03-a_Transfer-learning_LaneNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import math\n",
    "import os\n",
    "import os.path as ops\n",
    "import time\n",
    "\n",
    "import cv2\n",
    "import glog as log\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "from easydict import EasyDict as edict\n",
    "from collections import OrderedDict\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore warning for tf 2.0\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13.0-rc2\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Data Set and Weights Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = 'data/training_data_tusimple_gamma_tusimple_0d4/'\n",
    "weights_path = 'model/tusimple_lanenet/tusimple_lanenet_vgg_2018-10-19-13-33-56.ckpt-200000'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "__C = edict()\n",
    "\n",
    "cfg = __C\n",
    "\n",
    "__C.TRAIN = edict()\n",
    "__C.TRAIN.EPOCHS = 5000\n",
    "__C.TRAIN.NOOFBATCHES = 10\n",
    "__C.TRAIN.MOMENTUM = 0.9\n",
    "__C.TRAIN.LEARNING_RATE = 0.0005\n",
    "__C.TRAIN.GPU_MEMORY_FRACTION = 0.85\n",
    "__C.TRAIN.TF_ALLOW_GROWTH = True\n",
    "__C.TRAIN.BATCH_SIZE = 8\n",
    "__C.TRAIN.IMG_HEIGHT = 256\n",
    "__C.TRAIN.IMG_WIDTH = 512"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataSet(object):\n",
    "    def __init__(self, dataset_info_file):\n",
    "        self._gt_img_list, self._gt_label_binary_list = self._init_dataset(dataset_info_file)\n",
    "        self._random_dataset()\n",
    "        self._next_batch_loop_count = 0\n",
    "\n",
    "    def _init_dataset(self, dataset_info_file):\n",
    "        gt_img_list = []\n",
    "        gt_label_binary_list = []\n",
    "\n",
    "        assert ops.exists(dataset_info_file), '{:s}　does not exist'.format(dataset_info_file)\n",
    "\n",
    "        with open(dataset_info_file, 'r') as file:\n",
    "            for _info in file:\n",
    "                info_tmp = _info.strip(' ').split()\n",
    "\n",
    "                gt_img_list.append(info_tmp[0])\n",
    "                gt_label_binary_list.append(info_tmp[1])\n",
    "\n",
    "        return gt_img_list, gt_label_binary_list\n",
    "\n",
    "    def _random_dataset(self):\n",
    "        assert len(self._gt_img_list) == len(self._gt_label_binary_list)\n",
    "\n",
    "        random_idx = np.random.permutation(len(self._gt_img_list))\n",
    "        new_gt_img_list = []\n",
    "        new_gt_label_binary_list = []\n",
    "\n",
    "        for index in random_idx:\n",
    "            new_gt_img_list.append(self._gt_img_list[index])\n",
    "            new_gt_label_binary_list.append(self._gt_label_binary_list[index])\n",
    "\n",
    "        self._gt_img_list = new_gt_img_list\n",
    "        self._gt_label_binary_list = new_gt_label_binary_list\n",
    "\n",
    "    def next_batch(self, batch_size):\n",
    "        assert len(self._gt_label_binary_list) == len(self._gt_img_list)\n",
    "\n",
    "        idx_start = batch_size * self._next_batch_loop_count\n",
    "        idx_end = batch_size * self._next_batch_loop_count + batch_size\n",
    "\n",
    "        if idx_start == 0 and idx_end > len(self._gt_label_binary_list):\n",
    "            raise ValueError('Batch size cannot be larger than the total number of samples', \n",
    "                             idx_end, len(self._gt_label_binary_list))\n",
    "\n",
    "        if idx_end > len(self._gt_label_binary_list):\n",
    "            self._random_dataset()\n",
    "            self._next_batch_loop_count = 0\n",
    "            return self.next_batch(batch_size)\n",
    "        else:\n",
    "            gt_img_list = self._gt_img_list[idx_start:idx_end]\n",
    "            gt_label_binary_list = self._gt_label_binary_list[idx_start:idx_end]\n",
    "\n",
    "            gt_imgs = []\n",
    "            gt_labels_binary = []\n",
    "\n",
    "            for gt_img_path in gt_img_list:\n",
    "                gt_imgs.append(cv2.imread(gt_img_path, cv2.IMREAD_COLOR))\n",
    "\n",
    "            for gt_label_path in gt_label_binary_list:\n",
    "                label_img = cv2.imread(gt_label_path, cv2.IMREAD_COLOR)\n",
    "                label_binary = np.zeros([label_img.shape[0], label_img.shape[1]], dtype=np.uint8)\n",
    "                idx = np.where((label_img[:, :, :] != [0, 0, 0]).all(axis=2))\n",
    "                label_binary[idx] = 1\n",
    "                gt_labels_binary.append(label_binary)\n",
    "\n",
    "            self._next_batch_loop_count += 1\n",
    "\n",
    "        return gt_imgs, gt_labels_binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train dataset\n",
    "train_dataset_file = ops.join(dataset_dir, 'train.txt')\n",
    "assert ops.exists(train_dataset_file)\n",
    "train_dataset = DataSet(train_dataset_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Base Function\n",
    "## cnn_basenet.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNNBaseModel(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    @staticmethod\n",
    "    def conv2d(inputdata, out_channel, kernel_size, padding='SAME',\n",
    "               stride=1, w_init=None, b_init=None,\n",
    "               split=1, use_bias=True, data_format='NHWC', name=None):\n",
    "        with tf.variable_scope(name):\n",
    "            in_shape = inputdata.get_shape().as_list()\n",
    "            channel_axis = 3 if data_format == 'NHWC' else 1\n",
    "            in_channel = in_shape[channel_axis]\n",
    "            assert in_channel is not None, \"[Conv2D] Input cannot have unknown channel!\"\n",
    "            assert in_channel % split == 0\n",
    "            assert out_channel % split == 0\n",
    "\n",
    "            padding = padding.upper()\n",
    "\n",
    "            if isinstance(kernel_size, list):\n",
    "                filter_shape = [kernel_size[0], kernel_size[1]] + [in_channel / split, out_channel]\n",
    "            else:\n",
    "                filter_shape = [kernel_size, kernel_size] + [in_channel / split, out_channel]\n",
    "\n",
    "            if isinstance(stride, list):\n",
    "                strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \\\n",
    "                    else [1, 1, stride[0], stride[1]]\n",
    "            else:\n",
    "                strides = [1, stride, stride, 1] if data_format == 'NHWC' \\\n",
    "                    else [1, 1, stride, stride]\n",
    "\n",
    "            if w_init is None:\n",
    "                w_init = tf.contrib.layers.variance_scaling_initializer()\n",
    "            if b_init is None:\n",
    "                b_init = tf.constant_initializer()\n",
    "\n",
    "            w = tf.get_variable('W', filter_shape, initializer=w_init)\n",
    "            b = None\n",
    "\n",
    "            if use_bias:\n",
    "                b = tf.get_variable('b', [out_channel], initializer=b_init)\n",
    "\n",
    "            if split == 1:\n",
    "                conv = tf.nn.conv2d(inputdata, w, strides, padding, data_format=data_format)\n",
    "            else:\n",
    "                inputs = tf.split(inputdata, split, channel_axis)\n",
    "                kernels = tf.split(w, split, 3)\n",
    "                outputs = [tf.nn.conv2d(i, k, strides, padding, data_format=data_format)\n",
    "                           for i, k in zip(inputs, kernels)]\n",
    "                conv = tf.concat(outputs, channel_axis)\n",
    "\n",
    "            ret = tf.identity(tf.nn.bias_add(conv, b, data_format=data_format) if use_bias else conv, name=name)\n",
    "\n",
    "        return ret\n",
    "\n",
    "    @staticmethod\n",
    "    def relu(inputdata, name=None):\n",
    "        return tf.nn.relu(features=inputdata, name=name)\n",
    "\n",
    "    @staticmethod\n",
    "    def maxpooling(inputdata, kernel_size, stride=None, \\\n",
    "                   padding='VALID', data_format='NHWC', name=None):\n",
    "        padding = padding.upper()\n",
    "\n",
    "        if stride is None:\n",
    "            stride = kernel_size\n",
    "\n",
    "        if isinstance(kernel_size, list):\n",
    "            kernel = [1, kernel_size[0], kernel_size[1], 1] if data_format == 'NHWC' \\\n",
    "                else [1, 1, kernel_size[0], kernel_size[1]]\n",
    "        else:\n",
    "            kernel = [1, kernel_size, kernel_size, 1] if data_format == 'NHWC' \\\n",
    "                else [1, 1, kernel_size, kernel_size]\n",
    "\n",
    "        if isinstance(stride, list):\n",
    "            strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \\\n",
    "                else [1, 1, stride[0], stride[1]]\n",
    "        else:\n",
    "            strides = [1, stride, stride, 1] if data_format == 'NHWC' \\\n",
    "                else [1, 1, stride, stride]\n",
    "\n",
    "        return tf.nn.max_pool(value=inputdata, ksize=kernel, strides=strides, \\\n",
    "                              padding=padding, data_format=data_format, name=name)\n",
    "\n",
    "    @staticmethod\n",
    "    def layerbn(inputdata, is_training, name):\n",
    "        return tf.layers.batch_normalization(inputs=inputdata, training=is_training, name=name)\n",
    "\n",
    "    @staticmethod\n",
    "    def deconv2d(inputdata, out_channel, kernel_size, padding='SAME',\n",
    "                 stride=1, w_init=None, b_init=None,\n",
    "                 use_bias=True, activation=None, data_format='channels_last',\n",
    "                 trainable=True, name=None):\n",
    "        with tf.variable_scope(name):\n",
    "            in_shape = inputdata.get_shape().as_list()\n",
    "            channel_axis = 3 if data_format == 'channels_last' else 1\n",
    "            in_channel = in_shape[channel_axis]\n",
    "            assert in_channel is not None, \"[Deconv2D] Input cannot have unknown channel!\"\n",
    "\n",
    "            padding = padding.upper()\n",
    "\n",
    "            if w_init is None:\n",
    "                w_init = tf.contrib.layers.variance_scaling_initializer()\n",
    "            if b_init is None:\n",
    "                b_init = tf.constant_initializer()\n",
    "\n",
    "            ret = tf.layers.conv2d_transpose(inputs=inputdata, filters=out_channel,\n",
    "                                             kernel_size=kernel_size,\n",
    "                                             strides=stride, padding=padding,\n",
    "                                             data_format=data_format,\n",
    "                                             activation=activation, use_bias=use_bias,\n",
    "                                             kernel_initializer=w_init,\n",
    "                                             bias_initializer=b_init, trainable=trainable,\n",
    "                                             name=name)\n",
    "        return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining model using conv2d, Relu and Maxpooling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fcn_decoder.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FCNDecoder(CNNBaseModel):\n",
    "    def __init__(self, phase):\n",
    "        super(FCNDecoder, self).__init__()\n",
    "\n",
    "    def decode(self, input_tensor_dict, decode_layer_list, name):\n",
    "        ret = dict()\n",
    "\n",
    "        with tf.variable_scope(name):\n",
    "            # score stage 1\n",
    "            input_tensor = input_tensor_dict[decode_layer_list[0]]['data']\n",
    "\n",
    "            score = self.conv2d(inputdata=input_tensor, out_channel=64,\n",
    "                                kernel_size=1, use_bias=False, name='score_origin')\n",
    "            decode_layer_list = decode_layer_list[1:]\n",
    "            for i in range(len(decode_layer_list)):\n",
    "                deconv = self.deconv2d(inputdata=score, out_channel=64, kernel_size=4,\n",
    "                                       stride=2, use_bias=False, name='deconv_{:d}'.format(i + 1))\n",
    "                input_tensor = input_tensor_dict[decode_layer_list[i]]['data']\n",
    "                score = self.conv2d(inputdata=input_tensor, out_channel=64,\n",
    "                                    kernel_size=1, use_bias=False, name='score_{:d}'.format(i + 1))\n",
    "                fused = tf.add(deconv, score, name='fuse_{:d}'.format(i + 1))\n",
    "                score = fused\n",
    "\n",
    "            deconv_final = self.deconv2d(inputdata=score, out_channel=64, kernel_size=16,\n",
    "                                         stride=8, use_bias=False, name='deconv_final')\n",
    "\n",
    "            score_final = self.conv2d(inputdata=deconv_final, out_channel=2,\n",
    "                                      kernel_size=1, use_bias=False, name='score_final')\n",
    "\n",
    "            ret['logits'] = score_final\n",
    "\n",
    "        return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## vgg_encoder.py\n",
    "https://github.com/machrisaa/tensorflow-vgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VGG16Encoder(CNNBaseModel):\n",
    "    def __init__(self, phase):\n",
    "        super(VGG16Encoder, self).__init__()\n",
    "        self._train_phase = tf.constant('train', dtype=tf.string)\n",
    "        self._phase = phase\n",
    "        self._is_training = self._init_phase()\n",
    "\n",
    "    def _init_phase(self):\n",
    "        return tf.equal(self._phase, self._train_phase)\n",
    "\n",
    "    def _conv_stage(self, input_tensor, k_size, out_dims, name,\n",
    "                    stride=1, pad='SAME'):\n",
    "        with tf.variable_scope(name):\n",
    "            conv = self.conv2d(inputdata=input_tensor, out_channel=out_dims,\n",
    "                               kernel_size=k_size, stride=stride,\n",
    "                               use_bias=False, padding=pad, name='conv')\n",
    "\n",
    "            bn = self.layerbn(inputdata=conv, is_training=self._is_training, name='bn')\n",
    "\n",
    "            relu = self.relu(inputdata=bn, name='relu')\n",
    "\n",
    "            return relu\n",
    "\n",
    "    def encode(self, input_tensor, name):\n",
    "        ret = OrderedDict()\n",
    "\n",
    "        with tf.variable_scope(name):\n",
    "            # conv stage 1\n",
    "            conv_1_1 = self._conv_stage(input_tensor=input_tensor, k_size=3, out_dims=64, name='conv1_1')\n",
    "            conv_1_2 = self._conv_stage(input_tensor=conv_1_1, k_size=3, out_dims=64, name='conv1_2')\n",
    "            pool1 = self.maxpooling(inputdata=conv_1_2, kernel_size=2, stride=2, name='pool1')\n",
    "\n",
    "            # conv stage 2\n",
    "            conv_2_1 = self._conv_stage(input_tensor=pool1, k_size=3, out_dims=128, name='conv2_1')\n",
    "            conv_2_2 = self._conv_stage(input_tensor=conv_2_1, k_size=3, out_dims=128, name='conv2_2')\n",
    "            pool2 = self.maxpooling(inputdata=conv_2_2, kernel_size=2, stride=2, name='pool2')\n",
    "\n",
    "            # conv stage 3\n",
    "            conv_3_1 = self._conv_stage(input_tensor=pool2, k_size=3, out_dims=256, name='conv3_1')\n",
    "            conv_3_2 = self._conv_stage(input_tensor=conv_3_1, k_size=3, out_dims=256, name='conv3_2')\n",
    "            conv_3_3 = self._conv_stage(input_tensor=conv_3_2, k_size=3, out_dims=256, name='conv3_3')\n",
    "            pool3 = self.maxpooling(inputdata=conv_3_3, kernel_size=2, stride=2, name='pool3')\n",
    "            ret['pool3'] = dict()\n",
    "            ret['pool3']['data'] = pool3\n",
    "            ret['pool3']['shape'] = pool3.get_shape().as_list()\n",
    "\n",
    "            # conv stage 4\n",
    "            conv_4_1 = self._conv_stage(input_tensor=pool3, k_size=3, out_dims=512, name='conv4_1')\n",
    "            conv_4_2 = self._conv_stage(input_tensor=conv_4_1, k_size=3, out_dims=512, name='conv4_2')\n",
    "            conv_4_3 = self._conv_stage(input_tensor=conv_4_2, k_size=3, out_dims=512, name='conv4_3')\n",
    "            pool4 = self.maxpooling(inputdata=conv_4_3, kernel_size=2, stride=2, name='pool4')\n",
    "            ret['pool4'] = dict()\n",
    "            ret['pool4']['data'] = pool4\n",
    "            ret['pool4']['shape'] = pool4.get_shape().as_list()\n",
    "\n",
    "            # conv stage 5\n",
    "            conv_5_1 = self._conv_stage(input_tensor=pool4, k_size=3, out_dims=512, name='conv5_1')\n",
    "            conv_5_2 = self._conv_stage(input_tensor=conv_5_1, k_size=3, out_dims=512, name='conv5_2')\n",
    "            conv_5_3 = self._conv_stage(input_tensor=conv_5_2, k_size=3, out_dims=512, name='conv5_3')\n",
    "            pool5 = self.maxpooling(inputdata=conv_5_3, kernel_size=2, stride=2, name='pool5')\n",
    "            ret['pool5'] = dict()\n",
    "            ret['pool5']['data'] = pool5\n",
    "            ret['pool5']['shape'] = pool5.get_shape().as_list()\n",
    "\n",
    "        return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lanenet_merge_model.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LaneNet():\n",
    "    def __init__(self, phase):\n",
    "        super(LaneNet, self).__init__()\n",
    "        self._encoder = VGG16Encoder(phase=phase)\n",
    "        self._decoder = FCNDecoder(phase=phase)\n",
    "        return\n",
    "\n",
    "    def _build_model(self, input_tensor, name):\n",
    "        with tf.variable_scope(name):\n",
    "            # first encode\n",
    "            encode_ret = self._encoder.encode(input_tensor=input_tensor,\n",
    "                                              name='encode')\n",
    "            # second decode\n",
    "            decode_ret = self._decoder.decode(input_tensor_dict=encode_ret,\n",
    "                                              name='decode',\n",
    "                                              decode_layer_list=['pool5',\n",
    "                                                                 'pool4',\n",
    "                                                                 'pool3'])\n",
    "            return decode_ret\n",
    "\n",
    "    def compute_loss(self, input_tensor, binary_label, name):\n",
    "        with tf.variable_scope(name):\n",
    "            # Forward propagation to get logits\n",
    "            inference_ret = self._build_model(input_tensor=input_tensor, name='inference')\n",
    "\n",
    "            # Calculate the binary partition loss function\n",
    "            decode_logits = inference_ret['logits']\n",
    "            binary_label_plain = tf.reshape(binary_label,\n",
    "                                            shape=[binary_label.get_shape().as_list()[0] *\n",
    "                                                   binary_label.get_shape().as_list()[1] *\n",
    "                                                   binary_label.get_shape().as_list()[2]])\n",
    "            # Add class weights\n",
    "            unique_labels, unique_id, counts = tf.unique_with_counts(binary_label_plain)\n",
    "            counts = tf.cast(counts, tf.float32)\n",
    "            inverse_weights = tf.divide(1.0,\n",
    "                                        tf.log(tf.add(tf.divide(tf.constant(1.0), counts),\n",
    "                                                      tf.constant(1.02))))\n",
    "            inverse_weights = tf.gather(inverse_weights, binary_label)\n",
    "            binary_segmenatation_loss = tf.losses.sparse_softmax_cross_entropy(labels=binary_label, \\\n",
    "                                                                               logits=decode_logits, \\\n",
    "                                                                               weights=inverse_weights)\n",
    "            binary_segmenatation_loss = tf.reduce_mean(binary_segmenatation_loss)\n",
    "\n",
    "            ret = {'loss': binary_segmenatation_loss, 'binary_seg_logits': decode_logits}\n",
    "\n",
    "            return ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define cost, optimizer and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "CFG = cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/gpu:0'):\n",
    "    input_tensor = tf.placeholder(dtype=tf.float32,\n",
    "                                  shape=[CFG.TRAIN.BATCH_SIZE, CFG.TRAIN.IMG_HEIGHT,\n",
    "                                         CFG.TRAIN.IMG_WIDTH, 3],\n",
    "                                  name='input_tensor')\n",
    "    binary_label = tf.placeholder(dtype=tf.int64,\n",
    "                                  shape=[CFG.TRAIN.BATCH_SIZE, CFG.TRAIN.IMG_HEIGHT,\n",
    "                                         CFG.TRAIN.IMG_WIDTH, 1],\n",
    "                                         name='binary_label')\n",
    "\n",
    "    # Create the model\n",
    "    phase = tf.placeholder(dtype=tf.string, shape=None, name='net_phase')\n",
    "    net = LaneNet(phase=phase)\n",
    "\n",
    "    # Define loss\n",
    "    compute_ret = net.compute_loss(input_tensor=input_tensor, binary_label=binary_label, name='lanenet_model')\n",
    "    loss = compute_ret['loss']\n",
    "\n",
    "    # Evaluate model\n",
    "    out_logits = compute_ret['binary_seg_logits']\n",
    "    out_logits = tf.nn.softmax(logits=out_logits)\n",
    "    out_logits_out = tf.argmax(out_logits, axis=-1)\n",
    "    out = tf.argmax(out_logits, axis=-1)\n",
    "    out = tf.expand_dims(out, axis=-1)\n",
    "\n",
    "    idx = tf.where(tf.equal(binary_label, 1))\n",
    "    pix_cls_ret = tf.gather_nd(out, idx)\n",
    "    accuracy = tf.count_nonzero(pix_cls_ret)\n",
    "    accuracy = tf.divide(accuracy, tf.cast(tf.shape(pix_cls_ret)[0], tf.int64))\n",
    "\n",
    "    # Define optimizer\n",
    "    global_step = tf.Variable(0, trainable=False)\n",
    "    learning_rate = tf.train.exponential_decay(CFG.TRAIN.LEARNING_RATE, global_step,\n",
    "                                               100000, 0.1, staircase=True)\n",
    "    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "    with tf.control_dependencies(update_ops):\n",
    "        optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, \\\n",
    "                                               momentum=CFG.TRAIN.MOMENTUM).minimize(loss=loss,\n",
    "                                                                                     var_list=tf.trainable_variables(),\n",
    "                                                                                     global_step=global_step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set a session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set sess configuration\n",
    "sess_config = tf.ConfigProto(allow_soft_placement=True)\n",
    "sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION\n",
    "sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH\n",
    "sess_config.gpu_options.allocator_type = 'BFC'\n",
    "\n",
    "sess = tf.Session(config=sess_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'model/tusimple_lanenet/lanenet_model.pb'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set tf saver\n",
    "model_save_dir = 'model/tusimple_lanenet'\n",
    "if not ops.exists(model_save_dir):\n",
    "    os.makedirs(model_save_dir)\n",
    "\n",
    "train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))\n",
    "model_name = 'tusimple_lanenet_{:s}.ckpt'.format(str(train_start_time))\n",
    "model_save_path = ops.join(model_save_dir, model_name)\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "tf.train.write_graph(graph_or_graph_def=sess.graph, logdir='',\n",
    "                     name='{:s}/lanenet_model.pb'.format(model_save_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set tf summary\n",
    "tboard_save_path = 'tboard/tusimple_lanenet_{:s}'.format(str(train_start_time))\n",
    "if not ops.exists(tboard_save_path):\n",
    "    os.makedirs(tboard_save_path)\n",
    "\n",
    "train_accuracy_scalar = tf.summary.scalar(name='train_accuracy', tensor=accuracy)\n",
    "train_loss_scalar = tf.summary.scalar(name='train_loss', tensor=loss)\n",
    "learning_rate_scalar = tf.summary.scalar(name='learning_rate', tensor=learning_rate)\n",
    "merged_summary_op = tf.summary.merge([train_accuracy_scalar, train_loss_scalar, learning_rate_scalar])\n",
    "\n",
    "summary_writer = tf.summary.FileWriter(tboard_save_path)\n",
    "summary_writer.add_graph(sess.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set output image folder\n",
    "image_save_path = 'data_ret/tusimple_lanenet_{:s}'.format(str(train_start_time))\n",
    "if not ops.exists(image_save_path):\n",
    "    os.makedirs(image_save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "VGG_MEAN = [103.939, 116.779, 123.68]\n",
    "\n",
    "history = []\n",
    "train_cost_time_mean = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize_training_data(gt_imgs, binary_gt_labels):\n",
    "    gt_imgs = [cv2.resize(tmp,\n",
    "                          dsize=(CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),\n",
    "                          dst=tmp,\n",
    "                          interpolation=cv2.INTER_LINEAR)\n",
    "               for tmp in gt_imgs]\n",
    "    gt_imgs = [tmp - VGG_MEAN for tmp in gt_imgs]\n",
    "\n",
    "    binary_gt_labels = [cv2.resize(tmp,\n",
    "                                   dsize=(CFG.TRAIN.IMG_WIDTH, CFG.TRAIN.IMG_HEIGHT),\n",
    "                                   dst=tmp,\n",
    "                                   interpolation=cv2.INTER_NEAREST)\n",
    "                        for tmp in binary_gt_labels]\n",
    "    binary_gt_labels = [np.expand_dims(tmp, axis=-1) for tmp in binary_gt_labels]\n",
    "    \n",
    "    return gt_imgs, binary_gt_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0326 23:07:27.180421 21590 <ipython-input-20-53846b8186df>:19] Restore model from last model checkpoint model/tusimple_lanenet/tusimple_lanenet_vgg_2018-10-19-13-33-56.ckpt-200000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:      0 loss= 7.244865 acc= 0.711662 cost_time= 0.495296s \n",
      "Epoch:      1 loss= 3.435430 acc= 0.810470 cost_time= 0.337900s \n",
      "Epoch:      2 loss= 8.279784 acc= 0.701485 cost_time= 0.341886s \n",
      "Epoch:      3 loss= 5.730187 acc= 0.796917 cost_time= 0.317106s \n",
      "Epoch:      4 loss= 3.868070 acc= 0.808751 cost_time= 0.328688s \n",
      "Epoch:      5 loss= 4.734693 acc= 0.789500 cost_time= 0.319542s \n",
      "Epoch:      6 loss= 4.079753 acc= 0.824186 cost_time= 0.314499s \n",
      "Epoch:      7 loss= 6.760619 acc= 0.766607 cost_time= 0.310062s \n",
      "Epoch:      8 loss= 4.865343 acc= 0.798995 cost_time= 0.317200s \n",
      "Epoch:      9 loss= 2.038992 acc= 0.887292 cost_time= 0.330010s \n",
      "Epoch:     10 loss= 3.500103 acc= 0.852683 cost_time= 0.348159s \n",
      "Epoch:     11 loss= 2.915204 acc= 0.845604 cost_time= 0.336218s \n",
      "Epoch:     12 loss= 2.248005 acc= 0.889508 cost_time= 0.332469s \n",
      "Epoch:     13 loss= 4.070166 acc= 0.837687 cost_time= 0.327591s \n",
      "Epoch:     14 loss= 3.769880 acc= 0.800279 cost_time= 0.326402s \n",
      "Epoch:     15 loss= 2.916929 acc= 0.852536 cost_time= 0.336119s \n",
      "Epoch:     16 loss= 2.476630 acc= 0.869026 cost_time= 0.319118s \n",
