wav   healthy      0\n",
       "25  0037-3.wav   healthy      0\n",
       "26  0037-4.wav   healthy      0\n",
       "27  0038-3.wav   healthy      0\n",
       "28  0041-3.wav   healthy      0\n",
       "29  0042-4.wav   healthy      0\n",
       "30  0043-3.wav   healthy      0\n",
       "31  0044-3.wav   healthy      0\n",
       "32  0044-4.wav   healthy      0\n",
       "33  0063-2.wav   healthy      0\n",
       "34  0063-3.wav   healthy      0\n",
       "35  0070-1.wav   healthy      0\n",
       "36  0107-1.wav  wheezing      1\n",
       "37  0526-3.wav  wheezing      1\n",
       "38  0533-2.wav  wheezing      1\n",
       "39  0534-4.wav  wheezing      1\n",
       "40  0559-4.wav  wheezing      1\n",
       "41  0581-4.wav  wheezing      1\n",
       "42  0588-1.wav   healthy      0\n",
       "43  0588-4.wav   healthy      0\n",
       "44  0593-3.wav  wheezing      1\n",
       "45  0594-2.wav  wheezing      1\n",
       "46  0597-2.wav  wheezing      1\n",
       "47  0600-1.wav  wheezing      1\n",
       "48  0602-2.wav   healthy      0\n",
       "49  0611-4.wav  wheezing      1\n",
       "50  0612-1.wav  wheezing      1\n",
       "51  0613-2.wav  wheezing      1\n",
       "52  0614-1.wav   healthy      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file1 = 'breathdata/test.csv'\n",
    "df1 = pd.read_csv(Catholic_file1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "493fa2f8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>/0004-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0007-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0009-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0009-4.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  relative_path  class\n",
       "0   /0003-1.wav      0\n",
       "1   /0004-1.wav      0\n",
       "2   /0007-1.wav      0\n",
       "3   /0009-1.wav      0\n",
       "4   /0009-4.wav      0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['relative_path'] = '/' + df1['filename'].astype(str)\n",
    "df1 = df1[['relative_path', 'class']]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ecabfe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'breathdata/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8fcca0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Breath_sound_Util1():\n",
    "  \n",
    "  def open(audio_file):\n",
    "    sig, sr = torchaudio.load(audio_file)\n",
    "    \n",
    "    return (sig, sr)\n",
    "\n",
    "  def resample(aud, newsr):\n",
    "    sig, sr = aud\n",
    "    \n",
    "    if (sr == newsr):\n",
    "     return aud\n",
    "\n",
    "    num_channels = sig.shape[0]\n",
    "    resig = torchaudio.transforms.Resample(sr, newsr)(sig[:1,:])\n",
    "    if (num_channels > 1):\n",
    "      retwo = torchaudio.transforms.Resample(sr, newsr)(sig[1:,:])\n",
    "      resig = torch.cat([resig, retwo])\n",
    "\n",
    "    return ((resig, newsr))\n",
    "  \n",
    "\n",
    "  def pad(aud, max_ms):\n",
    "    sig, sr = aud\n",
    "    num_rows, sig_len = sig.shape\n",
    "    max_len = sr//1000 * max_ms\n",
    "   \n",
    "    if (sig_len > max_len):\n",
    "      sig = sig[:,:max_len]\n",
    " \n",
    "    elif (sig_len < max_len):\n",
    "\n",
    "      repeated = []\n",
    "      repeated.append(sig)\n",
    "      required_len = max_len - sig_len\n",
    "\n",
    "      while required_len > sig_len : \n",
    "        repeated.append(sig)\n",
    "        require_len -= sig_len\n",
    "      repeated.append(sig[:, :required_len])\n",
    " \n",
    "      sig = torch.cat(repeated, 1)\n",
    "\n",
    "    return (sig, sr)\n",
    "\n",
    "  def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):\n",
    "    sig,sr = aud\n",
    "    top_db = 80\n",
    "    \n",
    "    spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)\n",
    "    spec = transforms.AmplitudeToDB(top_db=top_db)(spec)\n",
    "    return (spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c890d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "class breathDS1(Dataset):\n",
    "    \n",
    "  def __init__(self, df1, data_path):\n",
    "    self.df1 = df1\n",
    "    self.data_path = str(data_path)\n",
    "    self.duration = 4000\n",
    "    self.sr = 48000\n",