      "Epoch:     17 loss= 4.067123 acc= 0.752853 cost_time= 0.315278s \n",
      "Epoch:     18 loss= 3.600342 acc= 0.841996 cost_time= 0.327750s \n",
      "Epoch:     19 loss= 1.940678 acc= 0.902825 cost_time= 0.323769s \n",
      "Epoch:     20 loss= 2.722689 acc= 0.828338 cost_time= 0.313451s \n",
      "Epoch:     21 loss= 4.205258 acc= 0.855708 cost_time= 0.337173s \n",
      "Epoch:     22 loss= 2.838475 acc= 0.857444 cost_time= 0.313905s \n",
      "Epoch:     23 loss= 1.659123 acc= 0.901098 cost_time= 0.314067s \n",
      "Epoch:     24 loss= 2.396319 acc= 0.852780 cost_time= 0.334808s \n",
      "Epoch:     25 loss= 2.491442 acc= 0.866131 cost_time= 0.313570s \n",
      "Epoch:     26 loss= 3.025042 acc= 0.828405 cost_time= 0.321294s \n",
      "Epoch:     27 loss= 1.345573 acc= 0.933302 cost_time= 0.319080s \n",
      "Epoch:     28 loss= 1.349129 acc= 0.902322 cost_time= 0.321754s \n",
      "Epoch:     29 loss= 3.478872 acc= 0.826889 cost_time= 0.318340s \n",
      "Epoch:     30 loss= 2.423070 acc= 0.859162 cost_time= 0.317254s \n",
      "Epoch:     31 loss= 1.695652 acc= 0.871602 cost_time= 0.322713s \n",
      "Epoch:     32 loss= 3.495107 acc= 0.846942 cost_time= 0.323718s \n",
      "Epoch:     33 loss= 4.343634 acc= 0.837599 cost_time= 0.320658s \n",
      "Epoch:     34 loss= 2.966678 acc= 0.795328 cost_time= 0.318222s \n",
      "Epoch:     35 loss= 1.143515 acc= 0.912754 cost_time= 0.320675s \n",
      "Epoch:     36 loss= 6.421601 acc= 0.816150 cost_time= 0.319000s \n",
      "Epoch:     37 loss= 1.132146 acc= 0.899530 cost_time= 0.314346s \n",
      "Epoch:     38 loss= 1.544717 acc= 0.870472 cost_time= 0.321494s \n",
      "Epoch:     39 loss= 1.287417 acc= 0.900537 cost_time= 0.339316s \n",
      "Epoch:     40 loss= 1.036212 acc= 0.936615 cost_time= 0.350903s \n",
      "Epoch:     41 loss= 1.984169 acc= 0.856042 cost_time= 0.328753s \n",
      "Epoch:     42 loss= 4.726497 acc= 0.784646 cost_time= 0.324990s \n",
      "Epoch:     43 loss= 1.868112 acc= 0.884760 cost_time= 0.333020s \n",
      "Epoch:     44 loss= 2.508464 acc= 0.886993 cost_time= 0.330162s \n",
      "Epoch:     45 loss= 1.656943 acc= 0.879652 cost_time= 0.325947s \n",
      "Epoch:     46 loss= 1.586317 acc= 0.882945 cost_time= 0.323029s \n",
      "Epoch:     47 loss= 1.465504 acc= 0.883265 cost_time= 0.315318s \n",
      "Epoch:     48 loss= 1.935161 acc= 0.816216 cost_time= 0.340340s \n",
      "Epoch:     49 loss= 1.639714 acc= 0.904045 cost_time= 0.317193s \n",
      "Epoch:     50 loss= 2.221464 acc= 0.845211 cost_time= 0.340315s \n",
      "Epoch:     51 loss= 3.733555 acc= 0.841855 cost_time= 0.316565s \n",
      "Epoch:     52 loss= 1.025637 acc= 0.912514 cost_time= 0.334352s \n",
      "Epoch:     53 loss= 1.272745 acc= 0.906345 cost_time= 0.338444s \n",
      "Epoch:     54 loss= 0.809582 acc= 0.929343 cost_time= 0.330945s \n",
      "Epoch:     55 loss= 1.021396 acc= 0.907652 cost_time= 0.341752s \n",
      "Epoch:     56 loss= 1.618495 acc= 0.850636 cost_time= 0.325153s \n",
      "Epoch:     57 loss= 3.135064 acc= 0.781977 cost_time= 0.318034s \n",
      "Epoch:     58 loss= 2.466390 acc= 0.783391 cost_time= 0.322692s \n",
      "Epoch:     59 loss= 0.953962 acc= 0.920749 cost_time= 0.315531s \n",
      "Epoch:     60 loss= 1.687438 acc= 0.887563 cost_time= 0.317850s \n",
      "Epoch:     61 loss= 1.576129 acc= 0.855298 cost_time= 0.332724s \n",
      "Epoch:     62 loss= 1.599880 acc= 0.868160 cost_time= 0.320261s \n",
      "Epoch:     63 loss= 0.965369 acc= 0.907227 cost_time= 0.317974s \n",
      "Epoch:     64 loss= 1.308532 acc= 0.903982 cost_time= 0.345603s \n",
      "Epoch:     65 loss= 3.754144 acc= 0.744185 cost_time= 0.337899s \n",
      "Epoch:     66 loss= 1.153056 acc= 0.891769 cost_time= 0.338120s \n",
      "Epoch:     67 loss= 2.361902 acc= 0.877863 cost_time= 0.336218s \n",
      "Epoch:     68 loss= 1.015825 acc= 0.921716 cost_time= 0.344306s \n",
      "Epoch:     69 loss= 3.679571 acc= 0.872327 cost_time= 0.339029s \n",
      "Epoch:     70 loss= 2.465923 acc= 0.823242 cost_time= 0.328716s \n",
      "Epoch:     71 loss= 1.492936 acc= 0.877410 cost_time= 0.331931s \n",
      "Epoch:     72 loss= 3.057200 acc= 0.899004 cost_time= 0.326270s \n",
      "Epoch:     73 loss= 1.480406 acc= 0.879774 cost_time= 0.338738s \n",
      "Epoch:     74 loss= 2.053014 acc= 0.877258 cost_time= 0.346026s \n",
      "Epoch:     75 loss= 3.788520 acc= 0.856404 cost_time= 0.369494s \n",
      "Epoch:     76 loss= 0.988340 acc= 0.917866 cost_time= 0.352514s \n",
      "Epoch:     77 loss= 1.009707 acc= 0.914322 cost_time= 0.384520s \n",
      "Epoch:     78 loss= 0.892782 acc= 0.927229 cost_time= 0.334160s \n",
      "Epoch:     79 loss= 1.922846 acc= 0.853881 cost_time= 0.332302s \n",
      "Epoch:     80 loss= 1.310541 acc= 0.896252 cost_time= 0.330207s \n",
      "Epoch:     81 loss= 0.918141 acc= 0.926188 cost_time= 0.338106s \n",
      "Epoch:     82 loss= 0.749711 acc= 0.937457 cost_time= 0.348507s \n",
      "Epoch:     83 loss= 0.894821 acc= 0.922692 cost_time= 0.354942s \n",
      "Epoch:     84 loss= 2.507344 acc= 0.896398 cost_time= 0.334430s \n",
      "Epoch:     85 loss= 3.579875 acc= 0.847011 cost_time= 0.351326s \n",
      "Epoch:     86 loss= 0.879057 acc= 0.915990 cost_time= 0.409477s \n",
      "Epoch:     87 loss= 6.366807 acc= 0.751680 cost_time= 0.340973s \n",
      "Epoch:     88 loss= 1.356477 acc= 0.875388 cost_time= 0.335584s \n",
      "Epoch:     89 loss= 3.157771 acc= 0.723306 cost_time= 0.329893s \n",
      "Epoch:     90 loss= 0.990203 acc= 0.908312 cost_time= 0.332453s \n",
      "Epoch:     91 loss= 0.897410 acc= 0.923796 cost_time= 0.344921s \n",
      "Epoch:     92 loss= 0.876653 acc= 0.909555 cost_time= 0.340668s \n",
      "Epoch:     93 loss= 1.277283 acc= 0.879805 cost_time= 0.334500s \n",
      "Epoch:     94 loss= 0.590017 acc= 0.936291 cost_time= 0.333701s \n",
      "Epoch:     95 loss= 3.334965 acc= 0.846901 cost_time= 0.338833s \n",
      "Epoch:     96 loss= 1.994338 acc= 0.828669 cost_time= 0.333114s \n",
      "Epoch:     97 loss= 1.411867 acc= 0.871200 cost_time= 0.325520s \n",
      "Epoch:     98 loss= 1.208663 acc= 0.869043 cost_time= 0.343457s \n",
      "Epoch:     99 loss= 0.858954 acc= 0.921298 cost_time= 0.344219s \n",
      "Epoch:    100 loss= 0.607149 acc= 0.947163 cost_time= 0.333466s \n",
      "Epoch:    101 loss= 2.381837 acc= 0.883774 cost_time= 0.327011s \n",
      "Epoch:    102 loss= 1.249266 acc= 0.863327 cost_time= 0.327301s \n",
      "Epoch:    103 loss= 1.807831 acc= 0.855886 cost_time= 0.333540s \n",
      "Epoch:    104 loss= 6.159441 acc= 0.775119 cost_time= 0.337383s \n",
      "Epoch:    105 loss= 0.545291 acc= 0.940303 cost_time= 0.331440s \n",
      "Epoch:    106 loss= 3.355253 acc= 0.849774 cost_time= 0.337554s \n",
      "Epoch:    107 loss= 0.940720 acc= 0.910886 cost_time= 0.335091s \n",
      "Epoch:    108 loss= 2.206736 acc= 0.835908 cost_time= 0.344700s \n",
      "Epoch:    109 loss= 1.730078 acc= 0.864561 cost_time= 0.335138s \n",
      "Epoch:    110 loss= 2.990628 acc= 0.791582 cost_time= 0.329983s \n",
      "Epoch:    111 loss= 2.985773 acc= 0.881019 cost_time= 0.338702s \n",
      "Epoch:    112 loss= 1.190211 acc= 0.896585 cost_time= 0.329416s \n",
      "Epoch:    113 loss= 0.956760 acc= 0.903828 cost_time= 0.349935s \n",
      "Epoch:    114 loss= 5.806676 acc= 0.802412 cost_time= 0.363356s \n",
      "Epoch:    115 loss= 0.816256 acc= 0.921126 cost_time= 0.344416s \n",
      "Epoch:    116 loss= 3.044322 acc= 0.876751 cost_time= 0.334262s \n",
      "Epoch:    117 loss= 0.871632 acc= 0.910051 cost_time= 0.339454s \n",
      "Epoch:    118 loss= 0.906055 acc= 0.909742 cost_time= 0.334284s \n",
      "Epoch:    119 loss= 0.809385 acc= 0.926433 cost_time= 0.337546s \n",
      "Epoch:    120 loss= 1.846808 acc= 0.878196 cost_time= 0.336574s \n",
      "Epoch:    121 loss= 1.027846 acc= 0.913807 cost_time= 0.340375s \n",
      "Epoch:    122 loss= 1.082895 acc= 0.895568 cost_time= 0.335640s \n",
      "Epoch:    123 loss= 0.938836 acc= 0.902818 cost_time= 0.337090s \n",
      "Epoch:    124 loss= 0.414267 acc= 0.955445 cost_time= 0.357164s \n",
      "Epoch:    125 loss= 0.667782 acc= 0.927284 cost_time= 0.389929s \n",
      "Epoch:    126 loss= 5.671070 acc= 0.784379 cost_time= 0.345581s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    127 loss= 1.548185 acc= 0.847327 cost_time= 0.333641s \n",
      "Epoch:    128 loss= 3.455930 acc= 0.846435 cost_time= 0.334569s \n",
      "Epoch:    129 loss= 2.594547 acc= 0.800969 cost_time= 0.327717s \n",
      "Epoch:    130 loss= 0.870565 acc= 0.904355 cost_time= 0.345766s \n",
      "Epoch:    131 loss= 0.836028 acc= 0.924582 cost_time= 0.352250s \n",
      "Epoch:    132 loss= 0.670532 acc= 0.932504 cost_time= 0.423507s \n",
      "Epoch:    133 loss= 0.965804 acc= 0.879867 cost_time= 0.348984s \n",
      "Epoch:    134 loss= 0.675763 acc= 0.916180 cost_time= 0.342553s \n",
      "Epoch:    135 loss= 0.691370 acc= 0.933425 cost_time= 0.354347s \n",
      "Epoch:    136 loss= 3.022183 acc= 0.847532 cost_time= 0.344814s \n",
      "Epoch:    137 loss= 2.517225 acc= 0.861451 cost_time= 0.345534s \n",
      "Epoch:    138 loss= 1.020048 acc= 0.898143 cost_time= 0.320245s \n",
      "Epoch:    139 loss= 0.686199 acc= 0.923621 cost_time= 0.345542s \n",
      "Epoch:    140 loss= 2.390042 acc= 0.795956 cost_time= 0.331258s \n",
      "Epoch:    141 loss= 1.036168 acc= 0.898381 cost_time= 0.331115s \n",
      "Epoch:    142 loss= 1.154918 acc= 0.881260 cost_time= 0.334510s \n",
      "Epoch:    143 loss= 7.011434 acc= 0.767775 cost_time= 0.329876s \n",
      "Epoch:    144 loss= 2.241563 acc= 0.873278 cost_time= 0.333702s \n",
      "Epoch:    145 loss= 3.200260 acc= 0.872758 cost_time= 0.338605s \n",
      "Epoch:    146 loss= 0.902024 acc= 0.922483 cost_time= 0.345886s \n",
      "Epoch:    147 loss= 0.778264 acc= 0.927050 cost_time= 0.337516s \n",
      "Epoch:    148 loss= 2.951479 acc= 0.796158 cost_time= 0.341535s \n",
      "Epoch:    149 loss= 3.303499 acc= 0.821498 cost_time= 0.345017s \n",
      "Epoch:    150 loss= 1.054142 acc= 0.920660 cost_time= 0.332360s \n",
      "Epoch:    151 loss= 0.896459 acc= 0.901449 cost_time= 0.333253s \n",
      "Epoch:    152 loss= 0.772216 acc= 0.936482 cost_time= 0.337834s \n",
      "Epoch:    153 loss= 1.022585 acc= 0.883243 cost_time= 0.345979s \n",
      "Epoch:    154 loss= 1.610418 acc= 0.851398 cost_time= 0.337223s \n",
      "Epoch:    155 loss= 1.359131 acc= 0.839500 cost_time= 0.343697s \n",
      "Epoch:    156 loss= 0.731369 acc= 0.921592 cost_time= 0.349879s \n",
      "Epoch:    157 loss= 1.128021 acc= 0.906683 cost_time= 0.335656s \n",
      "Epoch:    158 loss= 1.192176 acc= 0.901149 cost_time= 0.334635s \n",
      "Epoch:    159 loss= 0.564673 acc= 0.947577 cost_time= 0.350296s \n",
      "Epoch:    160 loss= 1.138881 acc= 0.894902 cost_time= 0.345093s \n",
      "Epoch:    161 loss= 0.701296 acc= 0.939773 cost_time= 0.340260s \n",
      "Epoch:    162 loss= 5.196730 acc= 0.823717 cost_time= 0.335692s \n",
      "Epoch:    163 loss= 0.627323 acc= 0.928482 cost_time= 0.355232s \n",
      "Epoch:    164 loss= 0.967791 acc= 0.924123 cost_time= 0.332051s \n",
      "Epoch:    165 loss= 1.069287 acc= 0.893548 cost_time= 0.332000s \n",
      "Epoch:    166 loss= 0.928007 acc= 0.901939 cost_time= 0.329547s \n",
      "Epoch:    167 loss= 0.765084 acc= 0.932115 cost_time= 0.332328s \n",
      "Epoch:    168 loss= 1.021460 acc= 0.931010 cost_time= 0.343097s \n",
      "Epoch:    169 loss= 1.082340 acc= 0.912872 cost_time= 0.349901s \n",
      "Epoch:    170 loss= 0.636238 acc= 0.936199 cost_time= 0.342358s \n",
      "Epoch:    171 loss= 0.707833 acc= 0.924898 cost_time= 0.327078s \n",
      "Epoch:    172 loss= 1.098107 acc= 0.896991 cost_time= 0.330552s \n",
      "Epoch:    173 loss= 1.030591 acc= 0.910216 cost_time= 0.345579s \n",
      "Epoch:    174 loss= 0.982833 acc= 0.900369 cost_time= 0.337552s \n",
      "Epoch:    175 loss= 1.557505 acc= 0.861153 cost_time= 0.358326s \n",
      "Epoch:    176 loss= 1.197592 acc= 0.858830 cost_time= 0.370845s \n",
      "Epoch:    177 loss= 1.412034 acc= 0.884406 cost_time= 0.352699s \n",
      "Epoch:    178 loss= 0.955013 acc= 0.903544 cost_time= 0.339338s \n",
      "Epoch:    179 loss= 1.273715 acc= 0.871960 cost_time= 0.361702s \n",
      "Epoch:    180 loss= 1.048486 acc= 0.910782 cost_time= 0.378285s \n",
      "Epoch:    181 loss= 0.944564 acc= 0.907834 cost_time= 0.372984s \n",
      "Epoch:    182 loss= 2.107216 acc= 0.773695 cost_time= 0.341080s \n",
      "Epoch:    183 loss= 0.686281 acc= 0.905608 cost_time= 0.345539s \n",
      "Epoch:    184 loss= 1.489518 acc= 0.896269 cost_time= 0.354010s \n",
      "Epoch:    185 loss= 0.520784 acc= 0.949819 cost_time= 0.433096s \n",
      "Epoch:    186 loss= 4.467979 acc= 0.880207 cost_time= 0.358194s \n",
      "Epoch:    187 loss= 0.609967 acc= 0.937039 cost_time= 0.343728s \n",
      "Epoch:    188 loss= 0.621409 acc= 0.939918 cost_time= 0.357974s \n",
      "Epoch:    189 loss= 0.731007 acc= 0.927261 cost_time= 0.414704s \n",
      "Epoch:    190 loss= 1.390700 acc= 0.840135 cost_time= 0.349363s \n",
      "Epoch:    191 loss= 0.615024 acc= 0.934916 cost_time= 0.342465s \n",
      "Epoch:    192 loss= 0.567837 acc= 0.921654 cost_time= 0.342687s \n",
      "Epoch:    193 loss= 0.745232 acc= 0.922550 cost_time= 0.333585s \n",
      "Epoch:    194 loss= 0.627706 acc= 0.928772 cost_time= 0.334782s \n",
      "Epoch:    195 loss= 0.713771 acc= 0.925521 cost_time= 0.316016s \n",
      "Epoch:    196 loss= 2.040567 acc= 0.882725 cost_time= 0.354259s \n",
      "Epoch:    197 loss= 0.948044 acc= 0.914746 cost_time= 0.379356s \n",
      "Epoch:    198 loss= 1.293435 acc= 0.861704 cost_time= 0.328900s \n",
      "Epoch:    199 loss= 0.477242 acc= 0.945333 cost_time= 0.316775s \n",
      "Epoch:    200 loss= 0.559557 acc= 0.930992 cost_time= 0.334925s \n",
      "Epoch:    201 loss= 0.830490 acc= 0.906640 cost_time= 0.319234s \n",
      "Epoch:    202 loss= 1.069535 acc= 0.854365 cost_time= 0.319111s \n",
      "Epoch:    203 loss= 0.553334 acc= 0.929700 cost_time= 0.331576s \n",
      "Epoch:    204 loss= 1.076860 acc= 0.909851 cost_time= 0.344422s \n",
      "Epoch:    205 loss= 1.083373 acc= 0.884432 cost_time= 0.331523s \n",
      "Epoch:    206 loss= 0.523063 acc= 0.941947 cost_time= 0.336108s \n",
      "Epoch:    207 loss= 2.679203 acc= 0.830704 cost_time= 0.341951s \n",
      "Epoch:    208 loss= 1.017128 acc= 0.898231 cost_time= 0.334781s \n",
      "Epoch:    209 loss= 0.868540 acc= 0.922239 cost_time= 0.350104s \n",
      "Epoch:    210 loss= 1.921177 acc= 0.887707 cost_time= 0.334129s \n",
      "Epoch:    211 loss= 1.971845 acc= 0.833543 cost_time= 0.328161s \n",
      "Epoch:    212 loss= 0.496751 acc= 0.939828 cost_time= 0.343678s \n",
      "Epoch:    213 loss= 1.365075 acc= 0.824971 cost_time= 0.336983s \n",
      "Epoch:    214 loss= 2.721359 acc= 0.859833 cost_time= 0.330818s \n",
      "Epoch:    215 loss= 1.084691 acc= 0.881929 cost_time= 0.338190s \n",
      "Epoch:    216 loss= 0.470745 acc= 0.955203 cost_time= 0.378678s \n",
      "Epoch:    217 loss= 0.771439 acc= 0.913651 cost_time= 0.341153s \n",
      "Epoch:    218 loss= 1.074845 acc= 0.865039 cost_time= 0.327237s \n",
      "Epoch:    219 loss= 2.379626 acc= 0.885169 cost_time= 0.360422s \n",
      "Epoch:    220 loss= 0.526493 acc= 0.935299 cost_time= 0.349168s \n",
      "Epoch:    221 loss= 1.110453 acc= 0.872453 cost_time= 0.335496s \n",
      "Epoch:    222 loss= 4.753802 acc= 0.841794 cost_time= 0.342155s \n",
      "Epoch:    223 loss= 2.642729 acc= 0.836292 cost_time= 0.329845s \n",
      "Epoch:    224 loss= 0.724638 acc= 0.929029 cost_time= 0.336348s \n",
      "Epoch:    225 loss= 1.110515 acc= 0.863457 cost_time= 0.347001s \n",
      "Epoch:    226 loss= 0.765105 acc= 0.921363 cost_time= 0.342056s \n",
      "Epoch:    227 loss= 0.773692 acc= 0.934258 cost_time= 0.403220s \n",
      "Epoch:    228 loss= 1.831746 acc= 0.782946 cost_time= 0.333025s \n",
      "Epoch:    229 loss= 1.054188 acc= 0.876649 cost_time= 0.337959s \n",
      "Epoch:    230 loss= 0.536686 acc= 0.942895 cost_time= 0.344072s \n",
      "Epoch:    231 loss= 0.524856 acc= 0.934454 cost_time= 0.340219s \n",
      "Epoch:    232 loss= 0.803980 acc= 0.927429 cost_time= 0.340912s \n",
      "Epoch:    233 loss= 0.587442 acc= 0.938481 cost_time= 0.340740s \n",
      "Epoch:    234 loss= 0.504840 acc= 0.945458 cost_time= 0.332535s \n",
      "Epoch:    235 loss= 2.046949 acc= 0.897978 cost_time= 0.340744s \n",
      "Epoch:    236 loss= 0.516761 acc= 0.945965 cost_time= 0.346566s \n",
      "Epoch:    237 loss= 2.550073 acc= 0.654731 cost_time= 0.326183s \n",
      "Epoch:    238 loss= 0.885316 acc= 0.897054 cost_time= 0.417659s \n",
      "Epoch:    239 loss= 0.883931 acc= 0.912437 cost_time= 0.348958s \n",
      "Epoch:    240 loss= 0.849324 acc= 0.892831 cost_time= 0.329777s \n",
      "Epoch:    241 loss= 0.606711 acc= 0.928822 cost_time= 0.325279s \n",
      "Epoch:    242 loss= 1.223813 acc= 0.844749 cost_time= 0.333154s \n",
      "Epoch:    243 loss= 0.580269 acc= 0.924870 cost_time= 0.321419s \n",
      "Epoch:    244 loss= 0.882862 acc= 0.898733 cost_time= 0.341967s \n",
      "Epoch:    245 loss= 0.951584 acc= 0.906813 cost_time= 0.323469s \n",
      "Epoch:    246 loss= 1.104393 acc= 0.863905 cost_time= 0.330488s \n",
      "Epoch:    247 loss= 0.831520 acc= 0.893519 cost_time= 0.330577s \n",
      "Epoch:    248 loss= 0.665120 acc= 0.921578 cost_time= 0.316930s \n",
      "Epoch:    249 loss= 0.445119 acc= 0.939015 cost_time= 0.342165s \n",
      "Epoch:    250 loss= 0.505104 acc= 0.949022 cost_time= 0.321758s \n",
      "Epoch:    251 loss= 0.573798 acc= 0.920369 cost_time= 0.315408s \n",
      "Epoch:    252 loss= 0.653049 acc= 0.927766 cost_time= 0.318445s \n",
      "Epoch:    253 loss= 0.615564 acc= 0.927074 cost_time= 0.315291s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    254 loss= 0.770810 acc= 0.928541 cost_time= 0.317345s \n",
      "Epoch:    255 loss= 0.752931 acc= 0.908700 cost_time= 0.331206s \n",
      "Epoch:    256 loss= 0.629886 acc= 0.936740 cost_time= 0.318940s \n",
      "Epoch:    257 loss= 0.779341 acc= 0.915622 cost_time= 0.319631s \n",
      "Epoch:    258 loss= 0.491283 acc= 0.938112 cost_time= 0.323834s \n",
      "Epoch:    259 loss= 1.788641 acc= 0.797589 cost_time= 0.331386s \n",
      "Epoch:    260 loss= 2.153614 acc= 0.803107 cost_time= 0.310445s \n",
      "Epoch:    261 loss= 2.905858 acc= 0.854816 cost_time= 0.316370s \n",
      "Epoch:    262 loss= 0.922777 acc= 0.896348 cost_time= 0.323247s \n",
      "Epoch:    263 loss= 2.613820 acc= 0.832768 cost_time= 0.314975s \n",
      "Epoch:    264 loss= 3.943381 acc= 0.822780 cost_time= 0.319832s \n",
      "Epoch:    265 loss= 1.125989 acc= 0.857326 cost_time= 0.315120s \n",
      "Epoch:    266 loss= 0.774866 acc= 0.911787 cost_time= 0.313447s \n",
      "Epoch:    267 loss= 2.928788 acc= 0.847493 cost_time= 0.314289s \n",
      "Epoch:    268 loss= 0.726219 acc= 0.918883 cost_time= 0.319734s \n",
      "Epoch:    269 loss= 0.951320 acc= 0.893113 cost_time= 0.317963s \n",
      "Epoch:    270 loss= 1.174195 acc= 0.875613 cost_time= 0.317372s \n",
      "Epoch:    271 loss= 0.660964 acc= 0.930207 cost_time= 0.322929s \n",
      "Epoch:    272 loss= 1.729968 acc= 0.875277 cost_time= 0.319194s \n",
      "Epoch:    273 loss= 1.702192 acc= 0.873427 cost_time= 0.316523s \n",
      "Epoch:    274 loss= 0.892705 acc= 0.884020 cost_time= 0.328666s \n",
      "Epoch:    275 loss= 0.990750 acc= 0.886347 cost_time= 0.316945s \n",
      "Epoch:    276 loss= 0.584304 acc= 0.933108 cost_time= 0.333821s \n",
      "Epoch:    277 loss= 0.611254 acc= 0.934000 cost_time= 0.319423s \n",
      "Epoch:    278 loss= 0.539026 acc= 0.941660 cost_time= 0.321098s \n",
      "Epoch:    279 loss= 0.568941 acc= 0.928310 cost_time= 0.315446s \n",
      "Epoch:    280 loss= 1.112128 acc= 0.859995 cost_time= 0.314783s \n",
      "Epoch:    281 loss= 1.037463 acc= 0.894262 cost_time= 0.316008s \n",
      "Epoch:    282 loss= 1.119640 acc= 0.869510 cost_time= 0.312229s \n",
      "Epoch:    283 loss= 0.864385 acc= 0.885454 cost_time= 0.337368s \n",
      "Epoch:    284 loss= 0.469305 acc= 0.936292 cost_time= 0.320220s \n",
      "Epoch:    285 loss= 0.760284 acc= 0.928360 cost_time= 0.321916s \n",
      "Epoch:    286 loss= 0.724548 acc= 0.918450 cost_time= 0.335077s \n",
      "Epoch:    287 loss= 0.737348 acc= 0.912121 cost_time= 0.322021s \n",
      "Epoch:    288 loss= 1.660141 acc= 0.883517 cost_time= 0.318611s \n",
      "Epoch:    289 loss= 0.757367 acc= 0.903224 cost_time= 0.316749s \n",
      "Epoch:    290 loss= 0.967755 acc= 0.924942 cost_time= 0.319370s \n",
      "Epoch:    291 loss= 2.092249 acc= 0.883240 cost_time= 0.315333s \n",
      "Epoch:    292 loss= 1.011466 acc= 0.885925 cost_time= 0.335483s \n",
      "Epoch:    293 loss= 0.689681 acc= 0.922961 cost_time= 0.320905s \n",
      "Epoch:    294 loss= 2.253438 acc= 0.855352 cost_time= 0.318001s \n",
      "Epoch:    295 loss= 1.678351 acc= 0.899906 cost_time= 0.315792s \n",
      "Epoch:    296 loss= 0.475605 acc= 0.950783 cost_time= 0.318979s \n",
      "Epoch:    297 loss= 0.502292 acc= 0.937829 cost_time= 0.321050s \n",
      "Epoch:    298 loss= 0.722117 acc= 0.911583 cost_time= 0.319363s \n",
      "Epoch:    299 loss= 1.328618 acc= 0.896965 cost_time= 0.336416s \n",
      "Epoch:    300 loss= 1.044178 acc= 0.886365 cost_time= 0.323746s \n",
      "Epoch:    301 loss= 0.560377 acc= 0.922641 cost_time= 0.317856s \n",
      "Epoch:    302 loss= 0.563127 acc= 0.940191 cost_time= 0.322465s \n",
      "Epoch:    303 loss= 0.659760 acc= 0.893267 cost_time= 0.318534s \n",
      "Epoch:    304 loss= 0.614708 acc= 0.914392 cost_time= 0.324194s \n",
      "Epoch:    305 loss= 0.936150 acc= 0.899047 cost_time= 0.320181s \n",
      "Epoch:    306 loss= 0.514332 acc= 0.936782 cost_time= 0.334044s \n",
      "Epoch:    307 loss= 4.606051 acc= 0.777497 cost_time= 0.312063s \n",
      "Epoch:    308 loss= 2.892818 acc= 0.828924 cost_time= 0.328074s \n",
      "Epoch:    309 loss= 1.100965 acc= 0.881260 cost_time= 0.318822s \n",
      "Epoch:    310 loss= 0.801136 acc= 0.880417 cost_time= 0.315559s \n",
      "Epoch:    311 loss= 0.531318 acc= 0.935373 cost_time= 0.321645s \n",
      "Epoch:    312 loss= 1.506950 acc= 0.884456 cost_time= 0.318865s \n",
      "Epoch:    313 loss= 0.807050 acc= 0.904647 cost_time= 0.324106s \n",
      "Epoch:    314 loss= 0.427395 acc= 0.951017 cost_time= 0.334922s \n",
      "Epoch:    315 loss= 5.033383 acc= 0.834198 cost_time= 0.321999s \n",
      "Epoch:    316 loss= 0.951263 acc= 0.908484 cost_time= 0.318308s \n",
      "Epoch:    317 loss= 0.538772 acc= 0.945874 cost_time= 0.320275s \n",
      "Epoch:    318 loss= 0.515755 acc= 0.937398 cost_time= 0.334691s \n",
      "Epoch:    319 loss= 0.571547 acc= 0.939334 cost_time= 0.319849s \n",
      "Epoch:    320 loss= 2.021590 acc= 0.891450 cost_time= 0.322823s \n",
      "Epoch:    321 loss= 0.650425 acc= 0.914300 cost_time= 0.314586s \n",
      "Epoch:    322 loss= 2.546535 acc= 0.859783 cost_time= 0.332303s \n",
      "Epoch:    323 loss= 0.738294 acc= 0.899995 cost_time= 0.319481s \n",
      "Epoch:    324 loss= 0.505580 acc= 0.936724 cost_time= 0.336316s \n",
      "Epoch:    325 loss= 1.311041 acc= 0.908183 cost_time= 0.332824s \n",
      "Epoch:    326 loss= 0.439569 acc= 0.946904 cost_time= 0.340138s \n",
      "Epoch:    327 loss= 1.158340 acc= 0.885940 cost_time= 0.320902s \n",
      "Epoch:    328 loss= 1.807761 acc= 0.894880 cost_time= 0.319204s \n",
      "Epoch:    329 loss= 1.362954 acc= 0.884843 cost_time= 0.319224s \n",
      "Epoch:    330 loss= 0.898971 acc= 0.892602 cost_time= 0.317000s \n",
      "Epoch:    331 loss= 0.728199 acc= 0.917557 cost_time= 0.320779s \n",
      "Epoch:    332 loss= 0.817781 acc= 0.912332 cost_time= 0.320584s \n",
      "Epoch:    333 loss= 0.912077 acc= 0.880761 cost_time= 0.319252s \n",
      "Epoch:    334 loss= 3.578275 acc= 0.799233 cost_time= 0.315554s \n",
      "Epoch:    335 loss= 1.227433 acc= 0.853243 cost_time= 0.315331s \n",
      "Epoch:    336 loss= 0.485524 acc= 0.920791 cost_time= 0.317955s \n",
      "Epoch:    337 loss= 3.191701 acc= 0.846351 cost_time= 0.318004s \n",
      "Epoch:    338 loss= 1.118359 acc= 0.851114 cost_time= 0.311153s \n",
      "Epoch:    339 loss= 3.092534 acc= 0.807818 cost_time= 0.319690s \n",
      "Epoch:    340 loss= 1.005026 acc= 0.884802 cost_time= 0.319916s \n",
      "Epoch:    341 loss= 0.519473 acc= 0.942191 cost_time= 0.322686s \n",
      "Epoch:    342 loss= 0.572387 acc= 0.919687 cost_time= 0.314733s \n",
      "Epoch:    343 loss= 0.713018 acc= 0.920577 cost_time= 0.320937s \n",
      "Epoch:    344 loss= 1.461524 acc= 0.850385 cost_time= 0.312962s \n",
      "Epoch:    345 loss= 1.195532 acc= 0.894320 cost_time= 0.318279s \n",
      "Epoch:    346 loss= 1.159596 acc= 0.905900 cost_time= 0.317764s \n",
      "Epoch:    347 loss= 0.643480 acc= 0.916785 cost_time= 0.321472s \n",
      "Epoch:    348 loss= 2.248537 acc= 0.896616 cost_time= 0.320982s \n",
      "Epoch:    349 loss= 0.642926 acc= 0.914153 cost_time= 0.318127s \n",
      "Epoch:    350 loss= 2.980741 acc= 0.825500 cost_time= 0.332865s \n",
      "Epoch:    351 loss= 0.564387 acc= 0.917785 cost_time= 0.334529s \n",
      "Epoch:    352 loss= 0.752061 acc= 0.912397 cost_time= 0.319111s \n",
      "Epoch:    353 loss= 0.408412 acc= 0.947918 cost_time= 0.335781s \n",
      "Epoch:    354 loss= 0.524707 acc= 0.939225 cost_time= 0.320128s \n",
      "Epoch:    355 loss= 0.593802 acc= 0.935898 cost_time= 0.319749s \n",
      "Epoch:    356 loss= 1.254072 acc= 0.893938 cost_time= 0.321966s \n",
      "Epoch:    357 loss= 0.966651 acc= 0.888167 cost_time= 0.320515s \n",
      "Epoch:    358 loss= 1.494752 acc= 0.827540 cost_time= 0.317302s \n",
      "Epoch:    359 loss= 0.525170 acc= 0.935834 cost_time= 0.323635s \n",
      "Epoch:    360 loss= 0.601132 acc= 0.926228 cost_time= 0.340203s \n",
      "Epoch:    361 loss= 0.464485 acc= 0.943776 cost_time= 0.320433s \n",
      "Epoch:    362 loss= 0.451733 acc= 0.938296 cost_time= 0.337923s \n",
      "Epoch:    363 loss= 0.568915 acc= 0.934653 cost_time= 0.316674s \n",
      "Epoch:    364 loss= 2.880400 acc= 0.836995 cost_time= 0.321872s \n",
      "Epoch:    365 loss= 0.943036 acc= 0.897378 cost_time= 0.335434s \n",
      "Epoch:    366 loss= 0.851049 acc= 0.872707 cost_time= 0.320899s \n",
      "Epoch:    367 loss= 0.501068 acc= 0.931304 cost_time= 0.321314s \n",
      "Epoch:    368 loss= 2.397099 acc= 0.897250 cost_time= 0.336132s \n",
      "Epoch:    369 loss= 0.609222 acc= 0.934405 cost_time= 0.322111s \n",
      "Epoch:    370 loss= 0.456567 acc= 0.948488 cost_time= 0.323651s \n",
      "Epoch:    371 loss= 0.950513 acc= 0.905162 cost_time= 0.325777s \n",
      "Epoch:    372 loss= 0.803459 acc= 0.924036 cost_time= 0.320969s \n",
      "Epoch:    373 loss= 1.684335 acc= 0.836187 cost_time= 0.316072s \n",
      "Epoch:    374 loss= 1.785122 acc= 0.868408 cost_time= 0.317777s \n",
      "Epoch:    375 loss= 0.798054 acc= 0.900673 cost_time= 0.318020s \n",
      "Epoch:    376 loss= 0.494909 acc= 0.931521 cost_time= 0.337582s \n",
      "Epoch:    377 loss= 0.539414 acc= 0.907122 cost_time= 0.315942s \n",