    "            \n",
    "  def __len__(self):\n",
    "    return len(self.df1)    \n",
    "    \n",
    "  def __getitem__(self, idx):\n",
    "    audio_file = self.data_path + self.df1.loc[idx, 'relative_path']\n",
    "    class_id = self.df1.loc[idx, 'class']\n",
    "    aud = Breath_sound_Util1.open(audio_file)\n",
    "    reaud = Breath_sound_Util1.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util1.pad(reaud, self.duration)\n",
    "    sgram = Breath_sound_Util1.spectro_gram(dur_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
    "\n",
    "    \n",
    "    return sgram, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "43b4f308",
   "metadata": {},
   "outputs": [],
   "source": [
    "brds1 = breathDS1(df1, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "40bce7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dl = torch.utils.data.DataLoader(brds1, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "17709449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([16, 376, 64, 1])\n",
      "torch.Size([16, 64, 376, 1])\n",
      "torch.Size([16, 1, 376, 64])\n",
      "torch.Size([5, 376, 64, 1])\n",
      "torch.Size([5, 64, 376, 1])\n",
      "torch.Size([5, 1, 376, 64])\n",
      "Accuracy: 0.8491, Total items: 53\n",
      "precision: 0.8000, F1: 0.7500\n",
      "recall: 0.7059\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     healthy       0.87      0.92      0.89        36\n",
      "    wheezing       0.80      0.71      0.75        17\n",
      "\n",
      "    accuracy                           0.85        53\n",
      "   macro avg       0.83      0.81      0.82        53\n",
      "weighted avg       0.85      0.85      0.85        53\n",
      "\n",
      "AUC:0.8112745098039216\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "def inference (Model1, val_dl):\n",
    "  correct_prediction = 0\n",
    "  total_prediction = 0\n",
    "  TP = 0 \n",
    "  TN = 0\n",
    "  FN = 0\n",
    "  FP = 0\n",
    "  a = []\n",
    "  b = []\n",
    "  with torch.no_grad():\n",
    "    for i, data in enumerate(val_dl):\n",
    "      \n",
    "      inputs, labels = data[0].to(device), data[1].to(device)\n",
    "      cpu_labels = data[1]\n",
    "      inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "      inputs = (inputs - inputs_m) / inputs_s\n",
    "      outputs = Model1(inputs)\n",
    " \n",
    "      _, prediction = torch.max(outputs,1)\n",
    "      \n",
    "      cpu_prediction = prediction.to('cpu')\n",
    "      correct_prediction += (prediction == labels).sum().item()\n",
    "      total_prediction += prediction.shape[0]\n",
    "          \n",
    "\n",
    "      TP += ((prediction == 1) & (labels == 1)).sum()\n",
    "# TN predict and label are both 0\n",
    "      TN += ((prediction == 0) & (labels == 0)).sum()\n",
    "# FN    predict 0 label 1\n",
    "      FN += ((prediction == 0) & (labels == 1)).sum()\n",
    "# FP    predict 1 label 0\n",
    "      FP += ((prediction == 1) & (labels == 0)).sum()\n",
    "\n",
    "      c = cpu_labels.tolist()\n",
    "      a.extend(c)\n",
    "\n",
    "      d = cpu_prediction.tolist()\n",
    "      b.extend(d)\n",
    "    \n",
    "    # TP : 휘징인데 휘징이라고 판단(잘함)\n",
    "    # FP : 헬시인데 휘징이라고 판단(못함)\n",
    "    # TN : 헬시인데 헬시라고 판단(잘함)\n",
    "    # FN : 휘징인데 헬시라고 판단(못함)\n",
    "    \n",
    "    # POSITIVE : 1(휘징)이라고 판단\n",
    "    # NEGATIVE : 0(헬시)라고 판단\n",
    "    # TRUE : 정답값 1(휘징)\n",
    "    # FALSE : 정답값 0(헬시)\n",
    "    \n",
    "    # PRECISION : 휘징이라고 판단한 것 중 진짜 휘징\n",
    "    # RECALL : 휘징인데 휘징이라고 잘 판단\n",
    "    \n",
    "\n",
    "  precision = TP / (TP + FP)\n",
    "  recall = TP / (TP + FN)\n",
    "  F1 = 2 * recall * precision / (recall + precision)\n",
    "\n",
    "  acc = correct_prediction/total_prediction\n",
    "  print(f'Accuracy: {acc:.4f}, Total items: {total_prediction}')\n",
    "  print(f'precision: {precision:.4f}, F1: {F1:.4f}')\n",
    "  print(f'recall: {recall:.4f}')\n",
    "  target_names = ['healthy', 'wheezing']\n",
    "  print(classification_report(a, b, target_names = target_names))\n",
    "  print(\"AUC:{}\".format(roc_auc_score(a, b)))\n",
    "\n",
    "inference(Model1, val_dl)"
   ]
  }
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