      "Epoch:    378 loss= 1.503339 acc= 0.852015 cost_time= 0.314799s \n",
      "Epoch:    379 loss= 0.713617 acc= 0.904626 cost_time= 0.322285s \n",
      "Epoch:    380 loss= 0.571286 acc= 0.931117 cost_time= 0.325724s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    381 loss= 0.711479 acc= 0.915887 cost_time= 0.313633s \n",
      "Epoch:    382 loss= 1.478314 acc= 0.808256 cost_time= 0.317727s \n",
      "Epoch:    383 loss= 0.996363 acc= 0.910704 cost_time= 0.322428s \n",
      "Epoch:    384 loss= 2.092217 acc= 0.864803 cost_time= 0.321554s \n",
      "Epoch:    385 loss= 2.028803 acc= 0.825239 cost_time= 0.323401s \n",
      "Epoch:    386 loss= 0.676561 acc= 0.928548 cost_time= 0.320855s \n",
      "Epoch:    387 loss= 2.408459 acc= 0.874400 cost_time= 0.330594s \n",
      "Epoch:    388 loss= 0.501579 acc= 0.930644 cost_time= 0.313727s \n",
      "Epoch:    389 loss= 0.752375 acc= 0.904976 cost_time= 0.322467s \n",
      "Epoch:    390 loss= 0.595475 acc= 0.931212 cost_time= 0.318672s \n",
      "Epoch:    391 loss= 1.236553 acc= 0.817175 cost_time= 0.326644s \n",
      "Epoch:    392 loss= 1.067248 acc= 0.862743 cost_time= 0.315019s \n",
      "Epoch:    393 loss= 0.776775 acc= 0.920914 cost_time= 0.319281s \n",
      "Epoch:    394 loss= 0.756397 acc= 0.910844 cost_time= 0.336264s \n",
      "Epoch:    395 loss= 0.492711 acc= 0.927291 cost_time= 0.322481s \n",
      "Epoch:    396 loss= 0.573060 acc= 0.932375 cost_time= 0.320650s \n",
      "Epoch:    397 loss= 0.591334 acc= 0.926682 cost_time= 0.315557s \n",
      "Epoch:    398 loss= 0.558611 acc= 0.904287 cost_time= 0.314238s \n",
      "Epoch:    399 loss= 0.662105 acc= 0.913640 cost_time= 0.334975s \n",
      "Epoch:    400 loss= 1.320385 acc= 0.796125 cost_time= 0.313949s \n",
      "Epoch:    401 loss= 1.300916 acc= 0.853678 cost_time= 0.318027s \n",
      "Epoch:    402 loss= 0.583591 acc= 0.924921 cost_time= 0.319269s \n",
      "Epoch:    403 loss= 0.515582 acc= 0.922998 cost_time= 0.325899s \n",
      "Epoch:    404 loss= 1.221125 acc= 0.856042 cost_time= 0.318289s \n",
      "Epoch:    405 loss= 1.263616 acc= 0.895991 cost_time= 0.319143s \n",
      "Epoch:    406 loss= 1.196707 acc= 0.882032 cost_time= 0.317207s \n",
      "Epoch:    407 loss= 0.913805 acc= 0.894593 cost_time= 0.326812s \n",
      "Epoch:    408 loss= 0.553186 acc= 0.926689 cost_time= 0.316537s \n",
      "Epoch:    409 loss= 0.373334 acc= 0.955007 cost_time= 0.324334s \n",
      "Epoch:    410 loss= 0.432134 acc= 0.936667 cost_time= 0.332248s \n",
      "Epoch:    411 loss= 1.215682 acc= 0.853216 cost_time= 0.317988s \n",
      "Epoch:    412 loss= 0.509440 acc= 0.944740 cost_time= 0.319883s \n",
      "Epoch:    413 loss= 0.456089 acc= 0.938133 cost_time= 0.323545s \n",
      "Epoch:    414 loss= 0.913615 acc= 0.886471 cost_time= 0.323870s \n",
      "Epoch:    415 loss= 0.482345 acc= 0.933518 cost_time= 0.336071s \n",
      "Epoch:    416 loss= 1.561661 acc= 0.851874 cost_time= 0.324271s \n",
      "Epoch:    417 loss= 0.692443 acc= 0.921495 cost_time= 0.319975s \n",
      "Epoch:    418 loss= 0.733385 acc= 0.904830 cost_time= 0.320295s \n",
      "Epoch:    419 loss= 0.773426 acc= 0.914063 cost_time= 0.322593s \n",
      "Epoch:    420 loss= 0.626942 acc= 0.922034 cost_time= 0.317908s \n",
      "Epoch:    421 loss= 1.614951 acc= 0.743793 cost_time= 0.330158s \n",
      "Epoch:    422 loss= 0.585412 acc= 0.903279 cost_time= 0.325899s \n",
      "Epoch:    423 loss= 0.520638 acc= 0.937172 cost_time= 0.319918s \n",
      "Epoch:    424 loss= 0.640210 acc= 0.935117 cost_time= 0.319072s \n",
      "Epoch:    425 loss= 1.082630 acc= 0.888208 cost_time= 0.318665s \n",
      "Epoch:    426 loss= 0.401155 acc= 0.933870 cost_time= 0.323540s \n",
      "Epoch:    427 loss= 0.464883 acc= 0.939603 cost_time= 0.327208s \n",
      "Epoch:    428 loss= 0.702674 acc= 0.915962 cost_time= 0.324179s \n",
      "Epoch:    429 loss= 0.419231 acc= 0.948417 cost_time= 0.319123s \n",
      "Epoch:    430 loss= 2.981417 acc= 0.810273 cost_time= 0.334725s \n",
      "Epoch:    431 loss= 0.555436 acc= 0.935563 cost_time= 0.322481s \n",
      "Epoch:    432 loss= 0.445210 acc= 0.941678 cost_time= 0.325044s \n",
      "Epoch:    433 loss= 0.727001 acc= 0.913441 cost_time= 0.329794s \n",
      "Epoch:    434 loss= 0.483763 acc= 0.916284 cost_time= 0.322132s \n",
      "Epoch:    435 loss= 0.884465 acc= 0.868722 cost_time= 0.321827s \n",
      "Epoch:    436 loss= 0.895517 acc= 0.894474 cost_time= 0.331320s \n",
      "Epoch:    437 loss= 0.605920 acc= 0.930917 cost_time= 0.326863s \n",
      "Epoch:    438 loss= 0.704369 acc= 0.900988 cost_time= 0.319149s \n",
      "Epoch:    439 loss= 2.479978 acc= 0.726791 cost_time= 0.312404s \n",
      "Epoch:    440 loss= 0.382696 acc= 0.935162 cost_time= 0.327631s \n",
      "Epoch:    441 loss= 0.545777 acc= 0.941923 cost_time= 0.329810s \n",
      "Epoch:    442 loss= 0.498659 acc= 0.933466 cost_time= 0.326574s \n",
      "Epoch:    443 loss= 0.432091 acc= 0.947099 cost_time= 0.335207s \n",
      "Epoch:    444 loss= 0.732434 acc= 0.904653 cost_time= 0.318544s \n",
      "Epoch:    445 loss= 5.904239 acc= 0.727035 cost_time= 0.316232s \n",
      "Epoch:    446 loss= 1.528972 acc= 0.883929 cost_time= 0.337682s \n",
      "Epoch:    447 loss= 0.620653 acc= 0.916398 cost_time= 0.320674s \n",
      "Epoch:    448 loss= 0.543643 acc= 0.911232 cost_time= 0.317963s \n",
      "Epoch:    449 loss= 0.460219 acc= 0.942860 cost_time= 0.334862s \n",
      "Epoch:    450 loss= 0.647406 acc= 0.932846 cost_time= 0.322088s \n",
      "Epoch:    451 loss= 1.411476 acc= 0.869421 cost_time= 0.319484s \n",
      "Epoch:    452 loss= 0.661943 acc= 0.906276 cost_time= 0.323861s \n",
      "Epoch:    453 loss= 0.968789 acc= 0.850499 cost_time= 0.317395s \n",
      "Epoch:    454 loss= 0.815116 acc= 0.889317 cost_time= 0.319278s \n",
      "Epoch:    455 loss= 1.029519 acc= 0.868514 cost_time= 0.336169s \n",
      "Epoch:    456 loss= 1.383834 acc= 0.877118 cost_time= 0.318325s \n",
      "Epoch:    457 loss= 0.470320 acc= 0.945781 cost_time= 0.337009s \n",
      "Epoch:    458 loss= 0.687999 acc= 0.907511 cost_time= 0.331208s \n",
      "Epoch:    459 loss= 0.343118 acc= 0.946587 cost_time= 0.327366s \n",
      "Epoch:    460 loss= 1.932932 acc= 0.846960 cost_time= 0.313314s \n",
      "Epoch:    461 loss= 0.592198 acc= 0.916889 cost_time= 0.337773s \n",
      "Epoch:    462 loss= 0.744672 acc= 0.887148 cost_time= 0.317307s \n",
      "Epoch:    463 loss= 0.538920 acc= 0.934770 cost_time= 0.314282s \n",
      "Epoch:    464 loss= 0.825095 acc= 0.887701 cost_time= 0.317344s \n",
      "Epoch:    465 loss= 0.533132 acc= 0.926272 cost_time= 0.325293s \n",
      "Epoch:    466 loss= 0.650579 acc= 0.917814 cost_time= 0.330665s \n",
      "Epoch:    467 loss= 0.688676 acc= 0.902501 cost_time= 0.314836s \n",
      "Epoch:    468 loss= 0.441729 acc= 0.934011 cost_time= 0.326104s \n",
      "Epoch:    469 loss= 0.737498 acc= 0.909801 cost_time= 0.316761s \n",
      "Epoch:    470 loss= 0.613237 acc= 0.930587 cost_time= 0.333170s \n",
      "Epoch:    471 loss= 0.416780 acc= 0.934010 cost_time= 0.316834s \n",
      "Epoch:    472 loss= 0.626467 acc= 0.928310 cost_time= 0.316982s \n",
      "Epoch:    473 loss= 1.114388 acc= 0.813773 cost_time= 0.320278s \n",
      "Epoch:    474 loss= 0.679875 acc= 0.920139 cost_time= 0.322582s \n",
      "Epoch:    475 loss= 0.524000 acc= 0.925958 cost_time= 0.319217s \n",
      "Epoch:    476 loss= 0.484074 acc= 0.939373 cost_time= 0.327392s \n",
      "Epoch:    477 loss= 0.978269 acc= 0.870048 cost_time= 0.316741s \n",
      "Epoch:    478 loss= 2.058152 acc= 0.834248 cost_time= 0.321616s \n",
      "Epoch:    479 loss= 0.958259 acc= 0.851871 cost_time= 0.318356s \n",
      "Epoch:    480 loss= 0.441129 acc= 0.940038 cost_time= 0.346084s \n",
      "Epoch:    481 loss= 0.623348 acc= 0.898913 cost_time= 0.378629s \n",
      "Epoch:    482 loss= 0.714170 acc= 0.911908 cost_time= 0.383655s \n",
      "Epoch:    483 loss= 1.057014 acc= 0.907818 cost_time= 0.335156s \n",
      "Epoch:    484 loss= 1.494492 acc= 0.881497 cost_time= 0.332133s \n",
      "Epoch:    485 loss= 0.784121 acc= 0.887778 cost_time= 0.346847s \n",
      "Epoch:    486 loss= 0.715915 acc= 0.896718 cost_time= 0.329032s \n",
      "Epoch:    487 loss= 2.207345 acc= 0.877940 cost_time= 0.367603s \n",
      "Epoch:    488 loss= 1.065662 acc= 0.862191 cost_time= 0.334244s \n",
      "Epoch:    489 loss= 1.288454 acc= 0.828308 cost_time= 0.337471s \n",
      "Epoch:    490 loss= 0.422746 acc= 0.941790 cost_time= 0.367635s \n",
      "Epoch:    491 loss= 0.587119 acc= 0.936702 cost_time= 0.325022s \n",
      "Epoch:    492 loss= 0.959939 acc= 0.852937 cost_time= 0.323145s \n",
      "Epoch:    493 loss= 0.789326 acc= 0.905543 cost_time= 0.318208s \n",
      "Epoch:    494 loss= 0.497675 acc= 0.928074 cost_time= 0.339051s \n",
      "Epoch:    495 loss= 1.030527 acc= 0.883658 cost_time= 0.332374s \n",
      "Epoch:    496 loss= 0.933847 acc= 0.874981 cost_time= 0.318244s \n",
      "Epoch:    497 loss= 0.436856 acc= 0.933452 cost_time= 0.342913s \n",
      "Epoch:    498 loss= 2.187123 acc= 0.846631 cost_time= 0.338575s \n",
      "Epoch:    499 loss= 0.464960 acc= 0.933654 cost_time= 0.336331s \n",
      "Epoch:    500 loss= 1.210165 acc= 0.837051 cost_time= 0.326666s \n",
      "Epoch:    501 loss= 0.909126 acc= 0.862840 cost_time= 0.341946s \n",
      "Epoch:    502 loss= 0.955361 acc= 0.905869 cost_time= 0.346614s \n",
      "Epoch:    503 loss= 2.051642 acc= 0.882593 cost_time= 0.330053s \n",
      "Epoch:    504 loss= 0.735525 acc= 0.873107 cost_time= 0.330948s \n",
      "Epoch:    505 loss= 0.729347 acc= 0.885587 cost_time= 0.344095s \n",
      "Epoch:    506 loss= 1.434667 acc= 0.820276 cost_time= 0.346508s \n",
      "Epoch:    507 loss= 3.312732 acc= 0.877549 cost_time= 0.342465s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    508 loss= 1.192156 acc= 0.859538 cost_time= 0.339851s \n",
      "Epoch:    509 loss= 0.582374 acc= 0.921959 cost_time= 0.342895s \n",
      "Epoch:    510 loss= 0.675743 acc= 0.924072 cost_time= 0.356995s \n",
      "Epoch:    511 loss= 0.719290 acc= 0.889512 cost_time= 0.366997s \n",
      "Epoch:    512 loss= 0.457220 acc= 0.933863 cost_time= 0.345175s \n",
      "Epoch:    513 loss= 2.283716 acc= 0.867856 cost_time= 0.327660s \n",
      "Epoch:    514 loss= 2.836793 acc= 0.871801 cost_time= 0.349924s \n",
      "Epoch:    515 loss= 2.074419 acc= 0.837878 cost_time= 0.344641s \n",
      "Epoch:    516 loss= 0.398440 acc= 0.946797 cost_time= 0.352882s \n",
      "Epoch:    517 loss= 0.507244 acc= 0.925383 cost_time= 0.347996s \n",
      "Epoch:    518 loss= 1.167444 acc= 0.884477 cost_time= 0.343573s \n",
      "Epoch:    519 loss= 0.476327 acc= 0.939376 cost_time= 0.336756s \n",
      "Epoch:    520 loss= 0.510367 acc= 0.944070 cost_time= 0.334517s \n",
      "Epoch:    521 loss= 0.478771 acc= 0.920007 cost_time= 0.342205s \n",
      "Epoch:    522 loss= 0.532008 acc= 0.917446 cost_time= 0.334393s \n",
      "Epoch:    523 loss= 0.384371 acc= 0.939085 cost_time= 0.339302s \n",
      "Epoch:    524 loss= 1.257024 acc= 0.892580 cost_time= 0.331088s \n",
      "Epoch:    525 loss= 2.099912 acc= 0.881421 cost_time= 0.333414s \n",
      "Epoch:    526 loss= 1.069013 acc= 0.857876 cost_time= 0.334059s \n",
      "Epoch:    527 loss= 1.405493 acc= 0.898158 cost_time= 0.341338s \n",
      "Epoch:    528 loss= 3.203360 acc= 0.891302 cost_time= 0.330381s \n",
      "Epoch:    529 loss= 1.163574 acc= 0.894928 cost_time= 0.329868s \n",
      "Epoch:    530 loss= 0.664772 acc= 0.921883 cost_time= 0.333698s \n",
      "Epoch:    531 loss= 0.622194 acc= 0.924102 cost_time= 0.340826s \n",
      "Epoch:    532 loss= 0.540902 acc= 0.934989 cost_time= 0.328681s \n",
      "Epoch:    533 loss= 0.591926 acc= 0.923754 cost_time= 0.346339s \n",
      "Epoch:    534 loss= 0.439922 acc= 0.933270 cost_time= 0.326382s \n",
      "Epoch:    535 loss= 0.497310 acc= 0.932285 cost_time= 0.321156s \n",
      "Epoch:    536 loss= 0.634091 acc= 0.898669 cost_time= 0.308317s \n",
      "Epoch:    537 loss= 0.564548 acc= 0.933604 cost_time= 0.313701s \n",
      "Epoch:    538 loss= 0.486088 acc= 0.931426 cost_time= 0.315937s \n",
      "Epoch:    539 loss= 0.400846 acc= 0.943655 cost_time= 0.317860s \n",
      "Epoch:    540 loss= 0.849575 acc= 0.872209 cost_time= 0.332016s \n",
      "Epoch:    541 loss= 1.041190 acc= 0.908762 cost_time= 0.314923s \n",
      "Epoch:    542 loss= 0.613526 acc= 0.911909 cost_time= 0.313046s \n",
      "Epoch:    543 loss= 1.008918 acc= 0.842955 cost_time= 0.312230s \n",
      "Epoch:    544 loss= 0.444261 acc= 0.936804 cost_time= 0.313627s \n",
      "Epoch:    545 loss= 1.090840 acc= 0.909696 cost_time= 0.317642s \n",
      "Epoch:    546 loss= 0.377801 acc= 0.937807 cost_time= 0.315154s \n",
      "Epoch:    547 loss= 0.640284 acc= 0.910726 cost_time= 0.314056s \n",
      "Epoch:    548 loss= 0.734502 acc= 0.895307 cost_time= 0.310274s \n",
      "Epoch:    549 loss= 0.507737 acc= 0.928456 cost_time= 0.311966s \n",
      "Epoch:    550 loss= 1.315402 acc= 0.892144 cost_time= 0.316472s \n",
      "Epoch:    551 loss= 0.425240 acc= 0.941378 cost_time= 0.309225s \n",
      "Epoch:    552 loss= 0.554736 acc= 0.940784 cost_time= 0.314180s \n",
      "Epoch:    553 loss= 0.605059 acc= 0.878115 cost_time= 0.312087s \n",
      "Epoch:    554 loss= 0.555144 acc= 0.908786 cost_time= 0.320305s \n",
      "Epoch:    555 loss= 0.781397 acc= 0.917219 cost_time= 0.314037s \n",
      "Epoch:    556 loss= 0.639223 acc= 0.934626 cost_time= 0.307971s \n",
      "Epoch:    557 loss= 0.742837 acc= 0.901805 cost_time= 0.314258s \n",
      "Epoch:    558 loss= 3.325390 acc= 0.862771 cost_time= 0.315844s \n",
      "Epoch:    559 loss= 2.382140 acc= 0.825435 cost_time= 0.322803s \n",
      "Epoch:    560 loss= 0.533309 acc= 0.923388 cost_time= 0.332172s \n",
      "Epoch:    561 loss= 0.746214 acc= 0.890844 cost_time= 0.312126s \n",
      "Epoch:    562 loss= 0.483865 acc= 0.932199 cost_time= 0.310562s \n",
      "Epoch:    563 loss= 1.204358 acc= 0.829314 cost_time= 0.308971s \n",
      "Epoch:    564 loss= 0.647452 acc= 0.903808 cost_time= 0.318696s \n",
      "Epoch:    565 loss= 0.901712 acc= 0.889095 cost_time= 0.316108s \n",
      "Epoch:    566 loss= 0.496702 acc= 0.944353 cost_time= 0.316429s \n",
      "Epoch:    567 loss= 3.026779 acc= 0.864411 cost_time= 0.311140s \n",
      "Epoch:    568 loss= 0.676834 acc= 0.911493 cost_time= 0.313094s \n",
      "Epoch:    569 loss= 0.719086 acc= 0.901513 cost_time= 0.312279s \n",
      "Epoch:    570 loss= 0.626425 acc= 0.912096 cost_time= 0.312644s \n",
      "Epoch:    571 loss= 0.434550 acc= 0.937992 cost_time= 0.313524s \n",
      "Epoch:    572 loss= 0.539429 acc= 0.921241 cost_time= 0.311549s \n",
      "Epoch:    573 loss= 2.033438 acc= 0.905990 cost_time= 0.313576s \n",
      "Epoch:    574 loss= 0.445095 acc= 0.933159 cost_time= 0.316983s \n",
      "Epoch:    575 loss= 1.632611 acc= 0.905235 cost_time= 0.318571s \n",
      "Epoch:    576 loss= 0.585657 acc= 0.906694 cost_time= 0.311148s \n",
      "Epoch:    577 loss= 0.650240 acc= 0.884048 cost_time= 0.312409s \n",
      "Epoch:    578 loss= 0.834921 acc= 0.898159 cost_time= 0.315070s \n",
      "Epoch:    579 loss= 0.848903 acc= 0.892766 cost_time= 0.310986s \n",
      "Epoch:    580 loss= 0.657403 acc= 0.906749 cost_time= 0.313402s \n",
      "Epoch:    581 loss= 0.375604 acc= 0.930075 cost_time= 0.311647s \n",
      "Epoch:    582 loss= 0.465325 acc= 0.932774 cost_time= 0.316244s \n",
      "Epoch:    583 loss= 1.571161 acc= 0.848549 cost_time= 0.312842s \n",
      "Epoch:    584 loss= 0.476536 acc= 0.931566 cost_time= 0.310117s \n",
      "Epoch:    585 loss= 0.388086 acc= 0.943159 cost_time= 0.317709s \n",
      "Epoch:    586 loss= 0.625358 acc= 0.917315 cost_time= 0.312961s \n",
      "Epoch:    587 loss= 0.463353 acc= 0.913243 cost_time= 0.316952s \n",
      "Epoch:    588 loss= 1.033463 acc= 0.875676 cost_time= 0.305444s \n",
      "Epoch:    589 loss= 2.004063 acc= 0.859080 cost_time= 0.311908s \n",
      "Epoch:    590 loss= 0.507464 acc= 0.931535 cost_time= 0.318133s \n",
      "Epoch:    591 loss= 0.678135 acc= 0.892961 cost_time= 0.311840s \n",
      "Epoch:    592 loss= 0.599434 acc= 0.915074 cost_time= 0.314187s \n",
      "Epoch:    593 loss= 0.819244 acc= 0.902017 cost_time= 0.315457s \n",
      "Epoch:    594 loss= 0.433509 acc= 0.933792 cost_time= 0.316064s \n",
      "Epoch:    595 loss= 1.564830 acc= 0.880407 cost_time= 0.314402s \n",
      "Epoch:    596 loss= 0.682863 acc= 0.925609 cost_time= 0.312842s \n",
      "Epoch:    597 loss= 0.425266 acc= 0.944730 cost_time= 0.339217s \n",
      "Epoch:    598 loss= 1.007578 acc= 0.832488 cost_time= 0.337829s \n",
      "Epoch:    599 loss= 0.419186 acc= 0.942737 cost_time= 0.349361s \n",
      "Epoch:    600 loss= 0.537204 acc= 0.920538 cost_time= 0.337606s \n",
      "Epoch:    601 loss= 0.369880 acc= 0.945187 cost_time= 0.338949s \n",
      "Epoch:    602 loss= 0.535004 acc= 0.911633 cost_time= 0.348336s \n",
      "Epoch:    603 loss= 0.448162 acc= 0.928424 cost_time= 0.337200s \n",
      "Epoch:    604 loss= 0.543276 acc= 0.914752 cost_time= 0.334831s \n",
      "Epoch:    605 loss= 0.574509 acc= 0.922737 cost_time= 0.333106s \n",
      "Epoch:    606 loss= 0.763773 acc= 0.878198 cost_time= 0.323498s \n",
      "Epoch:    607 loss= 0.962111 acc= 0.899841 cost_time= 0.323328s \n",
      "Epoch:    608 loss= 1.234214 acc= 0.865075 cost_time= 0.331318s \n",
      "Epoch:    609 loss= 0.639044 acc= 0.916004 cost_time= 0.327090s \n",
      "Epoch:    610 loss= 0.611792 acc= 0.899028 cost_time= 0.321147s \n",
      "Epoch:    611 loss= 0.331169 acc= 0.942453 cost_time= 0.325845s \n",
      "Epoch:    612 loss= 0.853548 acc= 0.878862 cost_time= 0.318048s \n",
      "Epoch:    613 loss= 0.905409 acc= 0.873717 cost_time= 0.320530s \n",
      "Epoch:    614 loss= 1.644889 acc= 0.852472 cost_time= 0.320766s \n",
      "Epoch:    615 loss= 0.448619 acc= 0.938044 cost_time= 0.325006s \n",
      "Epoch:    616 loss= 0.386128 acc= 0.937016 cost_time= 0.344302s \n",
      "Epoch:    617 loss= 0.452276 acc= 0.946814 cost_time= 0.333818s \n",
      "Epoch:    618 loss= 2.919538 acc= 0.876455 cost_time= 0.324132s \n",
      "Epoch:    619 loss= 0.342825 acc= 0.946240 cost_time= 0.326587s \n",
      "Epoch:    620 loss= 0.780541 acc= 0.879171 cost_time= 0.338712s \n",
      "Epoch:    621 loss= 1.216458 acc= 0.837343 cost_time= 0.323457s \n",
      "Epoch:    622 loss= 0.466114 acc= 0.933456 cost_time= 0.327263s \n",
      "Epoch:    623 loss= 0.691210 acc= 0.886474 cost_time= 0.324603s \n",
      "Epoch:    624 loss= 0.577155 acc= 0.916734 cost_time= 0.323770s \n",
      "Epoch:    625 loss= 0.864349 acc= 0.925322 cost_time= 0.323558s \n",
      "Epoch:    626 loss= 0.566035 acc= 0.924322 cost_time= 0.323174s \n",
      "Epoch:    627 loss= 0.488390 acc= 0.932437 cost_time= 0.340654s \n",
      "Epoch:    628 loss= 1.654857 acc= 0.788505 cost_time= 0.319856s \n",
      "Epoch:    629 loss= 0.777589 acc= 0.880306 cost_time= 0.329328s \n",
      "Epoch:    630 loss= 0.516845 acc= 0.930768 cost_time= 0.325781s \n",
      "Epoch:    631 loss= 2.313945 acc= 0.845624 cost_time= 0.326600s \n",
      "Epoch:    632 loss= 0.529433 acc= 0.921731 cost_time= 0.324579s \n",
      "Epoch:    633 loss= 0.693301 acc= 0.907401 cost_time= 0.334127s \n",
      "Epoch:    634 loss= 0.529191 acc= 0.908701 cost_time= 0.326355s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    635 loss= 3.418358 acc= 0.822488 cost_time= 0.328801s \n",
      "Epoch:    636 loss= 0.567646 acc= 0.925311 cost_time= 0.327011s \n",
      "Epoch:    637 loss= 1.113852 acc= 0.884354 cost_time= 0.322716s \n",
      "Epoch:    638 loss= 0.438382 acc= 0.930388 cost_time= 0.323035s \n",
      "Epoch:    639 loss= 3.403479 acc= 0.787042 cost_time= 0.324431s \n",
      "Epoch:    640 loss= 0.583611 acc= 0.913576 cost_time= 0.325104s \n",
      "Epoch:    641 loss= 2.399956 acc= 0.873945 cost_time= 0.323270s \n",
      "Epoch:    642 loss= 0.986293 acc= 0.870280 cost_time= 0.319840s \n",
      "Epoch:    643 loss= 0.437636 acc= 0.941122 cost_time= 0.325385s \n",
      "Epoch:    644 loss= 0.677023 acc= 0.891568 cost_time= 0.320199s \n",
      "Epoch:    645 loss= 0.640502 acc= 0.921253 cost_time= 0.320931s \n",
      "Epoch:    646 loss= 1.739289 acc= 0.876143 cost_time= 0.329004s \n",
      "Epoch:    647 loss= 0.535809 acc= 0.936460 cost_time= 0.322855s \n",
      "Epoch:    648 loss= 0.684515 acc= 0.908201 cost_time= 0.322230s \n",
      "Epoch:    649 loss= 0.651166 acc= 0.904940 cost_time= 0.320046s \n",
      "Epoch:    650 loss= 0.684380 acc= 0.920713 cost_time= 0.326812s \n",
      "Epoch:    651 loss= 0.386446 acc= 0.942757 cost_time= 0.324311s \n",
      "Epoch:    652 loss= 0.427803 acc= 0.924874 cost_time= 0.323679s \n",
      "Epoch:    653 loss= 0.541204 acc= 0.909467 cost_time= 0.322568s \n",
      "Epoch:    654 loss= 0.800491 acc= 0.916067 cost_time= 0.323701s \n",
      "Epoch:    655 loss= 0.573412 acc= 0.920098 cost_time= 0.323932s \n",
      "Epoch:    656 loss= 0.654528 acc= 0.914268 cost_time= 0.324637s \n",
      "Epoch:    657 loss= 0.737569 acc= 0.902124 cost_time= 0.327090s \n",
      "Epoch:    658 loss= 1.529866 acc= 0.831509 cost_time= 0.340114s \n",
      "Epoch:    659 loss= 0.458516 acc= 0.922209 cost_time= 0.322702s \n",
      "Epoch:    660 loss= 0.516110 acc= 0.923402 cost_time= 0.323829s \n",
      "Epoch:    661 loss= 0.550886 acc= 0.919308 cost_time= 0.322045s \n",
      "Epoch:    662 loss= 0.409732 acc= 0.927073 cost_time= 0.324618s \n",
      "Epoch:    663 loss= 0.415523 acc= 0.935242 cost_time= 0.326578s \n",
      "Epoch:    664 loss= 0.521318 acc= 0.935883 cost_time= 0.342454s \n",
      "Epoch:    665 loss= 1.859000 acc= 0.889467 cost_time= 0.330323s \n",
      "Epoch:    666 loss= 0.812651 acc= 0.923456 cost_time= 0.324184s \n",
      "Epoch:    667 loss= 0.449139 acc= 0.939234 cost_time= 0.323814s \n",
      "Epoch:    668 loss= 0.374959 acc= 0.951867 cost_time= 0.319510s \n",
      "Epoch:    669 loss= 0.394133 acc= 0.950178 cost_time= 0.336596s \n",
      "Epoch:    670 loss= 1.565172 acc= 0.881525 cost_time= 0.312685s \n",
      "Epoch:    671 loss= 0.474079 acc= 0.924595 cost_time= 0.312115s \n",
      "Epoch:    672 loss= 0.527964 acc= 0.924347 cost_time= 0.319470s \n",
      "Epoch:    673 loss= 0.944161 acc= 0.859833 cost_time= 0.313822s \n",
      "Epoch:    674 loss= 0.748283 acc= 0.907716 cost_time= 0.315494s \n",
      "Epoch:    675 loss= 0.723403 acc= 0.881899 cost_time= 0.311014s \n",
      "Epoch:    676 loss= 0.907373 acc= 0.842403 cost_time= 0.314705s \n",
      "Epoch:    677 loss= 1.044620 acc= 0.842319 cost_time= 0.311956s \n",
      "Epoch:    678 loss= 1.090944 acc= 0.850926 cost_time= 0.310902s \n",
      "Epoch:    679 loss= 0.650653 acc= 0.912665 cost_time= 0.315597s \n",
      "Epoch:    680 loss= 1.461668 acc= 0.832741 cost_time= 0.310982s \n",
      "Epoch:    681 loss= 0.849010 acc= 0.888640 cost_time= 0.313475s \n",
      "Epoch:    682 loss= 0.498472 acc= 0.913728 cost_time= 0.315304s \n",
      "Epoch:    683 loss= 0.465263 acc= 0.938738 cost_time= 0.313978s \n",
      "Epoch:    684 loss= 0.510269 acc= 0.931031 cost_time= 0.313024s \n",
      "Epoch:    685 loss= 1.020505 acc= 0.901021 cost_time= 0.313601s \n",
      "Epoch:    686 loss= 0.489736 acc= 0.922895 cost_time= 0.317669s \n",
      "Epoch:    687 loss= 0.465624 acc= 0.943499 cost_time= 0.317007s \n",
      "Epoch:    688 loss= 0.397255 acc= 0.945773 cost_time= 0.315578s \n",
      "Epoch:    689 loss= 0.592731 acc= 0.909420 cost_time= 0.317994s \n",
      "Epoch:    690 loss= 0.532419 acc= 0.929673 cost_time= 0.309659s \n",
      "Epoch:    691 loss= 0.674704 acc= 0.913156 cost_time= 0.311936s \n",
      "Epoch:    692 loss= 0.619125 acc= 0.895739 cost_time= 0.315489s \n",
      "Epoch:    693 loss= 0.524484 acc= 0.933490 cost_time= 0.313148s \n",
      "Epoch:    694 loss= 0.410654 acc= 0.939863 cost_time= 0.313489s \n",
      "Epoch:    695 loss= 0.469895 acc= 0.944162 cost_time= 0.314707s \n",
      "Epoch:    696 loss= 0.469172 acc= 0.940041 cost_time= 0.329592s \n",
      "Epoch:    697 loss= 0.604900 acc= 0.900478 cost_time= 0.332441s \n",
      "Epoch:    698 loss= 0.542624 acc= 0.915872 cost_time= 0.312217s \n",
      "Epoch:    699 loss= 0.832249 acc= 0.862406 cost_time= 0.312421s \n",
      "Epoch:    700 loss= 0.760197 acc= 0.856181 cost_time= 0.306126s \n",
      "Epoch:    701 loss= 0.488117 acc= 0.936869 cost_time= 0.315431s \n",
      "Epoch:    702 loss= 0.681948 acc= 0.914707 cost_time= 0.309626s \n",
      "Epoch:    703 loss= 0.666370 acc= 0.906871 cost_time= 0.328566s \n",
      "Epoch:    704 loss= 0.569113 acc= 0.920043 cost_time= 0.315309s \n",
      "Epoch:    705 loss= 0.507752 acc= 0.929993 cost_time= 0.313174s \n",
      "Epoch:    706 loss= 0.530753 acc= 0.908546 cost_time= 0.331275s \n",
      "Epoch:    707 loss= 1.317036 acc= 0.875879 cost_time= 0.311729s \n",
      "Epoch:    708 loss= 0.522413 acc= 0.927853 cost_time= 0.317317s \n",
      "Epoch:    709 loss= 0.722005 acc= 0.907345 cost_time= 0.312475s \n",
      "Epoch:    710 loss= 2.446419 acc= 0.850494 cost_time= 0.316245s \n",
      "Epoch:    711 loss= 0.331557 acc= 0.942184 cost_time= 0.333934s \n",
      "Epoch:    712 loss= 1.885356 acc= 0.875943 cost_time= 0.321218s \n",
      "Epoch:    713 loss= 0.808492 acc= 0.840379 cost_time= 0.318692s \n",
      "Epoch:    714 loss= 0.363836 acc= 0.939051 cost_time= 0.312989s \n",
      "Epoch:    715 loss= 1.673762 acc= 0.892260 cost_time= 0.313053s \n",
      "Epoch:    716 loss= 0.314744 acc= 0.947740 cost_time= 0.332841s \n",
      "Epoch:    717 loss= 0.576092 acc= 0.922296 cost_time= 0.311747s \n",
      "Epoch:    718 loss= 0.735947 acc= 0.885272 cost_time= 0.311732s \n",
      "Epoch:    719 loss= 0.940694 acc= 0.905404 cost_time= 0.332419s \n",
      "Epoch:    720 loss= 0.550364 acc= 0.923802 cost_time= 0.312442s \n",
      "Epoch:    721 loss= 0.485272 acc= 0.925026 cost_time= 0.311117s \n",
      "Epoch:    722 loss= 0.583321 acc= 0.912308 cost_time= 0.311947s \n",
      "Epoch:    723 loss= 0.928852 acc= 0.854927 cost_time= 0.315251s \n",
      "Epoch:    724 loss= 0.349027 acc= 0.941555 cost_time= 0.315469s \n",
      "Epoch:    725 loss= 0.544894 acc= 0.921743 cost_time= 0.316372s \n",
      "Epoch:    726 loss= 1.466232 acc= 0.880778 cost_time= 0.314340s \n",
      "Epoch:    727 loss= 1.874745 acc= 0.858970 cost_time= 0.314727s \n",
      "Epoch:    728 loss= 0.711310 acc= 0.893176 cost_time= 0.312008s \n",
      "Epoch:    729 loss= 0.707770 acc= 0.890202 cost_time= 0.307929s \n",
      "Epoch:    730 loss= 0.371932 acc= 0.924580 cost_time= 0.313869s \n",
      "Epoch:    731 loss= 1.320486 acc= 0.800393 cost_time= 0.299312s \n",
      "Epoch:    732 loss= 3.572222 acc= 0.755342 cost_time= 0.309246s \n",
      "Epoch:    733 loss= 0.453017 acc= 0.920774 cost_time= 0.311083s \n",
      "Epoch:    734 loss= 0.648400 acc= 0.917489 cost_time= 0.309407s \n",
      "Epoch:    735 loss= 0.857460 acc= 0.867838 cost_time= 0.313610s \n",
      "Epoch:    736 loss= 0.721952 acc= 0.887159 cost_time= 0.313925s \n",
      "Epoch:    737 loss= 1.247087 acc= 0.895919 cost_time= 0.310748s \n",
      "Epoch:    738 loss= 0.478462 acc= 0.935295 cost_time= 0.312994s \n",
      "Epoch:    739 loss= 0.647014 acc= 0.915917 cost_time= 0.309913s \n",
      "Epoch:    740 loss= 0.391180 acc= 0.937368 cost_time= 0.316901s \n",
      "Epoch:    741 loss= 3.423571 acc= 0.767633 cost_time= 0.313286s \n",
      "Epoch:    742 loss= 0.572050 acc= 0.931803 cost_time= 0.312589s \n",
      "Epoch:    743 loss= 0.463059 acc= 0.922193 cost_time= 0.318864s \n",
      "Epoch:    744 loss= 0.520485 acc= 0.915906 cost_time= 0.321439s \n",
      "Epoch:    745 loss= 0.617073 acc= 0.894247 cost_time= 0.309711s \n",
      "Epoch:    746 loss= 0.878395 acc= 0.867229 cost_time= 0.312889s \n",
      "Epoch:    747 loss= 0.400089 acc= 0.943876 cost_time= 0.317624s \n",
      "Epoch:    748 loss= 0.423657 acc= 0.942888 cost_time= 0.318163s \n",
      "Epoch:    749 loss= 0.561486 acc= 0.915355 cost_time= 0.314315s \n",
      "Epoch:    750 loss= 1.829169 acc= 0.845938 cost_time= 0.312814s \n",
      "Epoch:    751 loss= 0.781611 acc= 0.890788 cost_time= 0.312768s \n",
      "Epoch:    752 loss= 0.500091 acc= 0.918581 cost_time= 0.310301s \n",
      "Epoch:    753 loss= 0.918633 acc= 0.903699 cost_time= 0.311196s \n",
      "Epoch:    754 loss= 1.470407 acc= 0.906258 cost_time= 0.310387s \n",
      "Epoch:    755 loss= 0.556218 acc= 0.906268 cost_time= 0.313425s \n",
      "Epoch:    756 loss= 0.399403 acc= 0.935067 cost_time= 0.312352s \n",
      "Epoch:    757 loss= 0.641852 acc= 0.902267 cost_time= 0.306910s \n",
      "Epoch:    758 loss= 1.494984 acc= 0.770575 cost_time= 0.301649s \n",
      "Epoch:    759 loss= 0.682089 acc= 0.887647 cost_time= 0.311897s \n",
      "Epoch:    760 loss= 1.224932 acc= 0.893361 cost_time= 0.312102s \n",
      "Epoch:    761 loss= 0.444049 acc= 0.933479 cost_time= 0.313509s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    762 loss= 0.439095 acc= 0.917759 cost_time= 0.313304s \n",
      "Epoch:    763 loss= 0.363961 acc= 0.932985 cost_time= 0.315171s \n",
      "Epoch:    764 loss= 0.399126 acc= 0.936637 cost_time= 0.311791s \n",
      "Epoch:    765 loss= 0.825977 acc= 0.841729 cost_time= 0.305604s \n",
      "Epoch:    766 loss= 0.790434 acc= 0.864137 cost_time= 0.308272s \n",
      "Epoch:    767 loss= 0.753010 acc= 0.889291 cost_time= 0.304032s \n",
      "Epoch:    768 loss= 0.453505 acc= 0.923445 cost_time= 0.311710s \n",
      "Epoch:    769 loss= 0.428339 acc= 0.934337 cost_time= 0.314677s \n",
      "Epoch:    770 loss= 0.398307 acc= 0.942254 cost_time= 0.311563s \n",
      "Epoch:    771 loss= 1.353033 acc= 0.867254 cost_time= 0.311290s \n",
      "Epoch:    772 loss= 0.671106 acc= 0.908021 cost_time= 0.306578s \n",
      "Epoch:    773 loss= 0.620161 acc= 0.892500 cost_time= 0.311486s \n",
      "Epoch:    774 loss= 0.575798 acc= 0.923940 cost_time= 0.312119s \n",
      "Epoch:    775 loss= 0.545362 acc= 0.928288 cost_time= 0.313143s \n",
      "Epoch:    776 loss= 0.581610 acc= 0.901925 cost_time= 0.316680s \n",
      "Epoch:    777 loss= 0.576648 acc= 0.901357 cost_time= 0.313587s \n",
      "Epoch:    778 loss= 2.194897 acc= 0.838971 cost_time= 0.327901s \n",
      "Epoch:    779 loss= 0.599722 acc= 0.912273 cost_time= 0.310799s \n",
      "Epoch:    780 loss= 0.702861 acc= 0.912461 cost_time= 0.312124s \n",
      "Epoch:    781 loss= 0.384477 acc= 0.947112 cost_time= 0.314067s \n",
      "Epoch:    782 loss= 0.962805 acc= 0.853550 cost_time= 0.307258s \n",
      "Epoch:    783 loss= 0.354331 acc= 0.935573 cost_time= 0.314509s \n",
      "Epoch:    784 loss= 0.596481 acc= 0.908740 cost_time= 0.312376s \n",
      "Epoch:    785 loss= 0.422019 acc= 0.931339 cost_time= 0.315707s \n",
      "Epoch:    786 loss= 0.498709 acc= 0.935163 cost_time= 0.326653s \n",
      "Epoch:    787 loss= 0.373468 acc= 0.925345 cost_time= 0.309128s \n",
      "Epoch:    788 loss= 1.269577 acc= 0.899056 cost_time= 0.317911s \n",
      "Epoch:    789 loss= 0.338346 acc= 0.938798 cost_time= 0.312256s \n",
      "Epoch:    790 loss= 0.428223 acc= 0.932366 cost_time= 0.320798s \n",
      "Epoch:    791 loss= 0.961792 acc= 0.896954 cost_time= 0.309399s \n",
      "Epoch:    792 loss= 1.427518 acc= 0.855271 cost_time= 0.314164s \n",
      "Epoch:    793 loss= 0.375515 acc= 0.946149 cost_time= 0.313167s \n",
      "Epoch:    794 loss= 0.410627 acc= 0.932392 cost_time= 0.311828s \n",
      "Epoch:    795 loss= 0.625766 acc= 0.904283 cost_time= 0.313019s \n",
      "Epoch:    796 loss= 0.580913 acc= 0.895132 cost_time= 0.328563s \n",
      "Epoch:    797 loss= 1.403053 acc= 0.885678 cost_time= 0.313629s \n",
      "Epoch:    798 loss= 0.734445 acc= 0.878586 cost_time= 0.314962s \n",
      "Epoch:    799 loss= 0.450499 acc= 0.945426 cost_time= 0.313255s \n",
      "Epoch:    800 loss= 0.545078 acc= 0.917244 cost_time= 0.310689s \n",
      "Epoch:    801 loss= 0.412321 acc= 0.938433 cost_time= 0.313966s \n",
      "Epoch:    802 loss= 0.424542 acc= 0.938180 cost_time= 0.316251s \n",
      "Epoch:    803 loss= 0.749425 acc= 0.890108 cost_time= 0.314381s \n",
      "Epoch:    804 loss= 0.457022 acc= 0.916537 cost_time= 0.314364s \n",
      "Epoch:    805 loss= 0.566166 acc= 0.916011 cost_time= 0.331452s \n",
      "Epoch:    806 loss= 0.503127 acc= 0.933818 cost_time= 0.314148s \n",
      "Epoch:    807 loss= 0.328524 acc= 0.947907 cost_time= 0.321024s \n",
      "Epoch:    808 loss= 3.024192 acc= 0.750881 cost_time= 0.309447s \n",
      "Epoch:    809 loss= 0.971182 acc= 0.858335 cost_time= 0.306142s \n",
      "Epoch:    810 loss= 0.615855 acc= 0.892382 cost_time= 0.319973s \n",
      "Epoch:    811 loss= 0.382013 acc= 0.944127 cost_time= 0.318264s \n",
      "Epoch:    812 loss= 0.646586 acc= 0.897175 cost_time= 0.313471s \n",
      "Epoch:    813 loss= 0.422857 acc= 0.914009 cost_time= 0.313781s \n",
      "Epoch:    814 loss= 1.212755 acc= 0.803862 cost_time= 0.306590s \n",
      "Epoch:    815 loss= 0.488056 acc= 0.925689 cost_time= 0.318547s \n",
      "Epoch:    816 loss= 1.141464 acc= 0.896235 cost_time= 0.317065s \n",
      "Epoch:    817 loss= 0.465444 acc= 0.937627 cost_time= 0.314311s \n",
      "Epoch:    818 loss= 0.399211 acc= 0.940970 cost_time= 0.318642s \n",
      "Epoch:    819 loss= 0.928510 acc= 0.900273 cost_time= 0.319493s \n",
      "Epoch:    820 loss= 1.027687 acc= 0.851667 cost_time= 0.312027s \n",
      "Epoch:    821 loss= 0.563557 acc= 0.922601 cost_time= 0.330879s \n",
      "Epoch:    822 loss= 0.458413 acc= 0.941109 cost_time= 0.312845s \n",
      "Epoch:    823 loss= 0.544378 acc= 0.919625 cost_time= 0.313387s \n",
      "Epoch:    824 loss= 0.502370 acc= 0.895545 cost_time= 0.312839s \n",
      "Epoch:    825 loss= 3.569257 acc= 0.831766 cost_time= 0.314953s \n",
      "Epoch:    826 loss= 0.918680 acc= 0.897405 cost_time= 0.313586s \n",
      "Epoch:    827 loss= 0.621347 acc= 0.901312 cost_time= 0.314909s \n",
      "Epoch:    828 loss= 0.587656 acc= 0.901883 cost_time= 0.311624s \n",
      "Epoch:    829 loss= 0.343564 acc= 0.940932 cost_time= 0.311832s \n",
      "Epoch:    830 loss= 0.817778 acc= 0.863693 cost_time= 0.311927s \n",
      "Epoch:    831 loss= 2.012832 acc= 0.871523 cost_time= 0.312763s \n",
      "Epoch:    832 loss= 0.461784 acc= 0.932946 cost_time= 0.337061s \n",
      "Epoch:    833 loss= 0.407337 acc= 0.926326 cost_time= 0.319139s \n",
      "Epoch:    834 loss= 0.537933 acc= 0.913483 cost_time= 0.313649s \n",
      "Epoch:    835 loss= 2.248648 acc= 0.875000 cost_time= 0.319532s \n",
      "Epoch:    836 loss= 0.445110 acc= 0.926831 cost_time= 0.317667s \n",
      "Epoch:    837 loss= 0.451901 acc= 0.927843 cost_time= 0.313331s \n",
      "Epoch:    838 loss= 0.536854 acc= 0.915888 cost_time= 0.311594s \n",
      "Epoch:    839 loss= 0.439502 acc= 0.924352 cost_time= 0.316525s \n",
      "Epoch:    840 loss= 0.940145 acc= 0.905412 cost_time= 0.321146s \n",
      "Epoch:    841 loss= 1.361301 acc= 0.797373 cost_time= 0.307287s \n",
      "Epoch:    842 loss= 0.574008 acc= 0.911614 cost_time= 0.315721s \n",
      "Epoch:    843 loss= 0.359226 acc= 0.925971 cost_time= 0.316061s \n",
      "Epoch:    844 loss= 0.346435 acc= 0.950747 cost_time= 0.316643s \n",
      "Epoch:    845 loss= 0.339678 acc= 0.941943 cost_time= 0.312689s \n",
      "Epoch:    846 loss= 0.583108 acc= 0.914447 cost_time= 0.311294s \n",
      "Epoch:    847 loss= 0.401301 acc= 0.922195 cost_time= 0.315462s \n",
      "Epoch:    848 loss= 0.591880 acc= 0.913696 cost_time= 0.313431s \n",
      "Epoch:    849 loss= 0.326580 acc= 0.943190 cost_time= 0.315639s \n",
      "Epoch:    850 loss= 0.541631 acc= 0.922536 cost_time= 0.318981s \n",
      "Epoch:    851 loss= 0.486133 acc= 0.927921 cost_time= 0.317397s \n",
      "Epoch:    852 loss= 1.499700 acc= 0.869483 cost_time= 0.305953s \n",
      "Epoch:    853 loss= 1.559398 acc= 0.867012 cost_time= 0.311071s \n",
      "Epoch:    854 loss= 0.577843 acc= 0.902480 cost_time= 0.310649s \n",
      "Epoch:    855 loss= 0.441479 acc= 0.925001 cost_time= 0.314627s \n",
      "Epoch:    856 loss= 0.380900 acc= 0.940242 cost_time= 0.315835s \n",
      "Epoch:    857 loss= 0.411946 acc= 0.922775 cost_time= 0.312427s \n",
      "Epoch:    858 loss= 0.457586 acc= 0.937468 cost_time= 0.329928s \n",
      "Epoch:    859 loss= 0.616775 acc= 0.868128 cost_time= 0.321611s \n",
      "Epoch:    860 loss= 0.392787 acc= 0.946096 cost_time= 0.334999s \n",
      "Epoch:    861 loss= 0.441853 acc= 0.928707 cost_time= 0.326109s \n",
      "Epoch:    862 loss= 0.458260 acc= 0.944046 cost_time= 0.315765s \n",
      "Epoch:    863 loss= 0.655997 acc= 0.884304 cost_time= 0.310442s \n",
      "Epoch:    864 loss= 0.584218 acc= 0.922103 cost_time= 0.313828s \n",
      "Epoch:    865 loss= 0.383260 acc= 0.928627 cost_time= 0.314868s \n",
      "Epoch:    866 loss= 2.205297 acc= 0.824062 cost_time= 0.313008s \n",
      "Epoch:    867 loss= 0.783809 acc= 0.865171 cost_time= 0.320895s \n",
      "Epoch:    868 loss= 0.682841 acc= 0.868839 cost_time= 0.328342s \n",
      "Epoch:    869 loss= 0.400429 acc= 0.927493 cost_time= 0.315599s \n",
      "Epoch:    870 loss= 0.998770 acc= 0.844794 cost_time= 0.315351s \n",
      "Epoch:    871 loss= 1.678553 acc= 0.860921 cost_time= 0.311864s \n",
      "Epoch:    872 loss= 0.440039 acc= 0.927565 cost_time= 0.313036s \n",
      "Epoch:    873 loss= 0.371086 acc= 0.948747 cost_time= 0.315197s \n",
      "Epoch:    874 loss= 0.344857 acc= 0.949488 cost_time= 0.320227s \n",
      "Epoch:    875 loss= 0.477633 acc= 0.920921 cost_time= 0.340843s \n",
      "Epoch:    876 loss= 0.492707 acc= 0.909565 cost_time= 0.346353s \n",
      "Epoch:    877 loss= 1.387656 acc= 0.825451 cost_time= 0.333489s \n",
      "Epoch:    878 loss= 0.642765 acc= 0.907991 cost_time= 0.323623s \n",
      "Epoch:    879 loss= 0.596030 acc= 0.898246 cost_time= 0.328625s \n",
      "Epoch:    880 loss= 0.853455 acc= 0.837239 cost_time= 0.329310s \n",
      "Epoch:    881 loss= 1.642827 acc= 0.835599 cost_time= 0.327123s \n",
      "Epoch:    882 loss= 0.414712 acc= 0.935521 cost_time= 0.329152s \n",
      "Epoch:    883 loss= 1.416460 acc= 0.870077 cost_time= 0.342133s \n",
      "Epoch:    884 loss= 0.601289 acc= 0.900372 cost_time= 0.325964s \n",
      "Epoch:    885 loss= 0.716285 acc= 0.916780 cost_time= 0.324554s \n",
      "Epoch:    886 loss= 1.891694 acc= 0.841166 cost_time= 0.325275s \n",
      "Epoch:    887 loss= 1.004338 acc= 0.881648 cost_time= 0.330176s \n",
      "Epoch:    888 loss= 0.968374 acc= 0.815223 cost_time= 0.332757s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    889 loss= 0.777718 acc= 0.906265 cost_time= 0.308703s \n",
      "Epoch:    890 loss= 0.428737 acc= 0.932996 cost_time= 0.318206s \n",
      "Epoch:    891 loss= 0.476347 acc= 0.929876 cost_time= 0.315406s \n",
      "Epoch:    892 loss= 1.464656 acc= 0.876491 cost_time= 0.309883s \n",
      "Epoch:    893 loss= 0.446885 acc= 0.923892 cost_time= 0.312603s \n",
      "Epoch:    894 loss= 0.730698 acc= 0.874382 cost_time= 0.312415s \n",
      "Epoch:    895 loss= 0.827833 acc= 0.907567 cost_time= 0.315597s \n",
      "Epoch:    896 loss= 2.715756 acc= 0.816229 cost_time= 0.310653s \n",
      "Epoch:    897 loss= 0.452458 acc= 0.925124 cost_time= 0.313796s \n",
      "Epoch:    898 loss= 0.490925 acc= 0.902558 cost_time= 0.312365s \n",
      "Epoch:    899 loss= 1.828356 acc= 0.881143 cost_time= 0.319195s \n",
      "Epoch:    900 loss= 0.679270 acc= 0.901800 cost_time= 0.313342s \n",
      "Epoch:    901 loss= 0.492119 acc= 0.937609 cost_time= 0.312699s \n",
      "Epoch:    902 loss= 0.656299 acc= 0.878725 cost_time= 0.311913s \n",
      "Epoch:    903 loss= 0.917139 acc= 0.877809 cost_time= 0.315894s \n",
      "Epoch:    904 loss= 0.512494 acc= 0.931020 cost_time= 0.311182s \n",
      "Epoch:    905 loss= 2.757558 acc= 0.841460 cost_time= 0.314314s \n",
      "Epoch:    906 loss= 0.527657 acc= 0.920335 cost_time= 0.330764s \n",
      "Epoch:    907 loss= 0.423465 acc= 0.925380 cost_time= 0.329828s \n",
      "Epoch:    908 loss= 0.407673 acc= 0.933669 cost_time= 0.319780s \n",
      "Epoch:    909 loss= 1.099091 acc= 0.874887 cost_time= 0.311844s \n",
      "Epoch:    910 loss= 0.713450 acc= 0.885798 cost_time= 0.310317s \n",
      "Epoch:    911 loss= 0.673432 acc= 0.890106 cost_time= 0.311693s \n",
      "Epoch:    912 loss= 1.902986 acc= 0.776192 cost_time= 0.309299s \n",
      "Epoch:    913 loss= 0.785592 acc= 0.862352 cost_time= 0.312910s \n",
      "Epoch:    914 loss= 0.748818 acc= 0.879532 cost_time= 0.309398s \n",
      "Epoch:    915 loss= 0.741370 acc= 0.874131 cost_time= 0.318886s \n",
      "Epoch:    916 loss= 1.334598 acc= 0.872131 cost_time= 0.314966s \n",
      "Epoch:    917 loss= 0.801088 acc= 0.855468 cost_time= 0.305721s \n",
      "Epoch:    918 loss= 0.450305 acc= 0.937408 cost_time= 0.318613s \n",
      "Epoch:    919 loss= 0.549562 acc= 0.921058 cost_time= 0.316093s \n",
      "Epoch:    920 loss= 1.810358 acc= 0.827002 cost_time= 0.328450s \n",
      "Epoch:    921 loss= 0.693545 acc= 0.871336 cost_time= 0.316646s \n",
      "Epoch:    922 loss= 0.400337 acc= 0.943002 cost_time= 0.312626s \n",
      "Epoch:    923 loss= 0.695287 acc= 0.890318 cost_time= 0.317462s \n",
      "Epoch:    924 loss= 0.420514 acc= 0.917610 cost_time= 0.315709s \n",
      "Epoch:    925 loss= 1.648616 acc= 0.876978 cost_time= 0.318664s \n",
      "Epoch:    926 loss= 0.326560 acc= 0.931605 cost_time= 0.316989s \n",
      "Epoch:    927 loss= 0.563434 acc= 0.905137 cost_time= 0.314881s \n",
      "Epoch:    928 loss= 0.368566 acc= 0.941311 cost_time= 0.316908s \n",
      "Epoch:    929 loss= 0.449762 acc= 0.932191 cost_time= 0.313082s \n",
      "Epoch:    930 loss= 0.389520 acc= 0.935185 cost_time= 0.316059s \n",
      "Epoch:    931 loss= 1.124014 acc= 0.863047 cost_time= 0.330431s \n",
      "Epoch:    932 loss= 0.477331 acc= 0.929474 cost_time= 0.309279s \n",
      "Epoch:    933 loss= 0.533030 acc= 0.902108 cost_time= 0.312330s \n",
      "Epoch:    934 loss= 0.447283 acc= 0.919642 cost_time= 0.313807s \n",
      "Epoch:    935 loss= 1.237346 acc= 0.891366 cost_time= 0.314941s \n",
      "Epoch:    936 loss= 1.192077 acc= 0.796210 cost_time= 0.309807s \n",
      "Epoch:    937 loss= 1.302644 acc= 0.891524 cost_time= 0.315837s \n",
      "Epoch:    938 loss= 0.306464 acc= 0.933138 cost_time= 0.315631s \n",
      "Epoch:    939 loss= 0.394213 acc= 0.940270 cost_time= 0.314417s \n",
      "Epoch:    940 loss= 0.607821 acc= 0.900063 cost_time= 0.308632s \n",
      "Epoch:    941 loss= 0.451640 acc= 0.911367 cost_time= 0.313954s \n",
      "Epoch:    942 loss= 0.544952 acc= 0.895294 cost_time= 0.308464s \n",
      "Epoch:    943 loss= 0.386852 acc= 0.937872 cost_time= 0.314453s \n",
      "Epoch:    944 loss= 0.969629 acc= 0.890686 cost_time= 0.314072s \n",
      "Epoch:    945 loss= 0.800286 acc= 0.884744 cost_time= 0.313219s \n",
      "Epoch:    946 loss= 0.298748 acc= 0.949783 cost_time= 0.314960s \n",
      "Epoch:    947 loss= 0.644746 acc= 0.899655 cost_time= 0.310022s \n",
      "Epoch:    948 loss= 0.554582 acc= 0.921542 cost_time= 0.320239s \n",
      "Epoch:    949 loss= 0.305400 acc= 0.932518 cost_time= 0.316938s \n",
      "Epoch:    950 loss= 0.418145 acc= 0.928847 cost_time= 0.310541s \n",
      "Epoch:    951 loss= 0.865796 acc= 0.905900 cost_time= 0.316796s \n",
      "Epoch:    952 loss= 0.548714 acc= 0.895580 cost_time= 0.333683s \n",
      "Epoch:    953 loss= 0.444597 acc= 0.936615 cost_time= 0.315080s \n",
      "Epoch:    954 loss= 0.531693 acc= 0.925882 cost_time= 0.315114s \n",
      "Epoch:    955 loss= 0.709725 acc= 0.909060 cost_time= 0.330556s \n",
      "Epoch:    956 loss= 0.519372 acc= 0.885983 cost_time= 0.317008s \n",
      "Epoch:    957 loss= 0.509658 acc= 0.938835 cost_time= 0.319665s \n",
      "Epoch:    958 loss= 1.217019 acc= 0.881858 cost_time= 0.313041s \n",
      "Epoch:    959 loss= 0.746570 acc= 0.846259 cost_time= 0.308656s \n",
      "Epoch:    960 loss= 1.621822 acc= 0.853570 cost_time= 0.315405s \n",
      "Epoch:    961 loss= 0.517484 acc= 0.920138 cost_time= 0.309573s \n",
      "Epoch:    962 loss= 0.487930 acc= 0.919394 cost_time= 0.318930s \n",
      "Epoch:    963 loss= 0.585839 acc= 0.897668 cost_time= 0.315871s \n",
      "Epoch:    964 loss= 0.377371 acc= 0.934798 cost_time= 0.321007s \n",
      "Epoch:    965 loss= 0.358212 acc= 0.928070 cost_time= 0.332582s \n",
      "Epoch:    966 loss= 0.314146 acc= 0.937925 cost_time= 0.315624s \n",
      "Epoch:    967 loss= 1.691090 acc= 0.812642 cost_time= 0.310618s \n",
      "Epoch:    968 loss= 0.551255 acc= 0.919446 cost_time= 0.314757s \n",
      "Epoch:    969 loss= 1.548948 acc= 0.872026 cost_time= 0.305468s \n",
      "Epoch:    970 loss= 2.759530 acc= 0.802610 cost_time= 0.310861s \n",
      "Epoch:    971 loss= 1.656066 acc= 0.812839 cost_time= 0.310119s \n",
      "Epoch:    972 loss= 0.507931 acc= 0.925828 cost_time= 0.317216s \n",
      "Epoch:    973 loss= 0.330286 acc= 0.937787 cost_time= 0.315173s \n",
      "Epoch:    974 loss= 0.357991 acc= 0.926402 cost_time= 0.316940s \n",
      "Epoch:    975 loss= 0.481147 acc= 0.934261 cost_time= 0.314122s \n",
      "Epoch:    976 loss= 0.363333 acc= 0.939204 cost_time= 0.310661s \n",
      "Epoch:    977 loss= 0.839753 acc= 0.920865 cost_time= 0.331347s \n",
      "Epoch:    978 loss= 0.646357 acc= 0.886099 cost_time= 0.316804s \n",
      "Epoch:    979 loss= 0.764616 acc= 0.898265 cost_time= 0.318122s \n",
      "Epoch:    980 loss= 0.654095 acc= 0.903895 cost_time= 0.311280s \n",
      "Epoch:    981 loss= 1.056002 acc= 0.805148 cost_time= 0.307633s \n",
      "Epoch:    982 loss= 0.663693 acc= 0.884613 cost_time= 0.314583s \n",
      "Epoch:    983 loss= 0.727697 acc= 0.872546 cost_time= 0.312730s \n",
      "Epoch:    984 loss= 0.520130 acc= 0.927096 cost_time= 0.316113s \n",
      "Epoch:    985 loss= 0.396162 acc= 0.932817 cost_time= 0.313549s \n",
      "Epoch:    986 loss= 0.782386 acc= 0.830273 cost_time= 0.308254s \n",
      "Epoch:    987 loss= 0.324907 acc= 0.935835 cost_time= 0.318512s \n",
      "Epoch:    988 loss= 0.581707 acc= 0.930905 cost_time= 0.317906s \n",
      "Epoch:    989 loss= 1.047772 acc= 0.868168 cost_time= 0.313536s \n",
      "Epoch:    990 loss= 0.526426 acc= 0.913088 cost_time= 0.317823s \n",
      "Epoch:    991 loss= 0.723075 acc= 0.866444 cost_time= 0.310574s \n",
      "Epoch:    992 loss= 0.886874 acc= 0.858277 cost_time= 0.304233s \n",
      "Epoch:    993 loss= 0.368705 acc= 0.941624 cost_time= 0.315990s \n",
      "Epoch:    994 loss= 0.417671 acc= 0.938758 cost_time= 0.313493s \n",
      "Epoch:    995 loss= 0.470742 acc= 0.922354 cost_time= 0.316686s \n",
      "Epoch:    996 loss= 0.675641 acc= 0.892023 cost_time= 0.317556s \n",
      "Epoch:    997 loss= 0.621441 acc= 0.873875 cost_time= 0.326141s \n",
      "Epoch:    998 loss= 0.474719 acc= 0.935790 cost_time= 0.315744s \n",
      "Epoch:    999 loss= 0.610339 acc= 0.884139 cost_time= 0.313597s \n",
      "Epoch:   1000 loss= 0.491854 acc= 0.925834 cost_time= 0.313827s \n",
      "Epoch:   1001 loss= 0.645217 acc= 0.915753 cost_time= 0.310051s \n",
      "Epoch:   1002 loss= 0.685159 acc= 0.863547 cost_time= 0.312148s \n",
      "Epoch:   1003 loss= 0.608406 acc= 0.901856 cost_time= 0.318940s \n",
      "Epoch:   1004 loss= 0.437002 acc= 0.906073 cost_time= 0.311728s \n",
      "Epoch:   1005 loss= 1.903365 acc= 0.814899 cost_time= 0.309980s \n",
      "Epoch:   1006 loss= 0.754158 acc= 0.892100 cost_time= 0.326321s \n",
      "Epoch:   1007 loss= 0.451227 acc= 0.927050 cost_time= 0.315902s \n",
      "Epoch:   1008 loss= 0.644653 acc= 0.901016 cost_time= 0.311955s \n",
      "Epoch:   1009 loss= 0.290711 acc= 0.940437 cost_time= 0.315508s \n",
      "Epoch:   1010 loss= 0.870021 acc= 0.907746 cost_time= 0.313193s \n",
      "Epoch:   1011 loss= 1.475984 acc= 0.849332 cost_time= 0.319638s \n",
      "Epoch:   1012 loss= 0.721157 acc= 0.861572 cost_time= 0.317377s \n",
      "Epoch:   1013 loss= 0.578936 acc= 0.874930 cost_time= 0.312541s \n",
      "Epoch:   1014 loss= 0.753709 acc= 0.870757 cost_time= 0.317559s \n",
      "Epoch:   1015 loss= 0.347092 acc= 0.943851 cost_time= 0.315819s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1016 loss= 0.741678 acc= 0.865431 cost_time= 0.309530s \n",
      "Epoch:   1017 loss= 0.447149 acc= 0.925973 cost_time= 0.317708s \n",
      "Epoch:   1018 loss= 1.554861 acc= 0.911935 cost_time= 0.317461s \n",
      "Epoch:   1019 loss= 0.514910 acc= 0.902637 cost_time= 0.313387s \n",
      "Epoch:   1020 loss= 0.389001 acc= 0.920628 cost_time= 0.321985s \n",
      "Epoch:   1021 loss= 0.416093 acc= 0.933675 cost_time= 0.330817s \n",
      "Epoch:   1022 loss= 1.653154 acc= 0.879352 cost_time= 0.341419s \n",
      "Epoch:   1023 loss= 0.343289 acc= 0.942447 cost_time= 0.334842s \n",
      "Epoch:   1024 loss= 0.509372 acc= 0.922158 cost_time= 0.342720s \n",
      "Epoch:   1025 loss= 0.662827 acc= 0.886937 cost_time= 0.354825s \n",
      "Epoch:   1026 loss= 0.531321 acc= 0.915424 cost_time= 0.316281s \n",
      "Epoch:   1027 loss= 0.420269 acc= 0.933765 cost_time= 0.314874s \n",
      "Epoch:   1028 loss= 0.646024 acc= 0.893591 cost_time= 0.314716s \n",
      "Epoch:   1029 loss= 1.278349 acc= 0.866172 cost_time= 0.335022s \n",
      "Epoch:   1030 loss= 0.375600 acc= 0.923562 cost_time= 0.313472s \n",
      "Epoch:   1031 loss= 0.434727 acc= 0.933587 cost_time= 0.316460s \n",
      "Epoch:   1032 loss= 0.580499 acc= 0.896700 cost_time= 0.313738s \n",
      "Epoch:   1033 loss= 0.618797 acc= 0.907257 cost_time= 0.319285s \n",
      "Epoch:   1034 loss= 0.642710 acc= 0.905913 cost_time= 0.333377s \n",
      "Epoch:   1035 loss= 0.565417 acc= 0.916024 cost_time= 0.332172s \n",
      "Epoch:   1036 loss= 0.668774 acc= 0.871388 cost_time= 0.318510s \n",
      "Epoch:   1037 loss= 0.466418 acc= 0.924974 cost_time= 0.316574s \n",
      "Epoch:   1038 loss= 0.714670 acc= 0.857854 cost_time= 0.314622s \n",
      "Epoch:   1039 loss= 0.323695 acc= 0.937806 cost_time= 0.317803s \n",
      "Epoch:   1040 loss= 0.422599 acc= 0.934447 cost_time= 0.318708s \n",
      "Epoch:   1041 loss= 0.621145 acc= 0.891661 cost_time= 0.310562s \n",
      "Epoch:   1042 loss= 0.485654 acc= 0.891044 cost_time= 0.312511s \n",
      "Epoch:   1043 loss= 0.913769 acc= 0.882445 cost_time= 0.313180s \n",
      "Epoch:   1044 loss= 0.455158 acc= 0.932717 cost_time= 0.317114s \n",
      "Epoch:   1045 loss= 0.348278 acc= 0.929210 cost_time= 0.311800s \n",
      "Epoch:   1046 loss= 0.513672 acc= 0.934277 cost_time= 0.314603s \n",
      "Epoch:   1047 loss= 0.381530 acc= 0.938234 cost_time= 0.320655s \n",
      "Epoch:   1048 loss= 0.480733 acc= 0.926407 cost_time= 0.315037s \n",
      "Epoch:   1049 loss= 0.469967 acc= 0.929275 cost_time= 0.314425s \n",
      "Epoch:   1050 loss= 1.313915 acc= 0.894030 cost_time= 0.330852s \n",
      "Epoch:   1051 loss= 0.477676 acc= 0.928803 cost_time= 0.324096s \n",
      "Epoch:   1052 loss= 0.946645 acc= 0.875160 cost_time= 0.312577s \n",
      "Epoch:   1053 loss= 0.960408 acc= 0.869651 cost_time= 0.315014s \n",
      "Epoch:   1054 loss= 0.558817 acc= 0.915186 cost_time= 0.312382s \n",
      "Epoch:   1055 loss= 0.404921 acc= 0.923378 cost_time= 0.319299s \n",
      "Epoch:   1056 loss= 0.284429 acc= 0.949684 cost_time= 0.318716s \n",
      "Epoch:   1057 loss= 0.903014 acc= 0.840312 cost_time= 0.313112s \n",
      "Epoch:   1058 loss= 1.693391 acc= 0.823419 cost_time= 0.314264s \n",
      "Epoch:   1059 loss= 0.830380 acc= 0.902615 cost_time= 0.318717s \n",
      "Epoch:   1060 loss= 0.709240 acc= 0.857532 cost_time= 0.313012s \n",
      "Epoch:   1061 loss= 0.662180 acc= 0.914988 cost_time= 0.314553s \n",
      "Epoch:   1062 loss= 1.249126 acc= 0.796646 cost_time= 0.325931s \n",
      "Epoch:   1063 loss= 0.627478 acc= 0.875213 cost_time= 0.315267s \n",
      "Epoch:   1064 loss= 0.430806 acc= 0.918869 cost_time= 0.314800s \n",
      "Epoch:   1065 loss= 0.500280 acc= 0.914477 cost_time= 0.310763s \n",
      "Epoch:   1066 loss= 0.373385 acc= 0.932781 cost_time= 0.321124s \n",
      "Epoch:   1067 loss= 0.967312 acc= 0.855707 cost_time= 0.313175s \n",
      "Epoch:   1068 loss= 0.318135 acc= 0.933211 cost_time= 0.320453s \n",
      "Epoch:   1069 loss= 0.653353 acc= 0.879659 cost_time= 0.310719s \n",
      "Epoch:   1070 loss= 0.592624 acc= 0.898607 cost_time= 0.316655s \n",
      "Epoch:   1071 loss= 0.957202 acc= 0.836175 cost_time= 0.308089s \n",
      "Epoch:   1072 loss= 0.307079 acc= 0.941571 cost_time= 0.319904s \n",
      "Epoch:   1073 loss= 0.341064 acc= 0.930345 cost_time= 0.312790s \n",
      "Epoch:   1074 loss= 0.736877 acc= 0.852337 cost_time= 0.314478s \n",
      "Epoch:   1075 loss= 0.654300 acc= 0.914286 cost_time= 0.313771s \n",
      "Epoch:   1076 loss= 0.375609 acc= 0.921428 cost_time= 0.312230s \n",
      "Epoch:   1077 loss= 1.332081 acc= 0.846179 cost_time= 0.317765s \n",
      "Epoch:   1078 loss= 0.582568 acc= 0.894392 cost_time= 0.314600s \n",
      "Epoch:   1079 loss= 0.331079 acc= 0.941953 cost_time= 0.324024s \n",
      "Epoch:   1080 loss= 0.702803 acc= 0.867712 cost_time= 0.327238s \n",
      "Epoch:   1081 loss= 1.373903 acc= 0.889363 cost_time= 0.317574s \n",
      "Epoch:   1082 loss= 0.546768 acc= 0.912242 cost_time= 0.311848s \n",
      "Epoch:   1083 loss= 1.146008 acc= 0.852319 cost_time= 0.311893s \n",
      "Epoch:   1084 loss= 0.512326 acc= 0.919206 cost_time= 0.318018s \n",
      "Epoch:   1085 loss= 1.043532 acc= 0.896763 cost_time= 0.312778s \n",
      "Epoch:   1086 loss= 0.468470 acc= 0.919441 cost_time= 0.314125s \n",
      "Epoch:   1087 loss= 0.756556 acc= 0.919563 cost_time= 0.316416s \n",
      "Epoch:   1088 loss= 0.303239 acc= 0.935411 cost_time= 0.316161s \n",
      "Epoch:   1089 loss= 0.396992 acc= 0.916008 cost_time= 0.314736s \n",
      "Epoch:   1090 loss= 0.506709 acc= 0.906162 cost_time= 0.309691s \n",
      "Epoch:   1091 loss= 0.691233 acc= 0.918456 cost_time= 0.315381s \n",
      "Epoch:   1092 loss= 0.389136 acc= 0.921870 cost_time= 0.313604s \n",
      "Epoch:   1093 loss= 0.886895 acc= 0.884016 cost_time= 0.309094s \n",
      "Epoch:   1094 loss= 0.436262 acc= 0.928764 cost_time= 0.317734s \n",
      "Epoch:   1095 loss= 0.535676 acc= 0.915041 cost_time= 0.312842s \n",
      "Epoch:   1096 loss= 0.754210 acc= 0.888502 cost_time= 0.312627s \n",
      "Epoch:   1097 loss= 0.861935 acc= 0.850563 cost_time= 0.316184s \n",
      "Epoch:   1098 loss= 0.346744 acc= 0.940490 cost_time= 0.325196s \n",
      "Epoch:   1099 loss= 0.381276 acc= 0.921934 cost_time= 0.315408s \n",
      "Epoch:   1100 loss= 0.455862 acc= 0.918104 cost_time= 0.313544s \n",
      "Epoch:   1101 loss= 1.652658 acc= 0.863281 cost_time= 0.321362s \n",
      "Epoch:   1102 loss= 0.452725 acc= 0.930069 cost_time= 0.311674s \n",
      "Epoch:   1103 loss= 0.400590 acc= 0.929790 cost_time= 0.317375s \n",
      "Epoch:   1104 loss= 0.403483 acc= 0.910235 cost_time= 0.323973s \n",
      "Epoch:   1105 loss= 0.436647 acc= 0.918691 cost_time= 0.318540s \n",
      "Epoch:   1106 loss= 0.451294 acc= 0.934663 cost_time= 0.318094s \n",
      "Epoch:   1107 loss= 0.552135 acc= 0.909874 cost_time= 0.315001s \n",
      "Epoch:   1108 loss= 0.450127 acc= 0.931297 cost_time= 0.319207s \n",
      "Epoch:   1109 loss= 0.474400 acc= 0.905299 cost_time= 0.316395s \n",
      "Epoch:   1110 loss= 0.708866 acc= 0.892659 cost_time= 0.309505s \n",
      "Epoch:   1111 loss= 0.350924 acc= 0.937707 cost_time= 0.318992s \n",
      "Epoch:   1112 loss= 0.824151 acc= 0.842824 cost_time= 0.313738s \n",
      "Epoch:   1113 loss= 0.374316 acc= 0.933600 cost_time= 0.314459s \n",
      "Epoch:   1114 loss= 0.509589 acc= 0.929741 cost_time= 0.313231s \n",
      "Epoch:   1115 loss= 1.088675 acc= 0.882597 cost_time= 0.311996s \n",
      "Epoch:   1116 loss= 0.593058 acc= 0.903082 cost_time= 0.315558s \n",
      "Epoch:   1117 loss= 0.476506 acc= 0.935137 cost_time= 0.333292s \n",
      "Epoch:   1118 loss= 0.583015 acc= 0.918628 cost_time= 0.315304s \n",
      "Epoch:   1119 loss= 0.421104 acc= 0.923865 cost_time= 0.315148s \n",
      "Epoch:   1120 loss= 0.302760 acc= 0.946066 cost_time= 0.334301s \n",
      "Epoch:   1121 loss= 1.076830 acc= 0.908830 cost_time= 0.315413s \n",
      "Epoch:   1122 loss= 0.457879 acc= 0.926738 cost_time= 0.314721s \n",
      "Epoch:   1123 loss= 0.637381 acc= 0.901844 cost_time= 0.316007s \n",
      "Epoch:   1124 loss= 0.427587 acc= 0.930652 cost_time= 0.312587s \n",
      "Epoch:   1125 loss= 0.389747 acc= 0.935114 cost_time= 0.311955s \n",
      "Epoch:   1126 loss= 0.392821 acc= 0.931047 cost_time= 0.312760s \n",
      "Epoch:   1127 loss= 0.688838 acc= 0.886549 cost_time= 0.313430s \n",
      "Epoch:   1128 loss= 1.999103 acc= 0.829048 cost_time= 0.316138s \n",
      "Epoch:   1129 loss= 1.964180 acc= 0.847703 cost_time= 0.314646s \n",
      "Epoch:   1130 loss= 0.385934 acc= 0.929242 cost_time= 0.310970s \n",
      "Epoch:   1131 loss= 0.512858 acc= 0.900269 cost_time= 0.311618s \n",
      "Epoch:   1132 loss= 0.390532 acc= 0.930013 cost_time= 0.313512s \n",
      "Epoch:   1133 loss= 0.626734 acc= 0.887666 cost_time= 0.308583s \n",
      "Epoch:   1134 loss= 0.794928 acc= 0.856117 cost_time= 0.309847s \n",
      "Epoch:   1135 loss= 0.395858 acc= 0.928999 cost_time= 0.309834s \n",
      "Epoch:   1136 loss= 0.425793 acc= 0.943355 cost_time= 0.314453s \n",
      "Epoch:   1137 loss= 0.408365 acc= 0.934559 cost_time= 0.333323s \n",
      "Epoch:   1138 loss= 1.398825 acc= 0.834474 cost_time= 0.310553s \n",
      "Epoch:   1139 loss= 0.261739 acc= 0.934986 cost_time= 0.316109s \n",
      "Epoch:   1140 loss= 1.039398 acc= 0.892601 cost_time= 0.315556s \n",
      "Epoch:   1141 loss= 0.339579 acc= 0.945432 cost_time= 0.315862s \n",
      "Epoch:   1142 loss= 0.970845 acc= 0.903536 cost_time= 0.313468s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1143 loss= 0.694902 acc= 0.923941 cost_time= 0.314202s \n",
      "Epoch:   1144 loss= 0.511227 acc= 0.912119 cost_time= 0.314307s \n",
      "Epoch:   1145 loss= 0.479306 acc= 0.917673 cost_time= 0.316084s \n",
      "Epoch:   1146 loss= 0.512837 acc= 0.916509 cost_time= 0.315766s \n",
      "Epoch:   1147 loss= 0.481934 acc= 0.916035 cost_time= 0.317492s \n",
      "Epoch:   1148 loss= 0.384690 acc= 0.922426 cost_time= 0.313536s \n",
      "Epoch:   1149 loss= 0.765773 acc= 0.854145 cost_time= 0.313199s \n",
      "Epoch:   1150 loss= 0.349428 acc= 0.949478 cost_time= 0.326962s \n",
      "Epoch:   1151 loss= 0.470970 acc= 0.921198 cost_time= 0.316776s \n",
      "Epoch:   1152 loss= 0.972174 acc= 0.830253 cost_time= 0.311930s \n",
      "Epoch:   1153 loss= 0.651774 acc= 0.872474 cost_time= 0.311943s \n",
      "Epoch:   1154 loss= 0.755281 acc= 0.846090 cost_time= 0.306494s \n",
      "Epoch:   1155 loss= 0.453133 acc= 0.932783 cost_time= 0.329066s \n",
      "Epoch:   1156 loss= 0.457030 acc= 0.915065 cost_time= 0.313860s \n",
      "Epoch:   1157 loss= 1.164587 acc= 0.877078 cost_time= 0.311793s \n",
      "Epoch:   1158 loss= 0.562289 acc= 0.902277 cost_time= 0.308755s \n",
      "Epoch:   1159 loss= 0.316938 acc= 0.928786 cost_time= 0.317241s \n",
      "Epoch:   1160 loss= 0.468679 acc= 0.931708 cost_time= 0.316334s \n",
      "Epoch:   1161 loss= 0.412873 acc= 0.932585 cost_time= 0.319863s \n",
      "Epoch:   1162 loss= 0.384781 acc= 0.939929 cost_time= 0.315541s \n",
      "Epoch:   1163 loss= 0.347526 acc= 0.937336 cost_time= 0.315154s \n",
      "Epoch:   1164 loss= 0.681157 acc= 0.866297 cost_time= 0.304677s \n",
      "Epoch:   1165 loss= 0.399522 acc= 0.946722 cost_time= 0.311308s \n",
      "Epoch:   1166 loss= 0.382767 acc= 0.940485 cost_time= 0.320650s \n",
      "Epoch:   1167 loss= 0.647977 acc= 0.877213 cost_time= 0.311651s \n",
      "Epoch:   1168 loss= 0.451886 acc= 0.914448 cost_time= 0.311764s \n",
      "Epoch:   1169 loss= 0.600014 acc= 0.897435 cost_time= 0.317842s \n",
      "Epoch:   1170 loss= 0.407828 acc= 0.935711 cost_time= 0.320114s \n",
      "Epoch:   1171 loss= 0.693592 acc= 0.911490 cost_time= 0.314611s \n",
      "Epoch:   1172 loss= 0.429701 acc= 0.926740 cost_time= 0.318404s \n",
      "Epoch:   1173 loss= 1.431121 acc= 0.875924 cost_time= 0.316932s \n",
      "Epoch:   1174 loss= 1.257028 acc= 0.829176 cost_time= 0.313506s \n",
      "Epoch:   1175 loss= 0.428176 acc= 0.914362 cost_time= 0.318285s \n",
      "Epoch:   1176 loss= 0.339884 acc= 0.945382 cost_time= 0.317741s \n",
      "Epoch:   1177 loss= 0.416082 acc= 0.933479 cost_time= 0.316406s \n",
      "Epoch:   1178 loss= 0.652136 acc= 0.913977 cost_time= 0.320103s \n",
      "Epoch:   1179 loss= 0.558867 acc= 0.924848 cost_time= 0.328874s \n",
      "Epoch:   1180 loss= 0.607104 acc= 0.914181 cost_time= 0.325985s \n",
      "Epoch:   1181 loss= 0.776245 acc= 0.915151 cost_time= 0.314411s \n",
      "Epoch:   1182 loss= 0.628506 acc= 0.912736 cost_time= 0.312738s \n",
      "Epoch:   1183 loss= 0.342569 acc= 0.946321 cost_time= 0.310511s \n",
      "Epoch:   1184 loss= 0.668121 acc= 0.856099 cost_time= 0.328910s \n",
      "Epoch:   1185 loss= 0.501795 acc= 0.929284 cost_time= 0.316527s \n",
      "Epoch:   1186 loss= 0.311932 acc= 0.952952 cost_time= 0.318996s \n",
      "Epoch:   1187 loss= 0.629887 acc= 0.877840 cost_time= 0.313136s \n",
      "Epoch:   1188 loss= 0.433762 acc= 0.940995 cost_time= 0.310913s \n",
      "Epoch:   1189 loss= 0.496495 acc= 0.917746 cost_time= 0.319551s \n",
      "Epoch:   1190 loss= 0.412536 acc= 0.908209 cost_time= 0.315609s \n",
      "Epoch:   1191 loss= 0.412649 acc= 0.913251 cost_time= 0.315545s \n",
      "Epoch:   1192 loss= 0.358832 acc= 0.948339 cost_time= 0.315333s \n",
      "Epoch:   1193 loss= 0.669305 acc= 0.894223 cost_time= 0.314871s \n",
      "Epoch:   1194 loss= 0.588431 acc= 0.916980 cost_time= 0.312524s \n",
      "Epoch:   1195 loss= 0.444270 acc= 0.920872 cost_time= 0.336018s \n",
      "Epoch:   1196 loss= 0.450358 acc= 0.920611 cost_time= 0.315727s \n",
      "Epoch:   1197 loss= 0.470928 acc= 0.905160 cost_time= 0.312360s \n",
      "Epoch:   1198 loss= 1.407497 acc= 0.851347 cost_time= 0.331480s \n",
      "Epoch:   1199 loss= 0.349728 acc= 0.942775 cost_time= 0.319309s \n",
      "Epoch:   1200 loss= 1.384909 acc= 0.866278 cost_time= 0.313556s \n",
      "Epoch:   1201 loss= 0.564588 acc= 0.872152 cost_time= 0.310324s \n",
      "Epoch:   1202 loss= 0.678593 acc= 0.903094 cost_time= 0.311749s \n",
      "Epoch:   1203 loss= 0.369236 acc= 0.948829 cost_time= 0.317255s \n",
      "Epoch:   1204 loss= 0.433611 acc= 0.940805 cost_time= 0.314420s \n",
      "Epoch:   1205 loss= 1.086951 acc= 0.871473 cost_time= 0.316599s \n",
      "Epoch:   1206 loss= 0.651532 acc= 0.861979 cost_time= 0.309875s \n",
      "Epoch:   1207 loss= 0.354395 acc= 0.947774 cost_time= 0.319330s \n",
      "Epoch:   1208 loss= 0.461529 acc= 0.936967 cost_time= 0.324337s \n",
      "Epoch:   1209 loss= 0.403984 acc= 0.924358 cost_time= 0.333048s \n",
      "Epoch:   1210 loss= 1.502898 acc= 0.851938 cost_time= 0.310739s \n",
      "Epoch:   1211 loss= 0.417477 acc= 0.931286 cost_time= 0.315079s \n",
      "Epoch:   1212 loss= 0.480379 acc= 0.876530 cost_time= 0.314502s \n",
      "Epoch:   1213 loss= 0.353207 acc= 0.926078 cost_time= 0.317882s \n",
      "Epoch:   1214 loss= 0.366360 acc= 0.935088 cost_time= 0.313005s \n",
      "Epoch:   1215 loss= 0.867791 acc= 0.841942 cost_time= 0.309832s \n",
      "Epoch:   1216 loss= 0.870897 acc= 0.857270 cost_time= 0.307850s \n",
      "Epoch:   1217 loss= 0.550421 acc= 0.891279 cost_time= 0.314317s \n",
      "Epoch:   1218 loss= 0.372655 acc= 0.948840 cost_time= 0.314785s \n",
      "Epoch:   1219 loss= 0.331160 acc= 0.953585 cost_time= 0.314967s \n",
      "Epoch:   1220 loss= 0.549799 acc= 0.910513 cost_time= 0.316336s \n",
      "Epoch:   1221 loss= 0.373037 acc= 0.938960 cost_time= 0.330555s \n",
      "Epoch:   1222 loss= 0.829823 acc= 0.837932 cost_time= 0.314539s \n",
      "Epoch:   1223 loss= 0.473543 acc= 0.925865 cost_time= 0.315888s \n",
      "Epoch:   1224 loss= 0.425020 acc= 0.930416 cost_time= 0.323789s \n",
      "Epoch:   1225 loss= 0.383085 acc= 0.939904 cost_time= 0.313756s \n",
      "Epoch:   1226 loss= 0.368696 acc= 0.930986 cost_time= 0.315644s \n",
      "Epoch:   1227 loss= 0.497474 acc= 0.905738 cost_time= 0.313257s \n",
      "Epoch:   1228 loss= 1.020162 acc= 0.894077 cost_time= 0.311470s \n",
      "Epoch:   1229 loss= 0.345471 acc= 0.928138 cost_time= 0.313540s \n",
      "Epoch:   1230 loss= 0.547133 acc= 0.876677 cost_time= 0.313155s \n",
      "Epoch:   1231 loss= 0.964507 acc= 0.844894 cost_time= 0.309730s \n",
      "Epoch:   1232 loss= 0.395584 acc= 0.930426 cost_time= 0.316341s \n",
      "Epoch:   1233 loss= 0.514746 acc= 0.912571 cost_time= 0.329706s \n",
      "Epoch:   1234 loss= 0.316314 acc= 0.950420 cost_time= 0.315152s \n",
      "Epoch:   1235 loss= 0.420684 acc= 0.919956 cost_time= 0.311543s \n",
      "Epoch:   1236 loss= 0.406367 acc= 0.917498 cost_time= 0.315126s \n",
      "Epoch:   1237 loss= 0.459173 acc= 0.928086 cost_time= 0.333060s \n",
      "Epoch:   1238 loss= 0.363071 acc= 0.932942 cost_time= 0.316227s \n",
      "Epoch:   1239 loss= 0.495512 acc= 0.918405 cost_time= 0.312433s \n",
      "Epoch:   1240 loss= 0.309034 acc= 0.950216 cost_time= 0.317486s \n",
      "Epoch:   1241 loss= 0.648351 acc= 0.894762 cost_time= 0.314354s \n",
      "Epoch:   1242 loss= 0.589180 acc= 0.897763 cost_time= 0.312148s \n",
      "Epoch:   1243 loss= 0.313240 acc= 0.954652 cost_time= 0.317785s \n",
      "Epoch:   1244 loss= 0.691723 acc= 0.864819 cost_time= 0.320017s \n",
      "Epoch:   1245 loss= 0.538929 acc= 0.925219 cost_time= 0.328439s \n",
      "Epoch:   1246 loss= 0.714172 acc= 0.892882 cost_time= 0.331263s \n",
      "Epoch:   1247 loss= 0.835143 acc= 0.864625 cost_time= 0.309972s \n",
      "Epoch:   1248 loss= 0.551592 acc= 0.904343 cost_time= 0.316667s \n",
      "Epoch:   1249 loss= 0.369734 acc= 0.937808 cost_time= 0.318058s \n",
      "Epoch:   1250 loss= 0.516584 acc= 0.906960 cost_time= 0.311742s \n",
      "Epoch:   1251 loss= 1.762073 acc= 0.873146 cost_time= 0.317840s \n",
      "Epoch:   1252 loss= 0.485993 acc= 0.913912 cost_time= 0.314122s \n",
      "Epoch:   1253 loss= 0.644186 acc= 0.903491 cost_time= 0.319010s \n",
      "Epoch:   1254 loss= 0.298358 acc= 0.933842 cost_time= 0.321735s \n",
      "Epoch:   1255 loss= 0.375718 acc= 0.941492 cost_time= 0.320155s \n",
      "Epoch:   1256 loss= 0.723442 acc= 0.898972 cost_time= 0.317884s \n",
      "Epoch:   1257 loss= 0.432202 acc= 0.927303 cost_time= 0.328755s \n",
      "Epoch:   1258 loss= 0.556518 acc= 0.879722 cost_time= 0.315429s \n",
      "Epoch:   1259 loss= 0.525660 acc= 0.889853 cost_time= 0.318005s \n",
      "Epoch:   1260 loss= 1.000016 acc= 0.905720 cost_time= 0.321128s \n",
      "Epoch:   1261 loss= 0.516459 acc= 0.905523 cost_time= 0.313911s \n",
      "Epoch:   1262 loss= 0.458180 acc= 0.919819 cost_time= 0.314287s \n",
      "Epoch:   1263 loss= 0.319690 acc= 0.929894 cost_time= 0.320089s \n",
      "Epoch:   1264 loss= 0.376584 acc= 0.930731 cost_time= 0.318042s \n",
      "Epoch:   1265 loss= 0.424776 acc= 0.925646 cost_time= 0.314149s \n",
      "Epoch:   1266 loss= 2.370027 acc= 0.794072 cost_time= 0.318314s \n",
      "Epoch:   1267 loss= 0.346286 acc= 0.950173 cost_time= 0.316989s \n",
      "Epoch:   1268 loss= 0.528071 acc= 0.879771 cost_time= 0.313767s \n",
      "Epoch:   1269 loss= 1.917052 acc= 0.834011 cost_time= 0.326819s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1270 loss= 0.377593 acc= 0.933074 cost_time= 0.316686s \n",
      "Epoch:   1271 loss= 0.449224 acc= 0.925427 cost_time= 0.311882s \n",
      "Epoch:   1272 loss= 0.534691 acc= 0.912717 cost_time= 0.316350s \n",
      "Epoch:   1273 loss= 0.511731 acc= 0.913582 cost_time= 0.312009s \n",
      "Epoch:   1274 loss= 0.314021 acc= 0.929151 cost_time= 0.314065s \n",
      "Epoch:   1275 loss= 1.285351 acc= 0.892853 cost_time= 0.313699s \n",
      "Epoch:   1276 loss= 0.756632 acc= 0.903346 cost_time= 0.312942s \n",
      "Epoch:   1277 loss= 0.365632 acc= 0.940249 cost_time= 0.310416s \n",
      "Epoch:   1278 loss= 0.710912 acc= 0.908567 cost_time= 0.315776s \n",
      "Epoch:   1279 loss= 0.878456 acc= 0.903484 cost_time= 0.319109s \n",
      "Epoch:   1280 loss= 0.364419 acc= 0.938312 cost_time= 0.319351s \n",
      "Epoch:   1281 loss= 0.949256 acc= 0.829438 cost_time= 0.312215s \n",
      "Epoch:   1282 loss= 0.557474 acc= 0.899517 cost_time= 0.313478s \n",
      "Epoch:   1283 loss= 0.368643 acc= 0.933497 cost_time= 0.317346s \n",
      "Epoch:   1284 loss= 0.564690 acc= 0.894710 cost_time= 0.326214s \n",
      "Epoch:   1285 loss= 0.545838 acc= 0.903826 cost_time= 0.311347s \n",
      "Epoch:   1286 loss= 1.321535 acc= 0.879822 cost_time= 0.311364s \n",
      "Epoch:   1287 loss= 1.527766 acc= 0.883512 cost_time= 0.310422s \n",
      "Epoch:   1288 loss= 0.798616 acc= 0.919413 cost_time= 0.319846s \n",
      "Epoch:   1289 loss= 0.462503 acc= 0.938540 cost_time= 0.316559s \n",
      "Epoch:   1290 loss= 0.334238 acc= 0.931338 cost_time= 0.313214s \n",
      "Epoch:   1291 loss= 0.375277 acc= 0.936837 cost_time= 0.329531s \n",
      "Epoch:   1292 loss= 0.354461 acc= 0.923310 cost_time= 0.316115s \n",
      "Epoch:   1293 loss= 0.563091 acc= 0.887766 cost_time= 0.313196s \n",
      "Epoch:   1294 loss= 0.312761 acc= 0.930998 cost_time= 0.316286s \n",
      "Epoch:   1295 loss= 0.367135 acc= 0.927384 cost_time= 0.318400s \n",
      "Epoch:   1296 loss= 1.725909 acc= 0.818245 cost_time= 0.321235s \n",
      "Epoch:   1297 loss= 0.427032 acc= 0.939886 cost_time= 0.316153s \n",
      "Epoch:   1298 loss= 1.826239 acc= 0.849376 cost_time= 0.309198s \n",
      "Epoch:   1299 loss= 0.471447 acc= 0.928624 cost_time= 0.328942s \n",
      "Epoch:   1300 loss= 0.467580 acc= 0.930404 cost_time= 0.312689s \n",
      "Epoch:   1301 loss= 0.498017 acc= 0.915095 cost_time= 0.318189s \n",
      "Epoch:   1302 loss= 0.541523 acc= 0.909658 cost_time= 0.311977s \n",
      "Epoch:   1303 loss= 0.462068 acc= 0.905384 cost_time= 0.312432s \n",
      "Epoch:   1304 loss= 0.575929 acc= 0.892396 cost_time= 0.311036s \n",
      "Epoch:   1305 loss= 1.014997 acc= 0.825279 cost_time= 0.314061s \n",
      "Epoch:   1306 loss= 0.634892 acc= 0.864046 cost_time= 0.312671s \n",
      "Epoch:   1307 loss= 0.456712 acc= 0.936194 cost_time= 0.314317s \n",
      "Epoch:   1308 loss= 0.645129 acc= 0.887482 cost_time= 0.314014s \n",
      "Epoch:   1309 loss= 0.306777 acc= 0.945783 cost_time= 0.317466s \n",
      "Epoch:   1310 loss= 1.230920 acc= 0.809281 cost_time= 0.311131s \n",
      "Epoch:   1311 loss= 0.762731 acc= 0.914813 cost_time= 0.317523s \n",
      "Epoch:   1312 loss= 0.477783 acc= 0.929498 cost_time= 0.322478s \n",
      "Epoch:   1313 loss= 0.623478 acc= 0.876373 cost_time= 0.330335s \n",
      "Epoch:   1314 loss= 0.435412 acc= 0.936752 cost_time= 0.330359s \n",
      "Epoch:   1315 loss= 0.431928 acc= 0.922727 cost_time= 0.312699s \n",
      "Epoch:   1316 loss= 1.994676 acc= 0.884166 cost_time= 0.317222s \n",
      "Epoch:   1317 loss= 0.286345 acc= 0.955586 cost_time= 0.322630s \n",
      "Epoch:   1318 loss= 0.631495 acc= 0.884787 cost_time= 0.315489s \n",
      "Epoch:   1319 loss= 0.490213 acc= 0.913729 cost_time= 0.313095s \n",
      "Epoch:   1320 loss= 0.640141 acc= 0.893260 cost_time= 0.313773s \n",
      "Epoch:   1321 loss= 0.460507 acc= 0.922721 cost_time= 0.316712s \n",
      "Epoch:   1322 loss= 0.540767 acc= 0.913322 cost_time= 0.317168s \n",
      "Epoch:   1323 loss= 0.351524 acc= 0.937216 cost_time= 0.334725s \n",
      "Epoch:   1324 loss= 0.322753 acc= 0.951002 cost_time= 0.316856s \n",
      "Epoch:   1325 loss= 0.426077 acc= 0.931195 cost_time= 0.317750s \n",
      "Epoch:   1326 loss= 0.348208 acc= 0.927912 cost_time= 0.333491s \n",
      "Epoch:   1327 loss= 0.456618 acc= 0.921299 cost_time= 0.344125s \n",
      "Epoch:   1328 loss= 0.407979 acc= 0.933048 cost_time= 0.349436s \n",
      "Epoch:   1329 loss= 0.392318 acc= 0.938087 cost_time= 0.335365s \n",
      "Epoch:   1330 loss= 0.493324 acc= 0.912416 cost_time= 0.344827s \n",
      "Epoch:   1331 loss= 1.223968 acc= 0.893597 cost_time= 0.334419s \n",
      "Epoch:   1332 loss= 0.397104 acc= 0.942062 cost_time= 0.331472s \n",
      "Epoch:   1333 loss= 0.905081 acc= 0.914050 cost_time= 0.334100s \n",
      "Epoch:   1334 loss= 0.282605 acc= 0.943750 cost_time= 0.346661s \n",
      "Epoch:   1335 loss= 0.357759 acc= 0.946239 cost_time= 0.334477s \n",
      "Epoch:   1336 loss= 0.355314 acc= 0.941166 cost_time= 0.331610s \n",
      "Epoch:   1337 loss= 0.708591 acc= 0.916217 cost_time= 0.330332s \n",
      "Epoch:   1338 loss= 0.607154 acc= 0.898210 cost_time= 0.326088s \n",
      "Epoch:   1339 loss= 0.334019 acc= 0.942886 cost_time= 0.346177s \n",
      "Epoch:   1340 loss= 0.333574 acc= 0.936886 cost_time= 0.328503s \n",
      "Epoch:   1341 loss= 0.328627 acc= 0.939543 cost_time= 0.335581s \n",
      "Epoch:   1342 loss= 0.330440 acc= 0.942368 cost_time= 0.343242s \n",
      "Epoch:   1343 loss= 0.306932 acc= 0.934561 cost_time= 0.331370s \n",
      "Epoch:   1344 loss= 0.397664 acc= 0.927257 cost_time= 0.327291s \n",
      "Epoch:   1345 loss= 0.581209 acc= 0.894893 cost_time= 0.332851s \n",
      "Epoch:   1346 loss= 0.517786 acc= 0.906638 cost_time= 0.342015s \n",
      "Epoch:   1347 loss= 0.606972 acc= 0.886932 cost_time= 0.379543s \n",
      "Epoch:   1348 loss= 0.395695 acc= 0.930241 cost_time= 0.319964s \n",
      "Epoch:   1349 loss= 0.360114 acc= 0.937575 cost_time= 0.329353s \n",
      "Epoch:   1350 loss= 0.323864 acc= 0.950130 cost_time= 0.319129s \n",
      "Epoch:   1351 loss= 0.431607 acc= 0.926747 cost_time= 0.316113s \n",
      "Epoch:   1352 loss= 0.834158 acc= 0.868694 cost_time= 0.308758s \n",
      "Epoch:   1353 loss= 0.579517 acc= 0.874340 cost_time= 0.304090s \n",
      "Epoch:   1354 loss= 0.500280 acc= 0.920013 cost_time= 0.330469s \n",
      "Epoch:   1355 loss= 2.570246 acc= 0.815250 cost_time= 0.328531s \n",
      "Epoch:   1356 loss= 0.538663 acc= 0.881293 cost_time= 0.311004s \n",
      "Epoch:   1357 loss= 0.388121 acc= 0.932657 cost_time= 0.313666s \n",
      "Epoch:   1358 loss= 0.415219 acc= 0.919517 cost_time= 0.345826s \n",
      "Epoch:   1359 loss= 0.401887 acc= 0.936713 cost_time= 0.336777s \n",
      "Epoch:   1360 loss= 1.083030 acc= 0.848326 cost_time= 0.322644s \n",
      "Epoch:   1361 loss= 0.361138 acc= 0.934235 cost_time= 0.415446s \n",
      "Epoch:   1362 loss= 0.756738 acc= 0.830883 cost_time= 0.336129s \n",
      "Epoch:   1363 loss= 0.399506 acc= 0.931029 cost_time= 0.318967s \n",
      "Epoch:   1364 loss= 1.466928 acc= 0.811257 cost_time= 0.326168s \n",
      "Epoch:   1365 loss= 0.354747 acc= 0.926196 cost_time= 0.314525s \n",
      "Epoch:   1366 loss= 0.386088 acc= 0.935455 cost_time= 0.325541s \n",
      "Epoch:   1367 loss= 0.417308 acc= 0.913949 cost_time= 0.309754s \n",
      "Epoch:   1368 loss= 0.305571 acc= 0.942334 cost_time= 0.331007s \n",
      "Epoch:   1369 loss= 0.646849 acc= 0.883446 cost_time= 0.312552s \n",
      "Epoch:   1370 loss= 0.453978 acc= 0.930378 cost_time= 0.317624s \n",
      "Epoch:   1371 loss= 0.927536 acc= 0.818197 cost_time= 0.313930s \n",
      "Epoch:   1372 loss= 0.411637 acc= 0.930986 cost_time= 0.315995s \n",
      "Epoch:   1373 loss= 0.424121 acc= 0.926398 cost_time= 0.308503s \n",
      "Epoch:   1374 loss= 0.427119 acc= 0.923828 cost_time= 0.319149s \n",
      "Epoch:   1375 loss= 0.555741 acc= 0.899262 cost_time= 0.310106s \n",
      "Epoch:   1376 loss= 0.363541 acc= 0.940782 cost_time= 0.316119s \n",
      "Epoch:   1377 loss= 0.442206 acc= 0.928547 cost_time= 0.313591s \n",
      "Epoch:   1378 loss= 0.669846 acc= 0.907169 cost_time= 0.308295s \n",
      "Epoch:   1379 loss= 0.659957 acc= 0.903033 cost_time= 0.311625s \n",
      "Epoch:   1380 loss= 0.495847 acc= 0.931802 cost_time= 0.328727s \n",
      "Epoch:   1381 loss= 0.436433 acc= 0.927825 cost_time= 0.318210s \n",
      "Epoch:   1382 loss= 0.802522 acc= 0.865599 cost_time= 0.330115s \n",
      "Epoch:   1383 loss= 0.582840 acc= 0.895759 cost_time= 0.318089s \n",
      "Epoch:   1384 loss= 0.375324 acc= 0.930943 cost_time= 0.320974s \n",
      "Epoch:   1385 loss= 1.529260 acc= 0.860246 cost_time= 0.325479s \n",
      "Epoch:   1386 loss= 0.417981 acc= 0.931081 cost_time= 0.319957s \n",
      "Epoch:   1387 loss= 0.342212 acc= 0.941968 cost_time= 0.309088s \n",
      "Epoch:   1388 loss= 0.389272 acc= 0.954679 cost_time= 0.316655s \n",
      "Epoch:   1389 loss= 0.428775 acc= 0.939060 cost_time= 0.315872s \n",
      "Epoch:   1390 loss= 0.439089 acc= 0.924670 cost_time= 0.314101s \n",
      "Epoch:   1391 loss= 1.139525 acc= 0.894472 cost_time= 0.313275s \n",
      "Epoch:   1392 loss= 1.391151 acc= 0.879454 cost_time= 0.317252s \n",
      "Epoch:   1393 loss= 0.651123 acc= 0.894858 cost_time= 0.311077s \n",
      "Epoch:   1394 loss= 0.391072 acc= 0.934235 cost_time= 0.320489s \n",
      "Epoch:   1395 loss= 0.828451 acc= 0.860972 cost_time= 0.319674s \n",
      "Epoch:   1396 loss= 0.481250 acc= 0.918633 cost_time= 0.317015s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1397 loss= 0.446251 acc= 0.937735 cost_time= 0.329772s \n",
      "Epoch:   1398 loss= 0.811767 acc= 0.914601 cost_time= 0.324798s \n",
      "Epoch:   1399 loss= 0.526138 acc= 0.923987 cost_time= 0.313765s \n",
      "Epoch:   1400 loss= 0.414254 acc= 0.932632 cost_time= 0.332309s \n",
      "Epoch:   1401 loss= 0.354174 acc= 0.947096 cost_time= 0.321037s \n",
      "Epoch:   1402 loss= 0.305900 acc= 0.929307 cost_time= 0.340316s \n",
      "Epoch:   1403 loss= 0.944165 acc= 0.858685 cost_time= 0.309160s \n",
      "Epoch:   1404 loss= 0.360222 acc= 0.947573 cost_time= 0.315889s \n",
      "Epoch:   1405 loss= 0.818729 acc= 0.861710 cost_time= 0.319000s \n",
      "Epoch:   1406 loss= 0.389369 acc= 0.928314 cost_time= 0.313327s \n",
      "Epoch:   1407 loss= 0.479172 acc= 0.914956 cost_time= 0.338251s \n",
      "Epoch:   1408 loss= 0.617043 acc= 0.912802 cost_time= 0.310989s \n",
      "Epoch:   1409 loss= 0.374614 acc= 0.938722 cost_time= 0.332721s \n",
      "Epoch:   1410 loss= 0.824291 acc= 0.857584 cost_time= 0.308599s \n",
      "Epoch:   1411 loss= 0.594589 acc= 0.905180 cost_time= 0.332510s \n",
      "Epoch:   1412 loss= 0.540404 acc= 0.932997 cost_time= 0.308482s \n",
      "Epoch:   1413 loss= 0.605416 acc= 0.897395 cost_time= 0.325476s \n",
      "Epoch:   1414 loss= 0.458458 acc= 0.929094 cost_time= 0.310525s \n",
      "Epoch:   1415 loss= 0.532310 acc= 0.893427 cost_time= 0.308602s \n",
      "Epoch:   1416 loss= 1.669603 acc= 0.869619 cost_time= 0.309206s \n",
      "Epoch:   1417 loss= 0.746236 acc= 0.905705 cost_time= 0.319013s \n",
      "Epoch:   1418 loss= 0.400178 acc= 0.931144 cost_time= 0.321178s \n",
      "Epoch:   1419 loss= 0.354824 acc= 0.945929 cost_time= 0.308143s \n",
      "Epoch:   1420 loss= 0.331709 acc= 0.947796 cost_time= 0.310789s \n",
      "Epoch:   1421 loss= 0.356141 acc= 0.924871 cost_time= 0.315856s \n",
      "Epoch:   1422 loss= 0.742874 acc= 0.912732 cost_time= 0.333446s \n",
      "Epoch:   1423 loss= 0.463825 acc= 0.906141 cost_time= 0.308257s \n",
      "Epoch:   1424 loss= 0.285338 acc= 0.943981 cost_time= 0.311484s \n",
      "Epoch:   1425 loss= 0.422548 acc= 0.918623 cost_time= 0.312586s \n",
      "Epoch:   1426 loss= 0.392790 acc= 0.932407 cost_time= 0.309010s \n",
      "Epoch:   1427 loss= 0.345232 acc= 0.936366 cost_time= 0.306221s \n",
      "Epoch:   1428 loss= 0.491815 acc= 0.920017 cost_time= 0.315104s \n",
      "Epoch:   1429 loss= 0.309974 acc= 0.934931 cost_time= 0.332961s \n",
      "Epoch:   1430 loss= 0.429022 acc= 0.941314 cost_time= 0.308425s \n",
      "Epoch:   1431 loss= 0.397768 acc= 0.923143 cost_time= 0.313005s \n",
      "Epoch:   1432 loss= 0.384072 acc= 0.928798 cost_time= 0.316248s \n",
      "Epoch:   1433 loss= 0.368074 acc= 0.946178 cost_time= 0.307114s \n",
      "Epoch:   1434 loss= 0.452974 acc= 0.917147 cost_time= 0.311628s \n",
      "Epoch:   1435 loss= 0.325752 acc= 0.937010 cost_time= 0.318657s \n",
      "Epoch:   1436 loss= 0.769575 acc= 0.861261 cost_time= 0.328849s \n",
      "Epoch:   1437 loss= 0.313765 acc= 0.946316 cost_time= 0.310890s \n",
      "Epoch:   1438 loss= 0.526865 acc= 0.921019 cost_time= 0.310483s \n",
      "Epoch:   1439 loss= 0.388710 acc= 0.935575 cost_time= 0.315178s \n",
      "Epoch:   1440 loss= 0.315572 acc= 0.943203 cost_time= 0.309889s \n",
      "Epoch:   1441 loss= 0.698654 acc= 0.911682 cost_time= 0.314806s \n",
      "Epoch:   1442 loss= 0.290684 acc= 0.943506 cost_time= 0.307928s \n",
      "Epoch:   1443 loss= 1.171100 acc= 0.875341 cost_time= 0.332884s \n",
      "Epoch:   1444 loss= 1.232008 acc= 0.853624 cost_time= 0.306579s \n",
      "Epoch:   1445 loss= 1.042576 acc= 0.894685 cost_time= 0.317442s \n",
      "Epoch:   1446 loss= 0.374971 acc= 0.936118 cost_time= 0.312479s \n",
      "Epoch:   1447 loss= 0.910620 acc= 0.854053 cost_time= 0.309579s \n",
      "Epoch:   1448 loss= 0.434260 acc= 0.931680 cost_time= 0.311074s \n",
      "Epoch:   1449 loss= 1.275004 acc= 0.872391 cost_time= 0.307748s \n",
      "Epoch:   1450 loss= 0.436143 acc= 0.927675 cost_time= 0.327930s \n",
      "Epoch:   1451 loss= 0.521147 acc= 0.893218 cost_time= 0.310275s \n",
      "Epoch:   1452 loss= 1.704548 acc= 0.809062 cost_time= 0.310556s \n",
      "Epoch:   1453 loss= 0.321571 acc= 0.930415 cost_time= 0.313094s \n",
      "Epoch:   1454 loss= 0.762618 acc= 0.902108 cost_time= 0.307035s \n",
      "Epoch:   1455 loss= 0.282075 acc= 0.945264 cost_time= 0.311271s \n",
      "Epoch:   1456 loss= 0.966309 acc= 0.887174 cost_time= 0.311946s \n",
      "Epoch:   1457 loss= 0.442624 acc= 0.925594 cost_time= 0.330842s \n",
      "Epoch:   1458 loss= 1.171406 acc= 0.845719 cost_time= 0.306889s \n",
      "Epoch:   1459 loss= 0.330732 acc= 0.937574 cost_time= 0.323654s \n",
      "Epoch:   1460 loss= 0.329614 acc= 0.941402 cost_time= 0.312848s \n",
      "Epoch:   1461 loss= 0.256422 acc= 0.946057 cost_time= 0.310181s \n",
      "Epoch:   1462 loss= 0.444310 acc= 0.915477 cost_time= 0.307423s \n",
      "Epoch:   1463 loss= 3.086043 acc= 0.785223 cost_time= 0.330544s \n",
      "Epoch:   1464 loss= 2.189151 acc= 0.849439 cost_time= 0.310027s \n",
      "Epoch:   1465 loss= 0.311213 acc= 0.948061 cost_time= 0.333268s \n",
      "Epoch:   1466 loss= 0.345132 acc= 0.939717 cost_time= 0.332798s \n",
      "Epoch:   1467 loss= 0.472789 acc= 0.913425 cost_time= 0.316405s \n",
      "Epoch:   1468 loss= 0.260649 acc= 0.944899 cost_time= 0.317603s \n",
      "Epoch:   1469 loss= 0.623649 acc= 0.856490 cost_time= 0.304305s \n",
      "Epoch:   1470 loss= 0.294217 acc= 0.939343 cost_time= 0.311496s \n",
      "Epoch:   1471 loss= 0.566764 acc= 0.904803 cost_time= 0.309265s \n",
      "Epoch:   1472 loss= 0.433104 acc= 0.923775 cost_time= 0.307827s \n",
      "Epoch:   1473 loss= 1.362972 acc= 0.858936 cost_time= 0.328227s \n",
      "Epoch:   1474 loss= 0.302210 acc= 0.935827 cost_time= 0.315731s \n",
      "Epoch:   1475 loss= 0.689990 acc= 0.853912 cost_time= 0.317114s \n",
      "Epoch:   1476 loss= 0.542494 acc= 0.898808 cost_time= 0.305468s \n",
      "Epoch:   1477 loss= 0.378919 acc= 0.937713 cost_time= 0.315181s \n",
      "Epoch:   1478 loss= 0.479922 acc= 0.922055 cost_time= 0.308644s \n",
      "Epoch:   1479 loss= 0.479002 acc= 0.900219 cost_time= 0.312590s \n",
      "Epoch:   1480 loss= 0.867100 acc= 0.891008 cost_time= 0.329994s \n",
      "Epoch:   1481 loss= 0.337604 acc= 0.938726 cost_time= 0.317995s \n",
      "Epoch:   1482 loss= 0.741392 acc= 0.859414 cost_time= 0.309288s \n",
      "Epoch:   1483 loss= 0.706327 acc= 0.852316 cost_time= 0.308910s \n",
      "Epoch:   1484 loss= 0.599747 acc= 0.876776 cost_time= 0.314021s \n",
      "Epoch:   1485 loss= 0.822446 acc= 0.915535 cost_time= 0.314297s \n",
      "Epoch:   1486 loss= 0.285659 acc= 0.939975 cost_time= 0.310336s \n",
      "Epoch:   1487 loss= 1.418368 acc= 0.842242 cost_time= 0.324691s \n",
      "Epoch:   1488 loss= 0.414904 acc= 0.925673 cost_time= 0.312912s \n",
      "Epoch:   1489 loss= 0.302271 acc= 0.925170 cost_time= 0.322364s \n",
      "Epoch:   1490 loss= 0.507371 acc= 0.888927 cost_time= 0.311490s \n",
      "Epoch:   1491 loss= 0.434462 acc= 0.888436 cost_time= 0.313366s \n",
      "Epoch:   1492 loss= 0.421181 acc= 0.915903 cost_time= 0.316435s \n",
      "Epoch:   1493 loss= 0.337662 acc= 0.935356 cost_time= 0.309279s \n",
      "Epoch:   1494 loss= 0.633572 acc= 0.886867 cost_time= 0.309526s \n",
      "Epoch:   1495 loss= 0.508182 acc= 0.925068 cost_time= 0.310931s \n",
      "Epoch:   1496 loss= 0.389420 acc= 0.912040 cost_time= 0.324332s \n",
      "Epoch:   1497 loss= 0.433189 acc= 0.941675 cost_time= 0.311074s \n",
      "Epoch:   1498 loss= 0.569306 acc= 0.898911 cost_time= 0.309061s \n",
      "Epoch:   1499 loss= 0.343077 acc= 0.947689 cost_time= 0.320657s \n",
      "Epoch:   1500 loss= 0.451961 acc= 0.924100 cost_time= 0.312882s \n",
      "Epoch:   1501 loss= 0.374114 acc= 0.942371 cost_time= 0.310557s \n",
      "Epoch:   1502 loss= 0.391088 acc= 0.923444 cost_time= 0.313766s \n",
      "Epoch:   1503 loss= 0.501698 acc= 0.925370 cost_time= 0.321752s \n",
      "Epoch:   1504 loss= 0.420873 acc= 0.927641 cost_time= 0.313513s \n",
      "Epoch:   1505 loss= 0.286459 acc= 0.952095 cost_time= 0.310957s \n",
      "Epoch:   1506 loss= 0.316259 acc= 0.946087 cost_time= 0.309753s \n",
      "Epoch:   1507 loss= 0.348534 acc= 0.944272 cost_time= 0.308444s \n",
      "Epoch:   1508 loss= 0.613908 acc= 0.888726 cost_time= 0.332950s \n",
      "Epoch:   1509 loss= 0.853171 acc= 0.882064 cost_time= 0.317142s \n",
      "Epoch:   1510 loss= 0.582556 acc= 0.904516 cost_time= 0.309763s \n",
      "Epoch:   1511 loss= 0.407963 acc= 0.933404 cost_time= 0.312115s \n",
      "Epoch:   1512 loss= 0.978075 acc= 0.879572 cost_time= 0.309343s \n",
      "Epoch:   1513 loss= 0.434208 acc= 0.912164 cost_time= 0.312711s \n",
      "Epoch:   1514 loss= 0.521110 acc= 0.915837 cost_time= 0.311111s \n",
      "Epoch:   1515 loss= 0.392059 acc= 0.930587 cost_time= 0.332521s \n",
      "Epoch:   1516 loss= 0.748446 acc= 0.908369 cost_time= 0.305945s \n",
      "Epoch:   1517 loss= 0.642303 acc= 0.874502 cost_time= 0.313320s \n",
      "Epoch:   1518 loss= 0.507253 acc= 0.907481 cost_time= 0.308508s \n",
      "Epoch:   1519 loss= 0.286329 acc= 0.944610 cost_time= 0.310539s \n",
      "Epoch:   1520 loss= 0.755659 acc= 0.887962 cost_time= 0.333095s \n",
      "Epoch:   1521 loss= 0.409073 acc= 0.922782 cost_time= 0.307528s \n",
      "Epoch:   1522 loss= 0.367148 acc= 0.919102 cost_time= 0.312601s \n",
      "Epoch:   1523 loss= 0.434478 acc= 0.908762 cost_time= 0.311498s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1524 loss= 0.380515 acc= 0.920517 cost_time= 0.327899s \n",
      "Epoch:   1525 loss= 0.639982 acc= 0.916033 cost_time= 0.317026s \n",
      "Epoch:   1526 loss= 1.699812 acc= 0.846261 cost_time= 0.309283s \n",
      "Epoch:   1527 loss= 1.152647 acc= 0.861956 cost_time= 0.310377s \n",
      "Epoch:   1528 loss= 0.297835 acc= 0.939342 cost_time= 0.314626s \n",
      "Epoch:   1529 loss= 0.342696 acc= 0.940177 cost_time= 0.336726s \n",
      "Epoch:   1530 loss= 0.475696 acc= 0.902999 cost_time= 0.308178s \n",
      "Epoch:   1531 loss= 0.376864 acc= 0.927767 cost_time= 0.312448s \n",
      "Epoch:   1532 loss= 0.443975 acc= 0.930349 cost_time= 0.310054s \n",
      "Epoch:   1533 loss= 1.074018 acc= 0.907989 cost_time= 0.311844s \n",
      "Epoch:   1534 loss= 0.361944 acc= 0.947238 cost_time= 0.312811s \n",
      "Epoch:   1535 loss= 0.386056 acc= 0.923537 cost_time= 0.307103s \n",
      "Epoch:   1536 loss= 0.599053 acc= 0.908286 cost_time= 0.306596s \n",
      "Epoch:   1537 loss= 0.408736 acc= 0.926749 cost_time= 0.312269s \n",
      "Epoch:   1538 loss= 0.342644 acc= 0.949091 cost_time= 0.318398s \n",
      "Epoch:   1539 loss= 0.597356 acc= 0.908233 cost_time= 0.305660s \n",
      "Epoch:   1540 loss= 0.445726 acc= 0.916462 cost_time= 0.311620s \n",
      "Epoch:   1541 loss= 0.330661 acc= 0.943201 cost_time= 0.310263s \n",
      "Epoch:   1542 loss= 0.512176 acc= 0.911096 cost_time= 0.314226s \n",
      "Epoch:   1543 loss= 0.366934 acc= 0.942938 cost_time= 0.309219s \n",
      "Epoch:   1544 loss= 0.321186 acc= 0.940621 cost_time= 0.312554s \n",
      "Epoch:   1545 loss= 0.362845 acc= 0.928439 cost_time= 0.314173s \n",
      "Epoch:   1546 loss= 0.339291 acc= 0.927256 cost_time= 0.311209s \n",
      "Epoch:   1547 loss= 0.547576 acc= 0.897750 cost_time= 0.306494s \n",
      "Epoch:   1548 loss= 0.350761 acc= 0.945542 cost_time= 0.311646s \n",
      "Epoch:   1549 loss= 0.346312 acc= 0.927684 cost_time= 0.311726s \n",
      "Epoch:   1550 loss= 0.508151 acc= 0.908579 cost_time= 0.325861s \n",
      "Epoch:   1551 loss= 2.593312 acc= 0.818283 cost_time= 0.310263s \n",
      "Epoch:   1552 loss= 0.477033 acc= 0.895937 cost_time= 0.308319s \n",
      "Epoch:   1553 loss= 0.264859 acc= 0.943948 cost_time= 0.316783s \n",
      "Epoch:   1554 loss= 0.456714 acc= 0.917834 cost_time= 0.305631s \n",
      "Epoch:   1555 loss= 0.281786 acc= 0.940670 cost_time= 0.310789s \n",
      "Epoch:   1556 loss= 0.377169 acc= 0.938518 cost_time= 0.313526s \n",
      "Epoch:   1557 loss= 0.423132 acc= 0.927594 cost_time= 0.331596s \n",
      "Epoch:   1558 loss= 0.738746 acc= 0.857554 cost_time= 0.310062s \n",
      "Epoch:   1559 loss= 0.461822 acc= 0.944602 cost_time= 0.307679s \n",
      "Epoch:   1560 loss= 0.631694 acc= 0.901087 cost_time= 0.305943s \n",
      "Epoch:   1561 loss= 0.426892 acc= 0.912371 cost_time= 0.308506s \n",
      "Epoch:   1562 loss= 0.319444 acc= 0.951529 cost_time= 0.313615s \n",
      "Epoch:   1563 loss= 0.474759 acc= 0.914139 cost_time= 0.317868s \n",
      "Epoch:   1564 loss= 0.300743 acc= 0.952931 cost_time= 0.331894s \n",
      "Epoch:   1565 loss= 0.407556 acc= 0.933468 cost_time= 0.306441s \n",
      "Epoch:   1566 loss= 0.482169 acc= 0.931563 cost_time= 0.314745s \n",
      "Epoch:   1567 loss= 0.762670 acc= 0.894464 cost_time= 0.312881s \n",
      "Epoch:   1568 loss= 0.312601 acc= 0.935019 cost_time= 0.311707s \n",
      "Epoch:   1569 loss= 0.340327 acc= 0.937201 cost_time= 0.312847s \n",
      "Epoch:   1570 loss= 0.348900 acc= 0.925143 cost_time= 0.311520s \n",
      "Epoch:   1571 loss= 0.446803 acc= 0.914094 cost_time= 0.330190s \n",
      "Epoch:   1572 loss= 0.414432 acc= 0.929901 cost_time= 0.310166s \n",
      "Epoch:   1573 loss= 0.463467 acc= 0.913202 cost_time= 0.310389s \n",
      "Epoch:   1574 loss= 0.356841 acc= 0.944854 cost_time= 0.317965s \n",
      "Epoch:   1575 loss= 0.315958 acc= 0.943704 cost_time= 0.311396s \n",
      "Epoch:   1576 loss= 0.362650 acc= 0.930756 cost_time= 0.329996s \n",
      "Epoch:   1577 loss= 1.056741 acc= 0.885800 cost_time= 0.311786s \n",
      "Epoch:   1578 loss= 0.357761 acc= 0.923471 cost_time= 0.310291s \n",
      "Epoch:   1579 loss= 0.655858 acc= 0.863338 cost_time= 0.308321s \n",
      "Epoch:   1580 loss= 1.574635 acc= 0.816176 cost_time= 0.315343s \n",
      "Epoch:   1581 loss= 0.765139 acc= 0.861435 cost_time= 0.308666s \n",
      "Epoch:   1582 loss= 0.400505 acc= 0.930603 cost_time= 0.307702s \n",
      "Epoch:   1583 loss= 0.277869 acc= 0.947543 cost_time= 0.312343s \n",
      "Epoch:   1584 loss= 0.313054 acc= 0.936427 cost_time= 0.317195s \n",
      "Epoch:   1585 loss= 0.332347 acc= 0.948969 cost_time= 0.315915s \n",
      "Epoch:   1586 loss= 0.299136 acc= 0.936970 cost_time= 0.309232s \n",
      "Epoch:   1587 loss= 0.409451 acc= 0.915879 cost_time= 0.322482s \n",
      "Epoch:   1588 loss= 0.359227 acc= 0.935755 cost_time= 0.317038s \n",
      "Epoch:   1589 loss= 0.332850 acc= 0.949748 cost_time= 0.325329s \n",
      "Epoch:   1590 loss= 0.317188 acc= 0.946140 cost_time= 0.311112s \n",
      "Epoch:   1591 loss= 0.312277 acc= 0.952824 cost_time= 0.312562s \n",
      "Epoch:   1592 loss= 0.499760 acc= 0.906453 cost_time= 0.307985s \n",
      "Epoch:   1593 loss= 0.707968 acc= 0.915225 cost_time= 0.313766s \n",
      "Epoch:   1594 loss= 0.406010 acc= 0.924844 cost_time= 0.319664s \n",
      "Epoch:   1595 loss= 0.556288 acc= 0.934430 cost_time= 0.308748s \n",
      "Epoch:   1596 loss= 0.287121 acc= 0.946483 cost_time= 0.312917s \n",
      "Epoch:   1597 loss= 1.743825 acc= 0.853403 cost_time= 0.308864s \n",
      "Epoch:   1598 loss= 0.273697 acc= 0.942071 cost_time= 0.312365s \n",
      "Epoch:   1599 loss= 1.961707 acc= 0.856302 cost_time= 0.331113s \n",
      "Epoch:   1600 loss= 0.514755 acc= 0.910782 cost_time= 0.312163s \n",
      "Epoch:   1601 loss= 0.472241 acc= 0.903211 cost_time= 0.305200s \n",
      "Epoch:   1602 loss= 0.576715 acc= 0.886842 cost_time= 0.310976s \n",
      "Epoch:   1603 loss= 0.374029 acc= 0.933114 cost_time= 0.309969s \n",
      "Epoch:   1604 loss= 0.526184 acc= 0.930576 cost_time= 0.312455s \n",
      "Epoch:   1605 loss= 0.406900 acc= 0.936756 cost_time= 0.308813s \n",
      "Epoch:   1606 loss= 0.344576 acc= 0.929864 cost_time= 0.308780s \n",
      "Epoch:   1607 loss= 0.972964 acc= 0.883462 cost_time= 0.328491s \n",
      "Epoch:   1608 loss= 0.580522 acc= 0.909177 cost_time= 0.327219s \n",
      "Epoch:   1609 loss= 0.387294 acc= 0.947882 cost_time= 0.313849s \n",
      "Epoch:   1610 loss= 0.301467 acc= 0.937093 cost_time= 0.312447s \n",
      "Epoch:   1611 loss= 0.463568 acc= 0.912161 cost_time= 0.308876s \n",
      "Epoch:   1612 loss= 0.912712 acc= 0.853786 cost_time= 0.315437s \n",
      "Epoch:   1613 loss= 0.370975 acc= 0.923489 cost_time= 0.310254s \n",
      "Epoch:   1614 loss= 0.345475 acc= 0.926491 cost_time= 0.309597s \n",
      "Epoch:   1615 loss= 1.446787 acc= 0.854080 cost_time= 0.328808s \n",
      "Epoch:   1616 loss= 1.226388 acc= 0.879780 cost_time= 0.309047s \n",
      "Epoch:   1617 loss= 0.469685 acc= 0.926331 cost_time= 0.308613s \n",
      "Epoch:   1618 loss= 0.986666 acc= 0.834189 cost_time= 0.311004s \n",
      "Epoch:   1619 loss= 0.456075 acc= 0.932450 cost_time= 0.311372s \n",
      "Epoch:   1620 loss= 0.293952 acc= 0.947637 cost_time= 0.307482s \n",
      "Epoch:   1621 loss= 0.981202 acc= 0.814862 cost_time= 0.306353s \n",
      "Epoch:   1622 loss= 0.351006 acc= 0.945478 cost_time= 0.311430s \n",
      "Epoch:   1623 loss= 1.084482 acc= 0.912903 cost_time= 0.331547s \n",
      "Epoch:   1624 loss= 0.293238 acc= 0.932099 cost_time= 0.310581s \n",
      "Epoch:   1625 loss= 0.251770 acc= 0.963312 cost_time= 0.356480s \n",
      "Epoch:   1626 loss= 0.413885 acc= 0.928094 cost_time= 0.309783s \n",
      "Epoch:   1627 loss= 0.283184 acc= 0.930493 cost_time= 0.332418s \n",
      "Epoch:   1628 loss= 0.310173 acc= 0.933700 cost_time= 0.309133s \n",
      "Epoch:   1629 loss= 0.354493 acc= 0.934769 cost_time= 0.310642s \n",
      "Epoch:   1630 loss= 0.405358 acc= 0.935915 cost_time= 0.312905s \n",
      "Epoch:   1631 loss= 0.585399 acc= 0.912154 cost_time= 0.313578s \n",
      "Epoch:   1632 loss= 0.586010 acc= 0.905487 cost_time= 0.312627s \n",
      "Epoch:   1633 loss= 0.291793 acc= 0.937975 cost_time= 0.312808s \n",
      "Epoch:   1634 loss= 0.445801 acc= 0.939916 cost_time= 0.312184s \n",
      "Epoch:   1635 loss= 1.372093 acc= 0.857128 cost_time= 0.307761s \n",
      "Epoch:   1636 loss= 0.740207 acc= 0.853633 cost_time= 0.328587s \n",
      "Epoch:   1637 loss= 0.906446 acc= 0.891830 cost_time= 0.311500s \n",
      "Epoch:   1638 loss= 0.614413 acc= 0.884636 cost_time= 0.311843s \n",
      "Epoch:   1639 loss= 0.912655 acc= 0.900251 cost_time= 0.303880s \n",
      "Epoch:   1640 loss= 0.674793 acc= 0.884153 cost_time= 0.309219s \n",
      "Epoch:   1641 loss= 0.507773 acc= 0.907065 cost_time= 0.316735s \n",
      "Epoch:   1642 loss= 0.391419 acc= 0.928623 cost_time= 0.308717s \n",
      "Epoch:   1643 loss= 0.397061 acc= 0.922888 cost_time= 0.329202s \n",
      "Epoch:   1644 loss= 0.331044 acc= 0.930239 cost_time= 0.313663s \n",
      "Epoch:   1645 loss= 0.368517 acc= 0.941278 cost_time= 0.312279s \n",
      "Epoch:   1646 loss= 0.975398 acc= 0.898163 cost_time= 0.316772s \n",
      "Epoch:   1647 loss= 0.391344 acc= 0.922644 cost_time= 0.313426s \n",
      "Epoch:   1648 loss= 0.378547 acc= 0.929858 cost_time= 0.332847s \n",
      "Epoch:   1649 loss= 0.292999 acc= 0.958626 cost_time= 0.310991s \n",
      "Epoch:   1650 loss= 1.513819 acc= 0.885437 cost_time= 0.313307s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1651 loss= 0.275826 acc= 0.955791 cost_time= 0.311330s \n",
      "Epoch:   1652 loss= 1.219650 acc= 0.797341 cost_time= 0.315255s \n",
      "Epoch:   1653 loss= 1.267321 acc= 0.860976 cost_time= 0.307387s \n",
      "Epoch:   1654 loss= 0.297467 acc= 0.949992 cost_time= 0.314949s \n",
      "Epoch:   1655 loss= 0.323873 acc= 0.941860 cost_time= 0.313271s \n",
      "Epoch:   1656 loss= 0.269484 acc= 0.930830 cost_time= 0.311485s \n",
      "Epoch:   1657 loss= 0.324392 acc= 0.929490 cost_time= 0.331088s \n",
      "Epoch:   1658 loss= 0.509003 acc= 0.884257 cost_time= 0.308194s \n",
      "Epoch:   1659 loss= 1.165558 acc= 0.771498 cost_time= 0.306249s \n",
      "Epoch:   1660 loss= 0.336507 acc= 0.916208 cost_time= 0.304147s \n",
      "Epoch:   1661 loss= 0.403300 acc= 0.923922 cost_time= 0.312465s \n",
      "Epoch:   1662 loss= 0.355911 acc= 0.928962 cost_time= 0.312476s \n",
      "Epoch:   1663 loss= 0.776961 acc= 0.899114 cost_time= 0.306576s \n",
      "Epoch:   1664 loss= 0.349726 acc= 0.931246 cost_time= 0.329140s \n",
      "Epoch:   1665 loss= 0.258179 acc= 0.952736 cost_time= 0.313009s \n",
      "Epoch:   1666 loss= 0.477925 acc= 0.917515 cost_time= 0.312049s \n",
      "Epoch:   1667 loss= 0.408400 acc= 0.930743 cost_time= 0.315549s \n",
      "Epoch:   1668 loss= 0.708068 acc= 0.906387 cost_time= 0.310077s \n",
      "Epoch:   1669 loss= 0.572602 acc= 0.887245 cost_time= 0.306913s \n",
      "Epoch:   1670 loss= 0.382296 acc= 0.935867 cost_time= 0.311980s \n",
      "Epoch:   1671 loss= 0.632517 acc= 0.849220 cost_time= 0.308806s \n",
      "Epoch:   1672 loss= 0.385293 acc= 0.944271 cost_time= 0.311570s \n",
      "Epoch:   1673 loss= 1.144519 acc= 0.900384 cost_time= 0.311373s \n",
      "Epoch:   1674 loss= 1.599711 acc= 0.871101 cost_time= 0.309844s \n",
      "Epoch:   1675 loss= 2.231776 acc= 0.806175 cost_time= 0.309675s \n",
      "Epoch:   1676 loss= 1.052082 acc= 0.893088 cost_time= 0.333145s \n",
      "Epoch:   1677 loss= 0.365157 acc= 0.942144 cost_time= 0.311529s \n",
      "Epoch:   1678 loss= 0.277603 acc= 0.946262 cost_time= 0.316492s \n",
      "Epoch:   1679 loss= 0.467630 acc= 0.916091 cost_time= 0.311530s \n",
      "Epoch:   1680 loss= 0.735746 acc= 0.886114 cost_time= 0.313699s \n",
      "Epoch:   1681 loss= 0.427484 acc= 0.919500 cost_time= 0.307691s \n",
      "Epoch:   1682 loss= 1.065274 acc= 0.862304 cost_time= 0.305076s \n",
      "Epoch:   1683 loss= 0.334081 acc= 0.940335 cost_time= 0.310623s \n",
      "Epoch:   1684 loss= 0.707682 acc= 0.907365 cost_time= 0.307578s \n",
      "Epoch:   1685 loss= 0.749933 acc= 0.889739 cost_time= 0.331157s \n",
      "Epoch:   1686 loss= 0.666661 acc= 0.895053 cost_time= 0.311826s \n",
      "Epoch:   1687 loss= 1.420506 acc= 0.870599 cost_time= 0.305386s \n",
      "Epoch:   1688 loss= 0.345141 acc= 0.938074 cost_time= 0.312084s \n",
      "Epoch:   1689 loss= 1.342448 acc= 0.879622 cost_time= 0.309184s \n",
      "Epoch:   1690 loss= 0.314932 acc= 0.935144 cost_time= 0.331834s \n",
      "Epoch:   1691 loss= 0.839447 acc= 0.849709 cost_time= 0.309118s \n",
      "Epoch:   1692 loss= 0.475768 acc= 0.923803 cost_time= 0.334578s \n",
      "Epoch:   1693 loss= 0.642342 acc= 0.881610 cost_time= 0.313569s \n",
      "Epoch:   1694 loss= 0.408689 acc= 0.948200 cost_time= 0.316524s \n",
      "Epoch:   1695 loss= 0.514777 acc= 0.893614 cost_time= 0.306072s \n",
      "Epoch:   1696 loss= 0.374501 acc= 0.934485 cost_time= 0.311703s \n",
      "Epoch:   1697 loss= 1.372562 acc= 0.898979 cost_time= 0.311657s \n",
      "Epoch:   1698 loss= 0.334588 acc= 0.930182 cost_time= 0.311125s \n",
      "Epoch:   1699 loss= 0.763323 acc= 0.878316 cost_time= 0.322754s \n",
      "Epoch:   1700 loss= 0.475291 acc= 0.932346 cost_time= 0.310817s \n",
      "Epoch:   1701 loss= 0.570694 acc= 0.896386 cost_time= 0.310895s \n",
      "Epoch:   1702 loss= 1.374442 acc= 0.883992 cost_time= 0.312142s \n",
      "Epoch:   1703 loss= 0.681320 acc= 0.875084 cost_time= 0.307267s \n",
      "Epoch:   1704 loss= 0.486923 acc= 0.885119 cost_time= 0.329786s \n",
      "Epoch:   1705 loss= 0.419154 acc= 0.925186 cost_time= 0.315335s \n",
      "Epoch:   1706 loss= 0.355532 acc= 0.925898 cost_time= 0.312789s \n",
      "Epoch:   1707 loss= 0.924970 acc= 0.887665 cost_time= 0.312237s \n",
      "Epoch:   1708 loss= 0.301060 acc= 0.952042 cost_time= 0.320382s \n",
      "Epoch:   1709 loss= 0.402378 acc= 0.937520 cost_time= 0.307016s \n",
      "Epoch:   1710 loss= 0.380012 acc= 0.919073 cost_time= 0.312875s \n",
      "Epoch:   1711 loss= 0.353106 acc= 0.935216 cost_time= 0.312420s \n",
      "Epoch:   1712 loss= 0.575619 acc= 0.892300 cost_time= 0.310262s \n",
      "Epoch:   1713 loss= 1.572039 acc= 0.867616 cost_time= 0.333558s \n",
      "Epoch:   1714 loss= 0.316897 acc= 0.951418 cost_time= 0.310821s \n",
      "Epoch:   1715 loss= 0.931854 acc= 0.833213 cost_time= 0.307720s \n",
      "Epoch:   1716 loss= 0.299958 acc= 0.924429 cost_time= 0.310688s \n",
      "Epoch:   1717 loss= 0.901705 acc= 0.794373 cost_time= 0.303075s \n",
      "Epoch:   1718 loss= 0.345098 acc= 0.944677 cost_time= 0.330483s \n",
      "Epoch:   1719 loss= 1.508695 acc= 0.873980 cost_time= 0.312765s \n",
      "Epoch:   1720 loss= 0.300723 acc= 0.938486 cost_time= 0.309626s \n",
      "Epoch:   1721 loss= 0.306826 acc= 0.935560 cost_time= 0.309120s \n",
      "Epoch:   1722 loss= 0.291056 acc= 0.946628 cost_time= 0.320753s \n",
      "Epoch:   1723 loss= 0.365840 acc= 0.935734 cost_time= 0.313416s \n",
      "Epoch:   1724 loss= 0.452034 acc= 0.913597 cost_time= 0.309685s \n",
      "Epoch:   1725 loss= 0.499220 acc= 0.894855 cost_time= 0.312837s \n",
      "Epoch:   1726 loss= 0.717417 acc= 0.859607 cost_time= 0.313282s \n",
      "Epoch:   1727 loss= 1.139599 acc= 0.845005 cost_time= 0.310590s \n",
      "Epoch:   1728 loss= 0.489430 acc= 0.911674 cost_time= 0.306712s \n",
      "Epoch:   1729 loss= 0.684256 acc= 0.921898 cost_time= 0.317550s \n",
      "Epoch:   1730 loss= 0.766244 acc= 0.895680 cost_time= 0.311086s \n",
      "Epoch:   1731 loss= 0.275889 acc= 0.958481 cost_time= 0.312093s \n",
      "Epoch:   1732 loss= 0.298298 acc= 0.926945 cost_time= 0.311833s \n",
      "Epoch:   1733 loss= 0.384669 acc= 0.938740 cost_time= 0.315506s \n",
      "Epoch:   1734 loss= 0.624561 acc= 0.878692 cost_time= 0.323789s \n",
      "Epoch:   1735 loss= 0.331941 acc= 0.947690 cost_time= 0.312699s \n",
      "Epoch:   1736 loss= 0.414910 acc= 0.942243 cost_time= 0.323334s \n",
      "Epoch:   1737 loss= 0.788970 acc= 0.899181 cost_time= 0.309779s \n",
      "Epoch:   1738 loss= 0.299413 acc= 0.938070 cost_time= 0.314066s \n",
      "Epoch:   1739 loss= 0.418625 acc= 0.902227 cost_time= 0.305800s \n",
      "Epoch:   1740 loss= 0.834984 acc= 0.852429 cost_time= 0.310274s \n",
      "Epoch:   1741 loss= 0.604019 acc= 0.902643 cost_time= 0.307339s \n",
      "Epoch:   1742 loss= 0.323805 acc= 0.938474 cost_time= 0.310606s \n",
      "Epoch:   1743 loss= 0.467927 acc= 0.907119 cost_time= 0.315835s \n",
      "Epoch:   1744 loss= 2.023848 acc= 0.819392 cost_time= 0.313886s \n",
      "Epoch:   1745 loss= 0.663180 acc= 0.891014 cost_time= 0.302294s \n",
      "Epoch:   1746 loss= 1.093301 acc= 0.862780 cost_time= 0.308834s \n",
      "Epoch:   1747 loss= 0.407637 acc= 0.918855 cost_time= 0.311938s \n",
      "Epoch:   1748 loss= 0.346655 acc= 0.933333 cost_time= 0.331027s \n",
      "Epoch:   1749 loss= 0.450060 acc= 0.901319 cost_time= 0.307200s \n",
      "Epoch:   1750 loss= 0.468620 acc= 0.902098 cost_time= 0.317239s \n",
      "Epoch:   1751 loss= 0.290353 acc= 0.945642 cost_time= 0.306844s \n",
      "Epoch:   1752 loss= 0.380555 acc= 0.932031 cost_time= 0.315805s \n",
      "Epoch:   1753 loss= 0.406383 acc= 0.930761 cost_time= 0.312477s \n",
      "Epoch:   1754 loss= 0.486605 acc= 0.921002 cost_time= 0.309441s \n",
      "Epoch:   1755 loss= 0.354011 acc= 0.936096 cost_time= 0.307937s \n",
      "Epoch:   1756 loss= 0.288784 acc= 0.951289 cost_time= 0.308508s \n",
      "Epoch:   1757 loss= 0.515277 acc= 0.894123 cost_time= 0.322037s \n",
      "Epoch:   1758 loss= 0.837371 acc= 0.903178 cost_time= 0.308725s \n",
      "Epoch:   1759 loss= 0.272855 acc= 0.945047 cost_time= 0.308567s \n",
      "Epoch:   1760 loss= 0.276733 acc= 0.947599 cost_time= 0.309790s \n",
      "Epoch:   1761 loss= 1.147849 acc= 0.882785 cost_time= 0.316067s \n",
      "Epoch:   1762 loss= 0.377016 acc= 0.922205 cost_time= 0.329029s \n",
      "Epoch:   1763 loss= 0.380742 acc= 0.932084 cost_time= 0.311610s \n",
      "Epoch:   1764 loss= 0.575131 acc= 0.894174 cost_time= 0.315694s \n",
      "Epoch:   1765 loss= 0.411714 acc= 0.930585 cost_time= 0.311088s \n",
      "Epoch:   1766 loss= 0.553357 acc= 0.886714 cost_time= 0.311856s \n",
      "Epoch:   1767 loss= 0.334527 acc= 0.933053 cost_time= 0.317531s \n",
      "Epoch:   1768 loss= 0.422024 acc= 0.924121 cost_time= 0.310694s \n",
      "Epoch:   1769 loss= 0.232426 acc= 0.960365 cost_time= 0.318730s \n",
      "Epoch:   1770 loss= 0.395438 acc= 0.927729 cost_time= 0.312124s \n",
      "Epoch:   1771 loss= 0.331147 acc= 0.942827 cost_time= 0.311395s \n",
      "Epoch:   1772 loss= 0.525968 acc= 0.907308 cost_time= 0.307492s \n",
      "Epoch:   1773 loss= 0.716932 acc= 0.832675 cost_time= 0.303676s \n",
      "Epoch:   1774 loss= 0.265541 acc= 0.948746 cost_time= 0.321234s \n",
      "Epoch:   1775 loss= 0.301161 acc= 0.934879 cost_time= 0.310853s \n",
      "Epoch:   1776 loss= 0.416456 acc= 0.931040 cost_time= 0.336050s \n",
      "Epoch:   1777 loss= 0.276847 acc= 0.946941 cost_time= 0.309155s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1778 loss= 0.404985 acc= 0.928148 cost_time= 0.315545s \n",
      "Epoch:   1779 loss= 0.391859 acc= 0.943183 cost_time= 0.310729s \n",
      "Epoch:   1780 loss= 0.955436 acc= 0.838861 cost_time= 0.310238s \n",
      "Epoch:   1781 loss= 0.320392 acc= 0.942011 cost_time= 0.312547s \n",
      "Epoch:   1782 loss= 0.419792 acc= 0.935445 cost_time= 0.312333s \n",
      "Epoch:   1783 loss= 0.679985 acc= 0.875951 cost_time= 0.326320s \n",
      "Epoch:   1784 loss= 0.267740 acc= 0.948252 cost_time= 0.308620s \n",
      "Epoch:   1785 loss= 0.731232 acc= 0.897663 cost_time= 0.316157s \n",
      "Epoch:   1786 loss= 0.255360 acc= 0.945111 cost_time= 0.310428s \n",
      "Epoch:   1787 loss= 0.266544 acc= 0.939452 cost_time= 0.314297s \n",
      "Epoch:   1788 loss= 0.299621 acc= 0.938406 cost_time= 0.309775s \n",
      "Epoch:   1789 loss= 0.455920 acc= 0.935949 cost_time= 0.305017s \n",
      "Epoch:   1790 loss= 0.913761 acc= 0.896953 cost_time= 0.327192s \n",
      "Epoch:   1791 loss= 0.351778 acc= 0.952908 cost_time= 0.314428s \n",
      "Epoch:   1792 loss= 1.299851 acc= 0.865078 cost_time= 0.313450s \n",
      "Epoch:   1793 loss= 0.368551 acc= 0.932710 cost_time= 0.314234s \n",
      "Epoch:   1794 loss= 0.435790 acc= 0.927613 cost_time= 0.310455s \n",
      "Epoch:   1795 loss= 0.340518 acc= 0.928456 cost_time= 0.327664s \n",
      "Epoch:   1796 loss= 0.844233 acc= 0.871035 cost_time= 0.306692s \n",
      "Epoch:   1797 loss= 0.524103 acc= 0.908885 cost_time= 0.307340s \n",
      "Epoch:   1798 loss= 0.298079 acc= 0.946223 cost_time= 0.312305s \n",
      "Epoch:   1799 loss= 0.397523 acc= 0.931960 cost_time= 0.313417s \n",
      "Epoch:   1800 loss= 0.514483 acc= 0.926045 cost_time= 0.309940s \n",
      "Epoch:   1801 loss= 0.495876 acc= 0.918400 cost_time= 0.325472s \n",
      "Epoch:   1802 loss= 0.521750 acc= 0.904358 cost_time= 0.304539s \n",
      "Epoch:   1803 loss= 0.348335 acc= 0.944147 cost_time= 0.329711s \n",
      "Epoch:   1804 loss= 0.566889 acc= 0.868735 cost_time= 0.311140s \n",
      "Epoch:   1805 loss= 0.995862 acc= 0.845233 cost_time= 0.327890s \n",
      "Epoch:   1806 loss= 0.389843 acc= 0.938492 cost_time= 0.330045s \n",
      "Epoch:   1807 loss= 1.303562 acc= 0.877471 cost_time= 0.309767s \n",
      "Epoch:   1808 loss= 0.471475 acc= 0.916700 cost_time= 0.315336s \n",
      "Epoch:   1809 loss= 0.501590 acc= 0.909269 cost_time= 0.308705s \n",
      "Epoch:   1810 loss= 0.376881 acc= 0.933686 cost_time= 0.311376s \n",
      "Epoch:   1811 loss= 0.608315 acc= 0.916713 cost_time= 0.307592s \n",
      "Epoch:   1812 loss= 0.375313 acc= 0.933051 cost_time= 0.306095s \n",
      "Epoch:   1813 loss= 0.370637 acc= 0.935964 cost_time= 0.329102s \n",
      "Epoch:   1814 loss= 0.280254 acc= 0.945173 cost_time= 0.309873s \n",
      "Epoch:   1815 loss= 0.315968 acc= 0.931762 cost_time= 0.316348s \n",
      "Epoch:   1816 loss= 0.536072 acc= 0.912844 cost_time= 0.305048s \n",
      "Epoch:   1817 loss= 0.324811 acc= 0.923998 cost_time= 0.314824s \n",
      "Epoch:   1818 loss= 0.295202 acc= 0.960616 cost_time= 0.310403s \n",
      "Epoch:   1819 loss= 0.309525 acc= 0.951933 cost_time= 0.315544s \n",
      "Epoch:   1820 loss= 0.386743 acc= 0.928434 cost_time= 0.332687s \n",
      "Epoch:   1821 loss= 0.350457 acc= 0.942295 cost_time= 0.309169s \n",
      "Epoch:   1822 loss= 0.369374 acc= 0.928010 cost_time= 0.312520s \n",
      "Epoch:   1823 loss= 1.239956 acc= 0.887525 cost_time= 0.309067s \n",
      "Epoch:   1824 loss= 0.358745 acc= 0.933789 cost_time= 0.311392s \n",
      "Epoch:   1825 loss= 0.503187 acc= 0.889812 cost_time= 0.328384s \n",
      "Epoch:   1826 loss= 0.455640 acc= 0.914276 cost_time= 0.307291s \n",
      "Epoch:   1827 loss= 0.414517 acc= 0.936675 cost_time= 0.333184s \n",
      "Epoch:   1828 loss= 0.717371 acc= 0.892585 cost_time= 0.306741s \n",
      "Epoch:   1829 loss= 0.352829 acc= 0.944848 cost_time= 0.310443s \n",
      "Epoch:   1830 loss= 0.696487 acc= 0.908109 cost_time= 0.317468s \n",
      "Epoch:   1831 loss= 0.356353 acc= 0.941783 cost_time= 0.313993s \n",
      "Epoch:   1832 loss= 0.365726 acc= 0.930318 cost_time= 0.313917s \n",
      "Epoch:   1833 loss= 0.255620 acc= 0.940921 cost_time= 0.311112s \n",
      "Epoch:   1834 loss= 0.398473 acc= 0.943184 cost_time= 0.315730s \n",
      "Epoch:   1835 loss= 0.357334 acc= 0.937673 cost_time= 0.310361s \n",
      "Epoch:   1836 loss= 0.353025 acc= 0.929587 cost_time= 0.313293s \n",
      "Epoch:   1837 loss= 0.360166 acc= 0.953162 cost_time= 0.314585s \n",
      "Epoch:   1838 loss= 0.368356 acc= 0.928665 cost_time= 0.310466s \n",
      "Epoch:   1839 loss= 0.382270 acc= 0.922207 cost_time= 0.318788s \n",
      "Epoch:   1840 loss= 0.247293 acc= 0.943566 cost_time= 0.316343s \n",
      "Epoch:   1841 loss= 0.373309 acc= 0.943289 cost_time= 0.310448s \n",
      "Epoch:   1842 loss= 0.424389 acc= 0.935638 cost_time= 0.311049s \n",
      "Epoch:   1843 loss= 0.376904 acc= 0.927292 cost_time= 0.312909s \n",
      "Epoch:   1844 loss= 0.330052 acc= 0.929716 cost_time= 0.328465s \n",
      "Epoch:   1845 loss= 0.435503 acc= 0.932587 cost_time= 0.311828s \n",
      "Epoch:   1846 loss= 0.342698 acc= 0.940836 cost_time= 0.332816s \n",
      "Epoch:   1847 loss= 0.322536 acc= 0.933270 cost_time= 0.309686s \n",
      "Epoch:   1848 loss= 0.938920 acc= 0.896698 cost_time= 0.313194s \n",
      "Epoch:   1849 loss= 0.590050 acc= 0.871494 cost_time= 0.310369s \n",
      "Epoch:   1850 loss= 0.276287 acc= 0.942438 cost_time= 0.320918s \n",
      "Epoch:   1851 loss= 1.923220 acc= 0.874775 cost_time= 0.306978s \n",
      "Epoch:   1852 loss= 0.407649 acc= 0.920280 cost_time= 0.313254s \n",
      "Epoch:   1853 loss= 0.529010 acc= 0.915181 cost_time= 0.306807s \n",
      "Epoch:   1854 loss= 0.358025 acc= 0.940064 cost_time= 0.311759s \n",
      "Epoch:   1855 loss= 0.425543 acc= 0.924373 cost_time= 0.312336s \n",
      "Epoch:   1856 loss= 1.501239 acc= 0.794202 cost_time= 0.310214s \n",
      "Epoch:   1857 loss= 0.627106 acc= 0.889747 cost_time= 0.309450s \n",
      "Epoch:   1858 loss= 0.489172 acc= 0.907362 cost_time= 0.307842s \n",
      "Epoch:   1859 loss= 0.544217 acc= 0.883168 cost_time= 0.307503s \n",
      "Epoch:   1860 loss= 0.299237 acc= 0.937892 cost_time= 0.308841s \n",
      "Epoch:   1861 loss= 0.346287 acc= 0.928237 cost_time= 0.312684s \n",
      "Epoch:   1862 loss= 1.393080 acc= 0.891981 cost_time= 0.334091s \n",
      "Epoch:   1863 loss= 0.295891 acc= 0.936542 cost_time= 0.308115s \n",
      "Epoch:   1864 loss= 0.415835 acc= 0.914485 cost_time= 0.312383s \n",
      "Epoch:   1865 loss= 0.298303 acc= 0.945977 cost_time= 0.315380s \n",
      "Epoch:   1866 loss= 0.887976 acc= 0.900951 cost_time= 0.309868s \n",
      "Epoch:   1867 loss= 1.967212 acc= 0.875860 cost_time= 0.308517s \n",
      "Epoch:   1868 loss= 0.374926 acc= 0.942162 cost_time= 0.314813s \n",
      "Epoch:   1869 loss= 0.304620 acc= 0.942248 cost_time= 0.331563s \n",
      "Epoch:   1870 loss= 0.305148 acc= 0.938475 cost_time= 0.311126s \n",
      "Epoch:   1871 loss= 0.356526 acc= 0.935422 cost_time= 0.311760s \n",
      "Epoch:   1872 loss= 0.318872 acc= 0.949421 cost_time= 0.308274s \n",
      "Epoch:   1873 loss= 0.416999 acc= 0.924821 cost_time= 0.311326s \n",
      "Epoch:   1874 loss= 0.641107 acc= 0.879615 cost_time= 0.326582s \n",
      "Epoch:   1875 loss= 0.791038 acc= 0.867548 cost_time= 0.303534s \n",
      "Epoch:   1876 loss= 0.469720 acc= 0.912687 cost_time= 0.310923s \n",
      "Epoch:   1877 loss= 0.333332 acc= 0.917385 cost_time= 0.308339s \n",
      "Epoch:   1878 loss= 0.325112 acc= 0.938134 cost_time= 0.320739s \n",
      "Epoch:   1879 loss= 0.500626 acc= 0.906627 cost_time= 0.309183s \n",
      "Epoch:   1880 loss= 0.299289 acc= 0.943956 cost_time= 0.313252s \n",
      "Epoch:   1881 loss= 0.383051 acc= 0.945402 cost_time= 0.308557s \n",
      "Epoch:   1882 loss= 0.300064 acc= 0.957408 cost_time= 0.318190s \n",
      "Epoch:   1883 loss= 0.255854 acc= 0.944343 cost_time= 0.315181s \n",
      "Epoch:   1884 loss= 0.398289 acc= 0.941296 cost_time= 0.308127s \n",
      "Epoch:   1885 loss= 0.409712 acc= 0.940832 cost_time= 0.315975s \n",
      "Epoch:   1886 loss= 0.513314 acc= 0.929715 cost_time= 0.304023s \n",
      "Epoch:   1887 loss= 1.298049 acc= 0.874758 cost_time= 0.308239s \n",
      "Epoch:   1888 loss= 0.443680 acc= 0.919900 cost_time= 0.314111s \n",
      "Epoch:   1889 loss= 0.360832 acc= 0.925842 cost_time= 0.314332s \n",
      "Epoch:   1890 loss= 0.554933 acc= 0.893777 cost_time= 0.326391s \n",
      "Epoch:   1891 loss= 0.369983 acc= 0.933666 cost_time= 0.316991s \n",
      "Epoch:   1892 loss= 0.255513 acc= 0.958737 cost_time= 0.318948s \n",
      "Epoch:   1893 loss= 0.327997 acc= 0.933192 cost_time= 0.308343s \n",
      "Epoch:   1894 loss= 0.669428 acc= 0.853294 cost_time= 0.302830s \n",
      "Epoch:   1895 loss= 0.573978 acc= 0.919327 cost_time= 0.310684s \n",
      "Epoch:   1896 loss= 0.329738 acc= 0.932119 cost_time= 0.309076s \n",
      "Epoch:   1897 loss= 0.291338 acc= 0.957549 cost_time= 0.331768s \n",
      "Epoch:   1898 loss= 0.408375 acc= 0.925462 cost_time= 0.308776s \n",
      "Epoch:   1899 loss= 0.429835 acc= 0.917076 cost_time= 0.319463s \n",
      "Epoch:   1900 loss= 0.614643 acc= 0.918786 cost_time= 0.305602s \n",
      "Epoch:   1901 loss= 0.795822 acc= 0.888902 cost_time= 0.314605s \n",
      "Epoch:   1902 loss= 0.624044 acc= 0.883218 cost_time= 0.311505s \n",
      "Epoch:   1903 loss= 0.614715 acc= 0.888532 cost_time= 0.312819s \n",
      "Epoch:   1904 loss= 0.330446 acc= 0.952476 cost_time= 0.308391s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1905 loss= 0.262362 acc= 0.937550 cost_time= 0.311865s \n",
      "Epoch:   1906 loss= 0.386312 acc= 0.920599 cost_time= 0.310188s \n",
      "Epoch:   1907 loss= 0.322836 acc= 0.949053 cost_time= 0.311672s \n",
      "Epoch:   1908 loss= 0.302583 acc= 0.951065 cost_time= 0.312597s \n",
      "Epoch:   1909 loss= 0.675528 acc= 0.879441 cost_time= 0.313775s \n",
      "Epoch:   1910 loss= 0.330974 acc= 0.938854 cost_time= 0.310416s \n",
      "Epoch:   1911 loss= 0.318579 acc= 0.937411 cost_time= 0.330231s \n",
      "Epoch:   1912 loss= 0.315436 acc= 0.952397 cost_time= 0.311546s \n",
      "Epoch:   1913 loss= 1.391479 acc= 0.885445 cost_time= 0.312481s \n",
      "Epoch:   1914 loss= 0.529337 acc= 0.884153 cost_time= 0.307908s \n",
      "Epoch:   1915 loss= 0.384581 acc= 0.934940 cost_time= 0.317096s \n",
      "Epoch:   1916 loss= 0.662870 acc= 0.910082 cost_time= 0.319777s \n",
      "Epoch:   1917 loss= 0.985218 acc= 0.895316 cost_time= 0.317358s \n",
      "Epoch:   1918 loss= 0.310929 acc= 0.931536 cost_time= 0.326309s \n",
      "Epoch:   1919 loss= 0.776180 acc= 0.908846 cost_time= 0.311401s \n",
      "Epoch:   1920 loss= 0.397259 acc= 0.929638 cost_time= 0.316438s \n",
      "Epoch:   1921 loss= 0.277552 acc= 0.939643 cost_time= 0.312111s \n",
      "Epoch:   1922 loss= 0.539910 acc= 0.894820 cost_time= 0.310576s \n",
      "Epoch:   1923 loss= 0.864991 acc= 0.905131 cost_time= 0.319983s \n",
      "Epoch:   1924 loss= 0.578339 acc= 0.919238 cost_time= 0.309184s \n",
      "Epoch:   1925 loss= 0.544488 acc= 0.910482 cost_time= 0.312345s \n",
      "Epoch:   1926 loss= 0.357507 acc= 0.927032 cost_time= 0.312981s \n",
      "Epoch:   1927 loss= 0.352353 acc= 0.924433 cost_time= 0.309280s \n",
      "Epoch:   1928 loss= 0.461699 acc= 0.900831 cost_time= 0.311433s \n",
      "Epoch:   1929 loss= 0.350412 acc= 0.941866 cost_time= 0.308508s \n",
      "Epoch:   1930 loss= 1.579282 acc= 0.826967 cost_time= 0.328960s \n",
      "Epoch:   1931 loss= 0.319375 acc= 0.936529 cost_time= 0.310088s \n",
      "Epoch:   1932 loss= 0.775507 acc= 0.897073 cost_time= 0.310046s \n",
      "Epoch:   1933 loss= 0.601548 acc= 0.880649 cost_time= 0.315476s \n",
      "Epoch:   1934 loss= 0.346384 acc= 0.948198 cost_time= 0.314438s \n",
      "Epoch:   1935 loss= 0.286912 acc= 0.949733 cost_time= 0.309693s \n",
      "Epoch:   1936 loss= 0.777925 acc= 0.891770 cost_time= 0.313642s \n",
      "Epoch:   1937 loss= 0.322135 acc= 0.942706 cost_time= 0.313335s \n",
      "Epoch:   1938 loss= 0.980406 acc= 0.917438 cost_time= 0.314630s \n",
      "Epoch:   1939 loss= 0.263729 acc= 0.947987 cost_time= 0.334937s \n",
      "Epoch:   1940 loss= 0.359832 acc= 0.945965 cost_time= 0.313545s \n",
      "Epoch:   1941 loss= 0.446209 acc= 0.921257 cost_time= 0.307148s \n",
      "Epoch:   1942 loss= 0.460330 acc= 0.906169 cost_time= 0.309013s \n",
      "Epoch:   1943 loss= 1.237775 acc= 0.844897 cost_time= 0.310414s \n",
      "Epoch:   1944 loss= 0.526716 acc= 0.890604 cost_time= 0.328360s \n",
      "Epoch:   1945 loss= 0.250013 acc= 0.939425 cost_time= 0.315423s \n",
      "Epoch:   1946 loss= 0.618580 acc= 0.853344 cost_time= 0.308611s \n",
      "Epoch:   1947 loss= 1.184242 acc= 0.893491 cost_time= 0.312335s \n",
      "Epoch:   1948 loss= 0.566834 acc= 0.879649 cost_time= 0.314928s \n",
      "Epoch:   1949 loss= 0.386750 acc= 0.931253 cost_time= 0.308841s \n",
      "Epoch:   1950 loss= 0.350846 acc= 0.929779 cost_time= 0.310552s \n",
      "Epoch:   1951 loss= 1.138246 acc= 0.851544 cost_time= 0.306221s \n",
      "Epoch:   1952 loss= 0.644446 acc= 0.866430 cost_time= 0.305676s \n",
      "Epoch:   1953 loss= 0.400618 acc= 0.926729 cost_time= 0.329041s \n",
      "Epoch:   1954 loss= 0.306082 acc= 0.945265 cost_time= 0.313480s \n",
      "Epoch:   1955 loss= 0.679070 acc= 0.848431 cost_time= 0.316335s \n",
      "Epoch:   1956 loss= 0.462268 acc= 0.941664 cost_time= 0.310387s \n",
      "Epoch:   1957 loss= 0.418647 acc= 0.931172 cost_time= 0.315096s \n",
      "Epoch:   1958 loss= 0.355303 acc= 0.948046 cost_time= 0.309908s \n",
      "Epoch:   1959 loss= 0.337861 acc= 0.930205 cost_time= 0.307982s \n",
      "Epoch:   1960 loss= 0.295749 acc= 0.932601 cost_time= 0.319454s \n",
      "Epoch:   1961 loss= 0.385403 acc= 0.927914 cost_time= 0.309994s \n",
      "Epoch:   1962 loss= 0.360415 acc= 0.942933 cost_time= 0.315826s \n",
      "Epoch:   1963 loss= 0.872196 acc= 0.893865 cost_time= 0.309112s \n",
      "Epoch:   1964 loss= 0.371927 acc= 0.918696 cost_time= 0.311358s \n",
      "Epoch:   1965 loss= 0.612289 acc= 0.878791 cost_time= 0.309283s \n",
      "Epoch:   1966 loss= 0.567909 acc= 0.870805 cost_time= 0.305507s \n",
      "Epoch:   1967 loss= 0.346437 acc= 0.924251 cost_time= 0.330386s \n",
      "Epoch:   1968 loss= 0.440876 acc= 0.918748 cost_time= 0.311242s \n",
      "Epoch:   1969 loss= 0.395309 acc= 0.926327 cost_time= 0.306268s \n",
      "Epoch:   1970 loss= 0.440702 acc= 0.918561 cost_time= 0.312350s \n",
      "Epoch:   1971 loss= 0.345797 acc= 0.915623 cost_time= 0.312944s \n",
      "Epoch:   1972 loss= 0.422298 acc= 0.933945 cost_time= 0.308359s \n",
      "Epoch:   1973 loss= 0.386051 acc= 0.921022 cost_time= 0.308817s \n",
      "Epoch:   1974 loss= 0.292153 acc= 0.944583 cost_time= 0.334167s \n",
      "Epoch:   1975 loss= 1.004704 acc= 0.801858 cost_time= 0.310841s \n",
      "Epoch:   1976 loss= 0.576261 acc= 0.891114 cost_time= 0.312669s \n",
      "Epoch:   1977 loss= 0.277909 acc= 0.943230 cost_time= 0.315857s \n",
      "Epoch:   1978 loss= 0.647935 acc= 0.874589 cost_time= 0.308956s \n",
      "Epoch:   1979 loss= 0.449110 acc= 0.923033 cost_time= 0.313542s \n",
      "Epoch:   1980 loss= 0.341265 acc= 0.942660 cost_time= 0.308696s \n",
      "Epoch:   1981 loss= 0.353400 acc= 0.920275 cost_time= 0.313036s \n",
      "Epoch:   1982 loss= 0.276489 acc= 0.948878 cost_time= 0.310805s \n",
      "Epoch:   1983 loss= 0.336689 acc= 0.943976 cost_time= 0.315404s \n",
      "Epoch:   1984 loss= 0.722931 acc= 0.883947 cost_time= 0.305363s \n",
      "Epoch:   1985 loss= 1.280711 acc= 0.885533 cost_time= 0.311970s \n",
      "Epoch:   1986 loss= 0.374060 acc= 0.944485 cost_time= 0.310236s \n",
      "Epoch:   1987 loss= 0.347678 acc= 0.930413 cost_time= 0.305979s \n",
      "Epoch:   1988 loss= 1.223910 acc= 0.880478 cost_time= 0.330601s \n",
      "Epoch:   1989 loss= 0.375068 acc= 0.932961 cost_time= 0.310701s \n",
      "Epoch:   1990 loss= 0.684390 acc= 0.903222 cost_time= 0.307655s \n",
      "Epoch:   1991 loss= 0.438669 acc= 0.930963 cost_time= 0.308138s \n",
      "Epoch:   1992 loss= 0.412927 acc= 0.929976 cost_time= 0.313447s \n",
      "Epoch:   1993 loss= 0.275468 acc= 0.950607 cost_time= 0.334612s \n",
      "Epoch:   1994 loss= 0.521019 acc= 0.920403 cost_time= 0.307846s \n",
      "Epoch:   1995 loss= 0.313986 acc= 0.946550 cost_time= 0.334658s \n",
      "Epoch:   1996 loss= 0.307624 acc= 0.944225 cost_time= 0.312330s \n",
      "Epoch:   1997 loss= 0.371288 acc= 0.929863 cost_time= 0.310410s \n",
      "Epoch:   1998 loss= 0.372563 acc= 0.931207 cost_time= 0.309896s \n",
      "Epoch:   1999 loss= 0.414348 acc= 0.912484 cost_time= 0.315883s \n",
      "Epoch:   2000 loss= 0.541453 acc= 0.906366 cost_time= 0.331639s \n",
      "Epoch:   2001 loss= 0.409146 acc= 0.923478 cost_time= 0.308121s \n",
      "Epoch:   2002 loss= 0.401225 acc= 0.933602 cost_time= 0.308995s \n",
      "Epoch:   2003 loss= 0.316453 acc= 0.942331 cost_time= 0.310595s \n",
      "Epoch:   2004 loss= 0.369276 acc= 0.935754 cost_time= 0.309998s \n",
      "Epoch:   2005 loss= 0.836904 acc= 0.909455 cost_time= 0.310185s \n",
      "Epoch:   2006 loss= 0.566821 acc= 0.901446 cost_time= 0.313330s \n",
      "Epoch:   2007 loss= 0.289841 acc= 0.945050 cost_time= 0.309308s \n",
      "Epoch:   2008 loss= 0.304442 acc= 0.935226 cost_time= 0.313136s \n",
      "Epoch:   2009 loss= 1.226842 acc= 0.882514 cost_time= 0.330725s \n",
      "Epoch:   2010 loss= 0.534023 acc= 0.893201 cost_time= 0.303533s \n",
      "Epoch:   2011 loss= 1.035500 acc= 0.867196 cost_time= 0.312588s \n",
      "Epoch:   2012 loss= 0.295207 acc= 0.952630 cost_time= 0.309952s \n",
      "Epoch:   2013 loss= 0.273995 acc= 0.951078 cost_time= 0.313882s \n",
      "Epoch:   2014 loss= 0.452843 acc= 0.917258 cost_time= 0.308088s \n",
      "Epoch:   2015 loss= 0.314704 acc= 0.946987 cost_time= 0.314624s \n",
      "Epoch:   2016 loss= 0.415378 acc= 0.930195 cost_time= 0.331197s \n",
      "Epoch:   2017 loss= 0.292927 acc= 0.938226 cost_time= 0.315644s \n",
      "Epoch:   2018 loss= 0.359550 acc= 0.941642 cost_time= 0.317948s \n",
      "Epoch:   2019 loss= 0.293175 acc= 0.933768 cost_time= 0.314423s \n",
      "Epoch:   2020 loss= 0.399742 acc= 0.934146 cost_time= 0.310062s \n",
      "Epoch:   2021 loss= 0.459115 acc= 0.910250 cost_time= 0.315121s \n",
      "Epoch:   2022 loss= 0.940051 acc= 0.887233 cost_time= 0.312526s \n",
      "Epoch:   2023 loss= 0.477193 acc= 0.919324 cost_time= 0.308132s \n",
      "Epoch:   2024 loss= 0.308145 acc= 0.933446 cost_time= 0.311994s \n",
      "Epoch:   2025 loss= 0.414031 acc= 0.932308 cost_time= 0.313881s \n",
      "Epoch:   2026 loss= 0.345149 acc= 0.950096 cost_time= 0.306766s \n",
      "Epoch:   2027 loss= 0.487820 acc= 0.896407 cost_time= 0.308853s \n",
      "Epoch:   2028 loss= 0.312610 acc= 0.931233 cost_time= 0.316341s \n",
      "Epoch:   2029 loss= 0.231673 acc= 0.952446 cost_time= 0.313697s \n",
      "Epoch:   2030 loss= 0.443518 acc= 0.910519 cost_time= 0.330077s \n",
      "Epoch:   2031 loss= 0.816522 acc= 0.822171 cost_time= 0.299522s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2032 loss= 1.838826 acc= 0.791504 cost_time= 0.318444s \n",
      "Epoch:   2033 loss= 0.368722 acc= 0.918459 cost_time= 0.310042s \n",
      "Epoch:   2034 loss= 0.457561 acc= 0.904103 cost_time= 0.308137s \n",
      "Epoch:   2035 loss= 0.361611 acc= 0.930373 cost_time= 0.307745s \n",
      "Epoch:   2036 loss= 0.445433 acc= 0.922942 cost_time= 0.313669s \n",
      "Epoch:   2037 loss= 0.320742 acc= 0.942002 cost_time= 0.330194s \n",
      "Epoch:   2038 loss= 0.301434 acc= 0.940383 cost_time= 0.309630s \n",
      "Epoch:   2039 loss= 0.623973 acc= 0.853883 cost_time= 0.307280s \n",
      "Epoch:   2040 loss= 0.313762 acc= 0.944218 cost_time= 0.310159s \n",
      "Epoch:   2041 loss= 0.352421 acc= 0.940194 cost_time= 0.311085s \n",
      "Epoch:   2042 loss= 0.509606 acc= 0.924842 cost_time= 0.305890s \n",
      "Epoch:   2043 loss= 0.367941 acc= 0.923150 cost_time= 0.308719s \n",
      "Epoch:   2044 loss= 1.978778 acc= 0.829168 cost_time= 0.325824s \n",
      "Epoch:   2045 loss= 0.336068 acc= 0.932196 cost_time= 0.313542s \n",
      "Epoch:   2046 loss= 0.571074 acc= 0.886470 cost_time= 0.319413s \n",
      "Epoch:   2047 loss= 1.319596 acc= 0.831694 cost_time= 0.322175s \n",
      "Epoch:   2048 loss= 0.291881 acc= 0.934378 cost_time= 0.311558s \n",
      "Epoch:   2049 loss= 0.385826 acc= 0.919430 cost_time= 0.306534s \n",
      "Epoch:   2050 loss= 0.347550 acc= 0.946178 cost_time= 0.311971s \n",
      "Epoch:   2051 loss= 0.329635 acc= 0.934758 cost_time= 0.312027s \n",
      "Epoch:   2052 loss= 1.619744 acc= 0.876588 cost_time= 0.307723s \n",
      "Epoch:   2053 loss= 0.345563 acc= 0.926733 cost_time= 0.320747s \n",
      "Epoch:   2054 loss= 0.281056 acc= 0.946100 cost_time= 0.310619s \n",
      "Epoch:   2055 loss= 0.320990 acc= 0.944559 cost_time= 0.315863s \n",
      "Epoch:   2056 loss= 0.444409 acc= 0.893707 cost_time= 0.311619s \n",
      "Epoch:   2057 loss= 0.783788 acc= 0.876401 cost_time= 0.306715s \n",
      "Epoch:   2058 loss= 0.436885 acc= 0.932847 cost_time= 0.331782s \n",
      "Epoch:   2059 loss= 0.297879 acc= 0.930727 cost_time= 0.308824s \n",
      "Epoch:   2060 loss= 0.325770 acc= 0.941858 cost_time= 0.311059s \n",
      "Epoch:   2061 loss= 0.840030 acc= 0.901808 cost_time= 0.310360s \n",
      "Epoch:   2062 loss= 0.367461 acc= 0.944220 cost_time= 0.308655s \n",
      "Epoch:   2063 loss= 0.330842 acc= 0.934727 cost_time= 0.331672s \n",
      "Epoch:   2064 loss= 0.420920 acc= 0.918056 cost_time= 0.310765s \n",
      "Epoch:   2065 loss= 0.385030 acc= 0.939782 cost_time= 0.309373s \n",
      "Epoch:   2066 loss= 0.312702 acc= 0.943542 cost_time= 0.316046s \n",
      "Epoch:   2067 loss= 0.298488 acc= 0.947339 cost_time= 0.315226s \n",
      "Epoch:   2068 loss= 0.281853 acc= 0.939061 cost_time= 0.324554s \n",
      "Epoch:   2069 loss= 0.495565 acc= 0.907466 cost_time= 0.307523s \n",
      "Epoch:   2070 loss= 0.654624 acc= 0.863636 cost_time= 0.310282s \n",
      "Epoch:   2071 loss= 0.298806 acc= 0.937961 cost_time= 0.309727s \n",
      "Epoch:   2072 loss= 0.288823 acc= 0.948136 cost_time= 0.332016s \n",
      "Epoch:   2073 loss= 1.235135 acc= 0.812466 cost_time= 0.306828s \n",
      "Epoch:   2074 loss= 0.333794 acc= 0.931823 cost_time= 0.320713s \n",
      "Epoch:   2075 loss= 0.380338 acc= 0.927761 cost_time= 0.308660s \n",
      "Epoch:   2076 loss= 0.418669 acc= 0.924470 cost_time= 0.306524s \n",
      "Epoch:   2077 loss= 0.390890 acc= 0.933969 cost_time= 0.313798s \n",
      "Epoch:   2078 loss= 1.382014 acc= 0.873063 cost_time= 0.314415s \n",
      "Epoch:   2079 loss= 0.418390 acc= 0.932923 cost_time= 0.328572s \n",
      "Epoch:   2080 loss= 0.495074 acc= 0.894268 cost_time= 0.305702s \n",
      "Epoch:   2081 loss= 0.319882 acc= 0.934839 cost_time= 0.316751s \n",
      "Epoch:   2082 loss= 0.477685 acc= 0.907053 cost_time= 0.310392s \n",
      "Epoch:   2083 loss= 0.360875 acc= 0.943336 cost_time= 0.312710s \n",
      "Epoch:   2084 loss= 0.580004 acc= 0.922240 cost_time= 0.321715s \n",
      "Epoch:   2085 loss= 0.275759 acc= 0.947597 cost_time= 0.314736s \n",
      "Epoch:   2086 loss= 0.358827 acc= 0.926816 cost_time= 0.330972s \n",
      "Epoch:   2087 loss= 0.253832 acc= 0.931780 cost_time= 0.311261s \n",
      "Epoch:   2088 loss= 0.327390 acc= 0.937063 cost_time= 0.317036s \n",
      "Epoch:   2089 loss= 0.330864 acc= 0.944959 cost_time= 0.317354s \n",
      "Epoch:   2090 loss= 0.545128 acc= 0.901021 cost_time= 0.302626s \n",
      "Epoch:   2091 loss= 0.345513 acc= 0.927504 cost_time= 0.311144s \n",
      "Epoch:   2092 loss= 0.671679 acc= 0.920268 cost_time= 0.311431s \n",
      "Epoch:   2093 loss= 0.500870 acc= 0.893783 cost_time= 0.310925s \n",
      "Epoch:   2094 loss= 0.586805 acc= 0.914850 cost_time= 0.329025s \n",
      "Epoch:   2095 loss= 1.151662 acc= 0.870004 cost_time= 0.308113s \n",
      "Epoch:   2096 loss= 0.437607 acc= 0.902912 cost_time= 0.309417s \n",
      "Epoch:   2097 loss= 0.276133 acc= 0.943682 cost_time= 0.319513s \n",
      "Epoch:   2098 loss= 0.246496 acc= 0.944438 cost_time= 0.312064s \n",
      "Epoch:   2099 loss= 0.364465 acc= 0.933318 cost_time= 0.313464s \n",
      "Epoch:   2100 loss= 0.478194 acc= 0.917247 cost_time= 0.327865s \n",
      "Epoch:   2101 loss= 0.847138 acc= 0.861212 cost_time= 0.311508s \n",
      "Epoch:   2102 loss= 0.375838 acc= 0.939220 cost_time= 0.321005s \n",
      "Epoch:   2103 loss= 0.286564 acc= 0.940656 cost_time= 0.313517s \n",
      "Epoch:   2104 loss= 0.357059 acc= 0.908135 cost_time= 0.307193s \n",
      "Epoch:   2105 loss= 0.467713 acc= 0.894793 cost_time= 0.311524s \n",
      "Epoch:   2106 loss= 0.217853 acc= 0.957276 cost_time= 0.311028s \n",
      "Epoch:   2107 loss= 0.621362 acc= 0.863893 cost_time= 0.323950s \n",
      "Epoch:   2108 loss= 0.409959 acc= 0.930050 cost_time= 0.310776s \n",
      "Epoch:   2109 loss= 0.344389 acc= 0.930515 cost_time= 0.316132s \n",
      "Epoch:   2110 loss= 0.827317 acc= 0.856693 cost_time= 0.314018s \n",
      "Epoch:   2111 loss= 0.269154 acc= 0.949310 cost_time= 0.312195s \n",
      "Epoch:   2112 loss= 0.313307 acc= 0.936254 cost_time= 0.308321s \n",
      "Epoch:   2113 loss= 0.326443 acc= 0.933752 cost_time= 0.310281s \n",
      "Epoch:   2114 loss= 0.302562 acc= 0.940109 cost_time= 0.315881s \n",
      "Epoch:   2115 loss= 0.605836 acc= 0.923392 cost_time= 0.311211s \n",
      "Epoch:   2116 loss= 0.368721 acc= 0.937241 cost_time= 0.309636s \n",
      "Epoch:   2117 loss= 0.320412 acc= 0.946231 cost_time= 0.312448s \n",
      "Epoch:   2118 loss= 1.287064 acc= 0.872260 cost_time= 0.308782s \n",
      "Epoch:   2119 loss= 0.292326 acc= 0.933565 cost_time= 0.307536s \n",
      "Epoch:   2120 loss= 0.385091 acc= 0.911300 cost_time= 0.312202s \n",
      "Epoch:   2121 loss= 0.587006 acc= 0.893577 cost_time= 0.326168s \n",
      "Epoch:   2122 loss= 0.368467 acc= 0.942903 cost_time= 0.312451s \n",
      "Epoch:   2123 loss= 0.505116 acc= 0.900376 cost_time= 0.317726s \n",
      "Epoch:   2124 loss= 1.065246 acc= 0.832007 cost_time= 0.308115s \n",
      "Epoch:   2125 loss= 0.359530 acc= 0.942032 cost_time= 0.322629s \n",
      "Epoch:   2126 loss= 0.841102 acc= 0.864109 cost_time= 0.302761s \n",
      "Epoch:   2127 loss= 0.670943 acc= 0.907695 cost_time= 0.310989s \n",
      "Epoch:   2128 loss= 0.449389 acc= 0.914171 cost_time= 0.319329s \n",
      "Epoch:   2129 loss= 2.410684 acc= 0.827482 cost_time= 0.313061s \n",
      "Epoch:   2130 loss= 1.936009 acc= 0.825168 cost_time= 0.309935s \n",
      "Epoch:   2131 loss= 1.007231 acc= 0.826916 cost_time= 0.312509s \n",
      "Epoch:   2132 loss= 0.372716 acc= 0.937105 cost_time= 0.308943s \n",
      "Epoch:   2133 loss= 0.919565 acc= 0.899612 cost_time= 0.307744s \n",
      "Epoch:   2134 loss= 0.760071 acc= 0.890324 cost_time= 0.313546s \n",
      "Epoch:   2135 loss= 0.466167 acc= 0.906401 cost_time= 0.331048s \n",
      "Epoch:   2136 loss= 0.285007 acc= 0.950120 cost_time= 0.312687s \n",
      "Epoch:   2137 loss= 0.350083 acc= 0.913201 cost_time= 0.319715s \n",
      "Epoch:   2138 loss= 0.242203 acc= 0.943254 cost_time= 0.309057s \n",
      "Epoch:   2139 loss= 0.333790 acc= 0.937472 cost_time= 0.312126s \n",
      "Epoch:   2140 loss= 0.489217 acc= 0.896475 cost_time= 0.308009s \n",
      "Epoch:   2141 loss= 0.542122 acc= 0.911053 cost_time= 0.311759s \n",
      "Epoch:   2142 loss= 0.365484 acc= 0.927982 cost_time= 0.312714s \n",
      "Epoch:   2143 loss= 0.585635 acc= 0.910827 cost_time= 0.308268s \n",
      "Epoch:   2144 loss= 0.548896 acc= 0.902017 cost_time= 0.313019s \n",
      "Epoch:   2145 loss= 0.325484 acc= 0.944065 cost_time= 0.309042s \n",
      "Epoch:   2146 loss= 0.603887 acc= 0.912657 cost_time= 0.316756s \n",
      "Epoch:   2147 loss= 0.424867 acc= 0.923175 cost_time= 0.311634s \n",
      "Epoch:   2148 loss= 0.875207 acc= 0.883018 cost_time= 0.321388s \n",
      "Epoch:   2149 loss= 0.367241 acc= 0.932494 cost_time= 0.329500s \n",
      "Epoch:   2150 loss= 0.365209 acc= 0.938249 cost_time= 0.312266s \n",
      "Epoch:   2151 loss= 0.348750 acc= 0.917932 cost_time= 0.326604s \n",
      "Epoch:   2152 loss= 0.339182 acc= 0.937623 cost_time= 0.307597s \n",
      "Epoch:   2153 loss= 0.329249 acc= 0.938883 cost_time= 0.309495s \n",
      "Epoch:   2154 loss= 0.809976 acc= 0.890979 cost_time= 0.306716s \n",
      "Epoch:   2155 loss= 0.323492 acc= 0.948729 cost_time= 0.311173s \n",
      "Epoch:   2156 loss= 0.256513 acc= 0.939440 cost_time= 0.309897s \n",
      "Epoch:   2157 loss= 0.508966 acc= 0.905870 cost_time= 0.309920s \n",
      "Epoch:   2158 loss= 0.386100 acc= 0.935864 cost_time= 0.320730s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2159 loss= 0.632872 acc= 0.865461 cost_time= 0.305533s \n",
      "Epoch:   2160 loss= 0.451640 acc= 0.918488 cost_time= 0.311720s \n",
      "Epoch:   2161 loss= 0.287592 acc= 0.938911 cost_time= 0.319134s \n",
      "Epoch:   2162 loss= 0.499121 acc= 0.906490 cost_time= 0.308428s \n",
      "Epoch:   2163 loss= 0.907318 acc= 0.865969 cost_time= 0.305702s \n",
      "Epoch:   2164 loss= 1.405471 acc= 0.876581 cost_time= 0.309623s \n",
      "Epoch:   2165 loss= 0.835779 acc= 0.903429 cost_time= 0.313013s \n",
      "Epoch:   2166 loss= 1.433638 acc= 0.848937 cost_time= 0.314990s \n",
      "Epoch:   2167 loss= 0.314654 acc= 0.943197 cost_time= 0.317265s \n",
      "Epoch:   2168 loss= 0.647948 acc= 0.897753 cost_time= 0.306838s \n",
      "Epoch:   2169 loss= 0.226218 acc= 0.948712 cost_time= 0.311093s \n",
      "Epoch:   2170 loss= 0.678083 acc= 0.905935 cost_time= 0.331743s \n",
      "Epoch:   2171 loss= 0.598177 acc= 0.909845 cost_time= 0.314388s \n",
      "Epoch:   2172 loss= 0.770180 acc= 0.902491 cost_time= 0.321499s \n",
      "Epoch:   2173 loss= 0.475573 acc= 0.931224 cost_time= 0.315204s \n",
      "Epoch:   2174 loss= 0.531002 acc= 0.886429 cost_time= 0.308082s \n",
      "Epoch:   2175 loss= 0.419295 acc= 0.934242 cost_time= 0.308145s \n",
      "Epoch:   2176 loss= 0.319743 acc= 0.936165 cost_time= 0.316130s \n",
      "Epoch:   2177 loss= 0.988336 acc= 0.864528 cost_time= 0.327429s \n",
      "Epoch:   2178 loss= 0.365534 acc= 0.923601 cost_time= 0.309670s \n",
      "Epoch:   2179 loss= 0.492734 acc= 0.908169 cost_time= 0.316530s \n",
      "Epoch:   2180 loss= 0.244304 acc= 0.950534 cost_time= 0.310466s \n",
      "Epoch:   2181 loss= 0.365344 acc= 0.933307 cost_time= 0.314435s \n",
      "Epoch:   2182 loss= 0.250696 acc= 0.942319 cost_time= 0.311423s \n",
      "Epoch:   2183 loss= 0.267912 acc= 0.948767 cost_time= 0.310440s \n",
      "Epoch:   2184 loss= 0.580653 acc= 0.890278 cost_time= 0.337267s \n",
      "Epoch:   2185 loss= 1.105481 acc= 0.894212 cost_time= 0.315996s \n",
      "Epoch:   2186 loss= 0.376495 acc= 0.920101 cost_time= 0.323755s \n",
      "Epoch:   2187 loss= 0.372432 acc= 0.926583 cost_time= 0.310143s \n",
      "Epoch:   2188 loss= 0.255940 acc= 0.944011 cost_time= 0.315236s \n",
      "Epoch:   2189 loss= 0.629826 acc= 0.909955 cost_time= 0.313016s \n",
      "Epoch:   2190 loss= 0.651282 acc= 0.835636 cost_time= 0.307448s \n",
      "Epoch:   2191 loss= 0.337152 acc= 0.933788 cost_time= 0.313624s \n",
      "Epoch:   2192 loss= 0.359816 acc= 0.935945 cost_time= 0.309566s \n",
      "Epoch:   2193 loss= 0.334391 acc= 0.932313 cost_time= 0.310931s \n",
      "Epoch:   2194 loss= 0.458357 acc= 0.906544 cost_time= 0.310380s \n",
      "Epoch:   2195 loss= 0.348563 acc= 0.927318 cost_time= 0.311956s \n",
      "Epoch:   2196 loss= 1.130188 acc= 0.870136 cost_time= 0.311094s \n",
      "Epoch:   2197 loss= 0.290709 acc= 0.930550 cost_time= 0.313349s \n",
      "Epoch:   2198 loss= 0.337106 acc= 0.926694 cost_time= 0.331511s \n",
      "Epoch:   2199 loss= 0.241930 acc= 0.950350 cost_time= 0.311788s \n",
      "Epoch:   2200 loss= 1.272022 acc= 0.857188 cost_time= 0.321839s \n",
      "Epoch:   2201 loss= 0.318669 acc= 0.933288 cost_time= 0.308990s \n",
      "Epoch:   2202 loss= 0.301893 acc= 0.944098 cost_time= 0.330073s \n",
      "Epoch:   2203 loss= 1.252346 acc= 0.875049 cost_time= 0.326520s \n",
      "Epoch:   2204 loss= 0.576977 acc= 0.869224 cost_time= 0.308678s \n",
      "Epoch:   2205 loss= 0.798723 acc= 0.885351 cost_time= 0.333095s \n",
      "Epoch:   2206 loss= 0.363424 acc= 0.926944 cost_time= 0.310506s \n",
      "Epoch:   2207 loss= 0.227303 acc= 0.946065 cost_time= 0.331412s \n",
      "Epoch:   2208 loss= 0.268577 acc= 0.947709 cost_time= 0.313711s \n",
      "Epoch:   2209 loss= 0.527262 acc= 0.889670 cost_time= 0.310179s \n",
      "Epoch:   2210 loss= 0.299737 acc= 0.957000 cost_time= 0.311471s \n",
      "Epoch:   2211 loss= 0.388232 acc= 0.941931 cost_time= 0.308315s \n",
      "Epoch:   2212 loss= 0.280455 acc= 0.936711 cost_time= 0.333623s \n",
      "Epoch:   2213 loss= 0.522879 acc= 0.888716 cost_time= 0.310519s \n",
      "Epoch:   2214 loss= 0.457778 acc= 0.910507 cost_time= 0.327086s \n",
      "Epoch:   2215 loss= 0.341051 acc= 0.942206 cost_time= 0.309208s \n",
      "Epoch:   2216 loss= 0.415099 acc= 0.935812 cost_time= 0.313117s \n",
      "Epoch:   2217 loss= 0.662835 acc= 0.876813 cost_time= 0.304753s \n",
      "Epoch:   2218 loss= 0.491689 acc= 0.917860 cost_time= 0.305054s \n",
      "Epoch:   2219 loss= 0.372481 acc= 0.931334 cost_time= 0.311583s \n",
      "Epoch:   2220 loss= 0.243730 acc= 0.947782 cost_time= 0.306916s \n",
      "Epoch:   2221 loss= 0.388668 acc= 0.928742 cost_time= 0.331195s \n",
      "Epoch:   2222 loss= 0.292817 acc= 0.949684 cost_time= 0.307940s \n",
      "Epoch:   2223 loss= 0.336232 acc= 0.939839 cost_time= 0.319568s \n",
      "Epoch:   2224 loss= 0.305787 acc= 0.949784 cost_time= 0.308607s \n",
      "Epoch:   2225 loss= 0.281870 acc= 0.938391 cost_time= 0.309068s \n",
      "Epoch:   2226 loss= 0.557792 acc= 0.902248 cost_time= 0.311285s \n",
      "Epoch:   2227 loss= 0.361933 acc= 0.938374 cost_time= 0.312085s \n",
      "Epoch:   2228 loss= 0.588349 acc= 0.907609 cost_time= 0.332697s \n",
      "Epoch:   2229 loss= 0.636494 acc= 0.902352 cost_time= 0.314313s \n",
      "Epoch:   2230 loss= 0.418078 acc= 0.910447 cost_time= 0.310804s \n",
      "Epoch:   2231 loss= 0.239251 acc= 0.955457 cost_time= 0.309885s \n",
      "Epoch:   2232 loss= 0.333255 acc= 0.931143 cost_time= 0.309334s \n",
      "Epoch:   2233 loss= 0.931279 acc= 0.849735 cost_time= 0.305850s \n",
      "Epoch:   2234 loss= 0.253019 acc= 0.947568 cost_time= 0.312480s \n",
      "Epoch:   2235 loss= 0.361552 acc= 0.934361 cost_time= 0.327349s \n",
      "Epoch:   2236 loss= 0.391765 acc= 0.926987 cost_time= 0.305481s \n",
      "Epoch:   2237 loss= 0.754114 acc= 0.917475 cost_time= 0.318603s \n",
      "Epoch:   2238 loss= 0.302654 acc= 0.931774 cost_time= 0.314415s \n",
      "Epoch:   2239 loss= 0.395087 acc= 0.917511 cost_time= 0.310856s \n",
      "Epoch:   2240 loss= 0.936844 acc= 0.873901 cost_time= 0.310971s \n",
      "Epoch:   2241 loss= 0.345495 acc= 0.920742 cost_time= 0.308771s \n",
      "Epoch:   2242 loss= 0.287538 acc= 0.943922 cost_time= 0.308274s \n",
      "Epoch:   2243 loss= 0.480949 acc= 0.886846 cost_time= 0.312430s \n",
      "Epoch:   2244 loss= 0.567458 acc= 0.918720 cost_time= 0.311402s \n",
      "Epoch:   2245 loss= 0.402906 acc= 0.925249 cost_time= 0.308712s \n",
      "Epoch:   2246 loss= 0.440376 acc= 0.922755 cost_time= 0.316298s \n",
      "Epoch:   2247 loss= 0.285149 acc= 0.937789 cost_time= 0.309383s \n",
      "Epoch:   2248 loss= 0.233597 acc= 0.957947 cost_time= 0.314815s \n",
      "Epoch:   2249 loss= 0.336960 acc= 0.934650 cost_time= 0.327916s \n",
      "Epoch:   2250 loss= 0.250728 acc= 0.951072 cost_time= 0.316463s \n",
      "Epoch:   2251 loss= 0.369662 acc= 0.931199 cost_time= 0.308600s \n",
      "Epoch:   2252 loss= 0.368909 acc= 0.935042 cost_time= 0.312120s \n",
      "Epoch:   2253 loss= 0.384554 acc= 0.928846 cost_time= 0.315139s \n",
      "Epoch:   2254 loss= 0.417750 acc= 0.910147 cost_time= 0.316099s \n",
      "Epoch:   2255 loss= 0.365534 acc= 0.932708 cost_time= 0.309011s \n",
      "Epoch:   2256 loss= 0.253420 acc= 0.955824 cost_time= 0.333109s \n",
      "Epoch:   2257 loss= 0.261861 acc= 0.941443 cost_time= 0.314198s \n",
      "Epoch:   2258 loss= 0.409261 acc= 0.929283 cost_time= 0.309500s \n",
      "Epoch:   2259 loss= 1.397432 acc= 0.845900 cost_time= 0.307154s \n",
      "Epoch:   2260 loss= 0.401566 acc= 0.928144 cost_time= 0.314137s \n",
      "Epoch:   2261 loss= 0.369588 acc= 0.928508 cost_time= 0.330980s \n",
      "Epoch:   2262 loss= 0.323064 acc= 0.940030 cost_time= 0.312747s \n",
      "Epoch:   2263 loss= 0.480481 acc= 0.906649 cost_time= 0.330322s \n",
      "Epoch:   2264 loss= 0.536720 acc= 0.917612 cost_time= 0.312981s \n",
      "Epoch:   2265 loss= 0.333657 acc= 0.930277 cost_time= 0.305073s \n",
      "Epoch:   2266 loss= 0.279809 acc= 0.951363 cost_time= 0.319715s \n",
      "Epoch:   2267 loss= 0.419250 acc= 0.923346 cost_time= 0.308297s \n",
      "Epoch:   2268 loss= 0.376556 acc= 0.930383 cost_time= 0.310144s \n",
      "Epoch:   2269 loss= 0.388988 acc= 0.927571 cost_time= 0.307886s \n",
      "Epoch:   2270 loss= 0.326196 acc= 0.929327 cost_time= 0.330449s \n",
      "Epoch:   2271 loss= 0.317163 acc= 0.933820 cost_time= 0.311617s \n",
      "Epoch:   2272 loss= 0.791868 acc= 0.900358 cost_time= 0.309831s \n",
      "Epoch:   2273 loss= 0.360930 acc= 0.929831 cost_time= 0.312921s \n",
      "Epoch:   2274 loss= 0.252415 acc= 0.945935 cost_time= 0.314101s \n",
      "Epoch:   2275 loss= 0.941426 acc= 0.901501 cost_time= 0.313648s \n",
      "Epoch:   2276 loss= 0.495071 acc= 0.894327 cost_time= 0.307848s \n",
      "Epoch:   2277 loss= 0.306494 acc= 0.950903 cost_time= 0.312234s \n",
      "Epoch:   2278 loss= 0.473103 acc= 0.925150 cost_time= 0.337038s \n",
      "Epoch:   2279 loss= 0.322577 acc= 0.938841 cost_time= 0.316870s \n",
      "Epoch:   2280 loss= 0.339787 acc= 0.948416 cost_time= 0.319423s \n",
      "Epoch:   2281 loss= 0.275321 acc= 0.936684 cost_time= 0.312839s \n",
      "Epoch:   2282 loss= 0.299154 acc= 0.924439 cost_time= 0.311228s \n",
      "Epoch:   2283 loss= 0.543611 acc= 0.922333 cost_time= 0.312372s \n",
      "Epoch:   2284 loss= 0.453857 acc= 0.909788 cost_time= 0.313180s \n",
      "Epoch:   2285 loss= 0.298788 acc= 0.944603 cost_time= 0.312921s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2286 loss= 0.326118 acc= 0.930624 cost_time= 0.314566s \n",
      "Epoch:   2287 loss= 0.328246 acc= 0.933457 cost_time= 0.313396s \n",
      "Epoch:   2288 loss= 0.494498 acc= 0.907449 cost_time= 0.311164s \n",
      "Epoch:   2289 loss= 0.635170 acc= 0.846417 cost_time= 0.307903s \n",
      "Epoch:   2290 loss= 0.300016 acc= 0.948935 cost_time= 0.313035s \n",
      "Epoch:   2291 loss= 0.774372 acc= 0.896758 cost_time= 0.333036s \n",
      "Epoch:   2292 loss= 1.365971 acc= 0.884497 cost_time= 0.314174s \n",
      "Epoch:   2293 loss= 0.348690 acc= 0.935482 cost_time= 0.318093s \n",
      "Epoch:   2294 loss= 0.661880 acc= 0.902030 cost_time= 0.311130s \n",
      "Epoch:   2295 loss= 0.516815 acc= 0.893083 cost_time= 0.311253s \n",
      "Epoch:   2296 loss= 0.646447 acc= 0.916792 cost_time= 0.310505s \n",
      "Epoch:   2297 loss= 0.860861 acc= 0.912818 cost_time= 0.310433s \n",
      "Epoch:   2298 loss= 0.557272 acc= 0.891475 cost_time= 0.324987s \n",
      "Epoch:   2299 loss= 0.517772 acc= 0.908910 cost_time= 0.320348s \n",
      "Epoch:   2300 loss= 0.410333 acc= 0.935158 cost_time= 0.316940s \n",
      "Epoch:   2301 loss= 0.340637 acc= 0.935935 cost_time= 0.309388s \n",
      "Epoch:   2302 loss= 0.233891 acc= 0.948472 cost_time= 0.315906s \n",
      "Epoch:   2303 loss= 0.547413 acc= 0.890855 cost_time= 0.308160s \n",
      "Epoch:   2304 loss= 0.236112 acc= 0.961428 cost_time= 0.308763s \n",
      "Epoch:   2305 loss= 0.385382 acc= 0.939554 cost_time= 0.308208s \n",
      "Epoch:   2306 loss= 0.361243 acc= 0.941763 cost_time= 0.309618s \n",
      "Epoch:   2307 loss= 0.353506 acc= 0.930832 cost_time= 0.318720s \n",
      "Epoch:   2308 loss= 0.616279 acc= 0.901655 cost_time= 0.317385s \n",
      "Epoch:   2309 loss= 1.645446 acc= 0.888261 cost_time= 0.311108s \n",
      "Epoch:   2310 loss= 0.583917 acc= 0.909286 cost_time= 0.308853s \n",
      "Epoch:   2311 loss= 1.029010 acc= 0.889523 cost_time= 0.311663s \n",
      "Epoch:   2312 loss= 0.295203 acc= 0.943213 cost_time= 0.308637s \n",
      "Epoch:   2313 loss= 0.372297 acc= 0.931763 cost_time= 0.310565s \n",
      "Epoch:   2314 loss= 0.419870 acc= 0.918564 cost_time= 0.311197s \n",
      "Epoch:   2315 loss= 0.505329 acc= 0.887014 cost_time= 0.308439s \n",
      "Epoch:   2316 loss= 0.327417 acc= 0.933742 cost_time= 0.311833s \n",
      "Epoch:   2317 loss= 0.254922 acc= 0.950417 cost_time= 0.310615s \n",
      "Epoch:   2318 loss= 0.384320 acc= 0.934632 cost_time= 0.307355s \n",
      "Epoch:   2319 loss= 0.323282 acc= 0.940868 cost_time= 0.331010s \n",
      "Epoch:   2320 loss= 0.272459 acc= 0.941634 cost_time= 0.316149s \n",
      "Epoch:   2321 loss= 0.648450 acc= 0.879352 cost_time= 0.307584s \n",
      "Epoch:   2322 loss= 0.937147 acc= 0.819305 cost_time= 0.307402s \n",
      "Epoch:   2323 loss= 0.248496 acc= 0.944160 cost_time= 0.308803s \n",
      "Epoch:   2324 loss= 0.632904 acc= 0.918060 cost_time= 0.313654s \n",
      "Epoch:   2325 loss= 0.288917 acc= 0.942487 cost_time= 0.308133s \n",
      "Epoch:   2326 loss= 0.653380 acc= 0.893553 cost_time= 0.309834s \n",
      "Epoch:   2327 loss= 0.248347 acc= 0.941940 cost_time= 0.332229s \n",
      "Epoch:   2328 loss= 0.403790 acc= 0.935259 cost_time= 0.308033s \n",
      "Epoch:   2329 loss= 0.313782 acc= 0.957673 cost_time= 0.313791s \n",
      "Epoch:   2330 loss= 0.410523 acc= 0.920281 cost_time= 0.311507s \n",
      "Epoch:   2331 loss= 0.407654 acc= 0.919289 cost_time= 0.327762s \n",
      "Epoch:   2332 loss= 0.478379 acc= 0.913514 cost_time= 0.310477s \n",
      "Epoch:   2333 loss= 0.295703 acc= 0.939666 cost_time= 0.332010s \n",
      "Epoch:   2334 loss= 0.857513 acc= 0.902413 cost_time= 0.312201s \n",
      "Epoch:   2335 loss= 1.117285 acc= 0.882701 cost_time= 0.319086s \n",
      "Epoch:   2336 loss= 0.289973 acc= 0.942305 cost_time= 0.311643s \n",
      "Epoch:   2337 loss= 0.319850 acc= 0.944806 cost_time= 0.310138s \n",
      "Epoch:   2338 loss= 0.570189 acc= 0.891093 cost_time= 0.312579s \n",
      "Epoch:   2339 loss= 0.338633 acc= 0.940358 cost_time= 0.310825s \n",
      "Epoch:   2340 loss= 0.233966 acc= 0.951089 cost_time= 0.332110s \n",
      "Epoch:   2341 loss= 0.862091 acc= 0.877393 cost_time= 0.316388s \n",
      "Epoch:   2342 loss= 0.297095 acc= 0.954436 cost_time= 0.317092s \n",
      "Epoch:   2343 loss= 0.715788 acc= 0.900084 cost_time= 0.311903s \n",
      "Epoch:   2344 loss= 1.177588 acc= 0.871332 cost_time= 0.312258s \n",
      "Epoch:   2345 loss= 0.344125 acc= 0.949573 cost_time= 0.312842s \n",
      "Epoch:   2346 loss= 0.558142 acc= 0.891237 cost_time= 0.308688s \n",
      "Epoch:   2347 loss= 0.271319 acc= 0.942720 cost_time= 0.330793s \n",
      "Epoch:   2348 loss= 0.404881 acc= 0.930758 cost_time= 0.313777s \n",
      "Epoch:   2349 loss= 0.286821 acc= 0.946525 cost_time= 0.311686s \n",
      "Epoch:   2350 loss= 0.322434 acc= 0.940509 cost_time= 0.309379s \n",
      "Epoch:   2351 loss= 0.430541 acc= 0.925699 cost_time= 0.312452s \n",
      "Epoch:   2352 loss= 0.269878 acc= 0.950795 cost_time= 0.311671s \n",
      "Epoch:   2353 loss= 0.776956 acc= 0.808086 cost_time= 0.301414s \n",
      "Epoch:   2354 loss= 0.287896 acc= 0.947441 cost_time= 0.331290s \n",
      "Epoch:   2355 loss= 0.460413 acc= 0.923578 cost_time= 0.310172s \n",
      "Epoch:   2356 loss= 0.280389 acc= 0.943065 cost_time= 0.325939s \n",
      "Epoch:   2357 loss= 0.561120 acc= 0.935155 cost_time= 0.309992s \n",
      "Epoch:   2358 loss= 0.276007 acc= 0.955148 cost_time= 0.308758s \n",
      "Epoch:   2359 loss= 0.595253 acc= 0.897589 cost_time= 0.310081s \n",
      "Epoch:   2360 loss= 0.549193 acc= 0.876039 cost_time= 0.305935s \n",
      "Epoch:   2361 loss= 0.364769 acc= 0.921084 cost_time= 0.310941s \n",
      "Epoch:   2362 loss= 0.333820 acc= 0.942429 cost_time= 0.314999s \n",
      "Epoch:   2363 loss= 0.290116 acc= 0.936589 cost_time= 0.315210s \n",
      "Epoch:   2364 loss= 0.353693 acc= 0.931077 cost_time= 0.309969s \n",
      "Epoch:   2365 loss= 0.353368 acc= 0.932508 cost_time= 0.315578s \n",
      "Epoch:   2366 loss= 0.942956 acc= 0.880794 cost_time= 0.311548s \n",
      "Epoch:   2367 loss= 2.059680 acc= 0.843436 cost_time= 0.313596s \n",
      "Epoch:   2368 loss= 0.361706 acc= 0.940412 cost_time= 0.333567s \n",
      "Epoch:   2369 loss= 0.319236 acc= 0.937706 cost_time= 0.311647s \n",
      "Epoch:   2370 loss= 0.931299 acc= 0.903950 cost_time= 0.316563s \n",
      "Epoch:   2371 loss= 0.644820 acc= 0.878485 cost_time= 0.305684s \n",
      "Epoch:   2372 loss= 1.763995 acc= 0.862730 cost_time= 0.309878s \n",
      "Epoch:   2373 loss= 0.445919 acc= 0.911941 cost_time= 0.314933s \n",
      "Epoch:   2374 loss= 0.377401 acc= 0.918081 cost_time= 0.315572s \n",
      "Epoch:   2375 loss= 0.363719 acc= 0.931359 cost_time= 0.314704s \n",
      "Epoch:   2376 loss= 0.322241 acc= 0.941926 cost_time= 0.311182s \n",
      "Epoch:   2377 loss= 0.407593 acc= 0.930363 cost_time= 0.313924s \n",
      "Epoch:   2378 loss= 0.526199 acc= 0.899872 cost_time= 0.309352s \n",
      "Epoch:   2379 loss= 0.702183 acc= 0.862476 cost_time= 0.305464s \n",
      "Epoch:   2380 loss= 0.288825 acc= 0.930590 cost_time= 0.332117s \n",
      "Epoch:   2381 loss= 0.383993 acc= 0.929579 cost_time= 0.314557s \n",
      "Epoch:   2382 loss= 0.319312 acc= 0.935761 cost_time= 0.314207s \n",
      "Epoch:   2383 loss= 0.353803 acc= 0.941293 cost_time= 0.309879s \n",
      "Epoch:   2384 loss= 0.414966 acc= 0.916350 cost_time= 0.316284s \n",
      "Epoch:   2385 loss= 0.314522 acc= 0.943939 cost_time= 0.311098s \n",
      "Epoch:   2386 loss= 0.449063 acc= 0.894844 cost_time= 0.305928s \n",
      "Epoch:   2387 loss= 0.414325 acc= 0.928377 cost_time= 0.307967s \n",
      "Epoch:   2388 loss= 0.846448 acc= 0.854309 cost_time= 0.306599s \n",
      "Epoch:   2389 loss= 0.590737 acc= 0.915205 cost_time= 0.329991s \n",
      "Epoch:   2390 loss= 0.466286 acc= 0.917195 cost_time= 0.318199s \n",
      "Epoch:   2391 loss= 0.476784 acc= 0.916671 cost_time= 0.310916s \n",
      "Epoch:   2392 loss= 0.831635 acc= 0.820520 cost_time= 0.302486s \n",
      "Epoch:   2393 loss= 0.296302 acc= 0.935766 cost_time= 0.311503s \n",
      "Epoch:   2394 loss= 0.292727 acc= 0.938251 cost_time= 0.331801s \n",
      "Epoch:   2395 loss= 0.503807 acc= 0.880101 cost_time= 0.310696s \n",
      "Epoch:   2396 loss= 0.476962 acc= 0.917878 cost_time= 0.309771s \n",
      "Epoch:   2397 loss= 0.321084 acc= 0.931444 cost_time= 0.311299s \n",
      "Epoch:   2398 loss= 0.215483 acc= 0.956416 cost_time= 0.318916s \n",
      "Epoch:   2399 loss= 0.269309 acc= 0.945597 cost_time= 0.315562s \n",
      "Epoch:   2400 loss= 0.281502 acc= 0.943212 cost_time= 0.309257s \n",
      "Epoch:   2401 loss= 1.365021 acc= 0.884247 cost_time= 0.308702s \n",
      "Epoch:   2402 loss= 0.474472 acc= 0.894449 cost_time= 0.303856s \n",
      "Epoch:   2403 loss= 0.355439 acc= 0.926550 cost_time= 0.316722s \n",
      "Epoch:   2404 loss= 0.267680 acc= 0.946154 cost_time= 0.310938s \n",
      "Epoch:   2405 loss= 1.433899 acc= 0.833874 cost_time= 0.306781s \n",
      "Epoch:   2406 loss= 0.413105 acc= 0.928890 cost_time= 0.327673s \n",
      "Epoch:   2407 loss= 0.310941 acc= 0.952008 cost_time= 0.311726s \n",
      "Epoch:   2408 loss= 0.262580 acc= 0.940652 cost_time= 0.312000s \n",
      "Epoch:   2409 loss= 0.323659 acc= 0.921491 cost_time= 0.314355s \n",
      "Epoch:   2410 loss= 0.832359 acc= 0.846528 cost_time= 0.310053s \n",
      "Epoch:   2411 loss= 0.276579 acc= 0.945313 cost_time= 0.309832s \n",
      "Epoch:   2412 loss= 0.296410 acc= 0.934127 cost_time= 0.329853s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2413 loss= 0.413762 acc= 0.934954 cost_time= 0.309932s \n",
      "Epoch:   2414 loss= 2.799333 acc= 0.835923 cost_time= 0.313009s \n",
      "Epoch:   2415 loss= 0.604501 acc= 0.870053 cost_time= 0.311962s \n",
      "Epoch:   2416 loss= 0.319851 acc= 0.944281 cost_time= 0.306865s \n",
      "Epoch:   2417 loss= 1.950765 acc= 0.827171 cost_time= 0.327268s \n",
      "Epoch:   2418 loss= 0.264614 acc= 0.959622 cost_time= 0.314366s \n",
      "Epoch:   2419 loss= 1.018416 acc= 0.838619 cost_time= 0.328694s \n",
      "Epoch:   2420 loss= 0.538962 acc= 0.896154 cost_time= 0.305540s \n",
      "Epoch:   2421 loss= 1.032006 acc= 0.849884 cost_time= 0.306774s \n",
      "Epoch:   2422 loss= 0.543644 acc= 0.881734 cost_time= 0.308121s \n",
      "Epoch:   2423 loss= 0.301009 acc= 0.934450 cost_time= 0.313356s \n",
      "Epoch:   2424 loss= 0.723477 acc= 0.890597 cost_time= 0.331016s \n",
      "Epoch:   2425 loss= 0.318405 acc= 0.941778 cost_time= 0.319287s \n",
      "Epoch:   2426 loss= 0.567473 acc= 0.882693 cost_time= 0.309212s \n",
      "Epoch:   2427 loss= 0.646316 acc= 0.869057 cost_time= 0.310204s \n",
      "Epoch:   2428 loss= 0.302119 acc= 0.938149 cost_time= 0.316056s \n",
      "Epoch:   2429 loss= 0.385704 acc= 0.922137 cost_time= 0.315117s \n",
      "Epoch:   2430 loss= 0.390969 acc= 0.916110 cost_time= 0.311081s \n",
      "Epoch:   2431 loss= 0.351194 acc= 0.921577 cost_time= 0.308585s \n",
      "Epoch:   2432 loss= 0.594272 acc= 0.914151 cost_time= 0.311260s \n",
      "Epoch:   2433 loss= 0.308414 acc= 0.947672 cost_time= 0.332500s \n",
      "Epoch:   2434 loss= 1.112499 acc= 0.866347 cost_time= 0.313641s \n",
      "Epoch:   2435 loss= 0.413970 acc= 0.928003 cost_time= 0.310084s \n",
      "Epoch:   2436 loss= 0.429176 acc= 0.897553 cost_time= 0.309808s \n",
      "Epoch:   2437 loss= 0.423912 acc= 0.940534 cost_time= 0.315822s \n",
      "Epoch:   2438 loss= 0.272326 acc= 0.959301 cost_time= 0.331401s \n",
      "Epoch:   2439 loss= 0.423142 acc= 0.913254 cost_time= 0.309782s \n",
      "Epoch:   2440 loss= 0.448796 acc= 0.909784 cost_time= 0.306619s \n",
      "Epoch:   2441 loss= 0.511641 acc= 0.861899 cost_time= 0.303051s \n",
      "Epoch:   2442 loss= 0.257481 acc= 0.947005 cost_time= 0.316414s \n",
      "Epoch:   2443 loss= 1.648808 acc= 0.841838 cost_time= 0.305393s \n",
      "Epoch:   2444 loss= 0.323353 acc= 0.943448 cost_time= 0.310518s \n",
      "Epoch:   2445 loss= 0.573780 acc= 0.870882 cost_time= 0.308760s \n",
      "Epoch:   2446 loss= 0.307585 acc= 0.937535 cost_time= 0.313301s \n",
      "Epoch:   2447 loss= 0.580293 acc= 0.881723 cost_time= 0.307768s \n",
      "Epoch:   2448 loss= 0.289730 acc= 0.940466 cost_time= 0.313474s \n",
      "Epoch:   2449 loss= 0.333740 acc= 0.937451 cost_time= 0.312636s \n",
      "Epoch:   2450 loss= 0.466309 acc= 0.921729 cost_time= 0.325552s \n",
      "Epoch:   2451 loss= 0.577447 acc= 0.879448 cost_time= 0.302880s \n",
      "Epoch:   2452 loss= 0.306638 acc= 0.946724 cost_time= 0.312894s \n",
      "Epoch:   2453 loss= 0.632547 acc= 0.907964 cost_time= 0.309707s \n",
      "Epoch:   2454 loss= 0.757345 acc= 0.881743 cost_time= 0.331926s \n",
      "Epoch:   2455 loss= 0.345182 acc= 0.942753 cost_time= 0.321258s \n",
      "Epoch:   2456 loss= 0.271707 acc= 0.954522 cost_time= 0.322719s \n",
      "Epoch:   2457 loss= 0.374494 acc= 0.918652 cost_time= 0.314484s \n",
      "Epoch:   2458 loss= 0.368232 acc= 0.915435 cost_time= 0.306373s \n",
      "Epoch:   2459 loss= 0.272541 acc= 0.944566 cost_time= 0.317256s \n",
      "Epoch:   2460 loss= 0.248220 acc= 0.945122 cost_time= 0.312479s \n",
      "Epoch:   2461 loss= 0.260593 acc= 0.957803 cost_time= 0.314725s \n",
      "Epoch:   2462 loss= 0.387514 acc= 0.928543 cost_time= 0.322745s \n",
      "Epoch:   2463 loss= 0.481924 acc= 0.887704 cost_time= 0.322667s \n",
      "Epoch:   2464 loss= 0.320521 acc= 0.942122 cost_time= 0.316496s \n",
      "Epoch:   2465 loss= 1.143522 acc= 0.851442 cost_time= 0.311743s \n",
      "Epoch:   2466 loss= 0.241301 acc= 0.954904 cost_time= 0.316499s \n",
      "Epoch:   2467 loss= 0.306257 acc= 0.937769 cost_time= 0.310557s \n",
      "Epoch:   2468 loss= 0.373969 acc= 0.898378 cost_time= 0.310019s \n",
      "Epoch:   2469 loss= 0.520510 acc= 0.888220 cost_time= 0.305796s \n",
      "Epoch:   2470 loss= 0.355652 acc= 0.950184 cost_time= 0.307394s \n",
      "Epoch:   2471 loss= 0.287864 acc= 0.943515 cost_time= 0.311766s \n",
      "Epoch:   2472 loss= 0.509183 acc= 0.913587 cost_time= 0.316516s \n",
      "Epoch:   2473 loss= 0.465258 acc= 0.922862 cost_time= 0.330708s \n",
      "Epoch:   2474 loss= 1.230594 acc= 0.879845 cost_time= 0.305953s \n",
      "Epoch:   2475 loss= 0.538792 acc= 0.893668 cost_time= 0.309701s \n",
      "Epoch:   2476 loss= 0.886766 acc= 0.897066 cost_time= 0.311302s \n",
      "Epoch:   2477 loss= 0.252376 acc= 0.950573 cost_time= 0.311814s \n",
      "Epoch:   2478 loss= 0.776118 acc= 0.918972 cost_time= 0.315570s \n",
      "Epoch:   2479 loss= 0.283428 acc= 0.939942 cost_time= 0.307703s \n",
      "Epoch:   2480 loss= 1.020808 acc= 0.878974 cost_time= 0.327527s \n",
      "Epoch:   2481 loss= 0.320579 acc= 0.949276 cost_time= 0.308581s \n",
      "Epoch:   2482 loss= 0.342250 acc= 0.917753 cost_time= 0.315857s \n",
      "Epoch:   2483 loss= 0.263063 acc= 0.946970 cost_time= 0.311646s \n",
      "Epoch:   2484 loss= 0.333723 acc= 0.933129 cost_time= 0.316114s \n",
      "Epoch:   2485 loss= 0.313405 acc= 0.942766 cost_time= 0.313699s \n",
      "Epoch:   2486 loss= 0.756306 acc= 0.906601 cost_time= 0.314526s \n",
      "Epoch:   2487 loss= 0.436227 acc= 0.906154 cost_time= 0.307568s \n",
      "Epoch:   2488 loss= 0.764182 acc= 0.900177 cost_time= 0.315520s \n",
      "Epoch:   2489 loss= 0.266936 acc= 0.937352 cost_time= 0.330813s \n",
      "Epoch:   2490 loss= 0.315546 acc= 0.940661 cost_time= 0.305055s \n",
      "Epoch:   2491 loss= 0.395107 acc= 0.926363 cost_time= 0.316074s \n",
      "Epoch:   2492 loss= 1.028611 acc= 0.885213 cost_time= 0.307530s \n",
      "Epoch:   2493 loss= 0.395945 acc= 0.921221 cost_time= 0.307899s \n",
      "Epoch:   2494 loss= 0.390805 acc= 0.915636 cost_time= 0.310692s \n",
      "Epoch:   2495 loss= 0.244268 acc= 0.942818 cost_time= 0.313851s \n",
      "Epoch:   2496 loss= 0.285474 acc= 0.931783 cost_time= 0.333314s \n",
      "Epoch:   2497 loss= 0.739933 acc= 0.862565 cost_time= 0.310430s \n",
      "Epoch:   2498 loss= 0.466594 acc= 0.895860 cost_time= 0.306033s \n",
      "Epoch:   2499 loss= 0.252751 acc= 0.947184 cost_time= 0.309390s \n",
      "Epoch:   2500 loss= 0.629643 acc= 0.874979 cost_time= 0.311496s \n",
      "Epoch:   2501 loss= 0.706876 acc= 0.919893 cost_time= 0.311455s \n",
      "Epoch:   2502 loss= 0.508921 acc= 0.906995 cost_time= 0.303526s \n",
      "Epoch:   2503 loss= 0.352707 acc= 0.928179 cost_time= 0.328315s \n",
      "Epoch:   2504 loss= 0.413478 acc= 0.901959 cost_time= 0.307270s \n",
      "Epoch:   2505 loss= 0.510525 acc= 0.932596 cost_time= 0.324144s \n",
      "Epoch:   2506 loss= 0.282948 acc= 0.946008 cost_time= 0.311825s \n",
      "Epoch:   2507 loss= 1.560137 acc= 0.877266 cost_time= 0.311184s \n",
      "Epoch:   2508 loss= 0.360161 acc= 0.939758 cost_time= 0.310740s \n",
      "Epoch:   2509 loss= 0.521807 acc= 0.889176 cost_time= 0.304027s \n",
      "Epoch:   2510 loss= 0.302198 acc= 0.943916 cost_time= 0.307514s \n",
      "Epoch:   2511 loss= 0.381036 acc= 0.916621 cost_time= 0.310803s \n",
      "Epoch:   2512 loss= 0.418542 acc= 0.923341 cost_time= 0.324370s \n",
      "Epoch:   2513 loss= 0.360952 acc= 0.929412 cost_time= 0.309727s \n",
      "Epoch:   2514 loss= 0.442186 acc= 0.922175 cost_time= 0.316989s \n",
      "Epoch:   2515 loss= 0.545707 acc= 0.884427 cost_time= 0.306860s \n",
      "Epoch:   2516 loss= 0.429424 acc= 0.917222 cost_time= 0.306968s \n",
      "Epoch:   2517 loss= 0.533444 acc= 0.897416 cost_time= 0.306341s \n",
      "Epoch:   2518 loss= 0.348616 acc= 0.934090 cost_time= 0.308439s \n",
      "Epoch:   2519 loss= 0.533450 acc= 0.874436 cost_time= 0.310095s \n",
      "Epoch:   2520 loss= 0.286176 acc= 0.938250 cost_time= 0.312728s \n",
      "Epoch:   2521 loss= 0.507653 acc= 0.916065 cost_time= 0.309163s \n",
      "Epoch:   2522 loss= 0.423014 acc= 0.916071 cost_time= 0.307656s \n",
      "Epoch:   2523 loss= 0.303254 acc= 0.949547 cost_time= 0.312054s \n",
      "Epoch:   2524 loss= 0.293392 acc= 0.934791 cost_time= 0.334822s \n",
      "Epoch:   2525 loss= 0.462115 acc= 0.908218 cost_time= 0.308892s \n",
      "Epoch:   2526 loss= 0.368949 acc= 0.918168 cost_time= 0.324299s \n",
      "Epoch:   2527 loss= 1.193954 acc= 0.832823 cost_time= 0.305069s \n",
      "Epoch:   2528 loss= 0.588853 acc= 0.905964 cost_time= 0.307828s \n",
      "Epoch:   2529 loss= 1.386713 acc= 0.836502 cost_time= 0.308237s \n",
      "Epoch:   2530 loss= 0.310558 acc= 0.929146 cost_time= 0.309554s \n",
      "Epoch:   2531 loss= 0.286802 acc= 0.949334 cost_time= 0.312048s \n",
      "Epoch:   2532 loss= 0.900691 acc= 0.896923 cost_time= 0.309913s \n",
      "Epoch:   2533 loss= 0.542321 acc= 0.913088 cost_time= 0.309378s \n",
      "Epoch:   2534 loss= 0.304011 acc= 0.933636 cost_time= 0.330945s \n",
      "Epoch:   2535 loss= 0.487511 acc= 0.921451 cost_time= 0.314284s \n",
      "Epoch:   2536 loss= 0.375987 acc= 0.936700 cost_time= 0.314532s \n",
      "Epoch:   2537 loss= 0.772252 acc= 0.896920 cost_time= 0.307461s \n",
      "Epoch:   2538 loss= 0.532670 acc= 0.910524 cost_time= 0.311660s \n",
      "Epoch:   2539 loss= 0.924820 acc= 0.890210 cost_time= 0.314777s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2540 loss= 0.895410 acc= 0.900924 cost_time= 0.323385s \n",
      "Epoch:   2541 loss= 0.491021 acc= 0.888029 cost_time= 0.312754s \n",
      "Epoch:   2542 loss= 0.267415 acc= 0.952716 cost_time= 0.311526s \n",
      "Epoch:   2543 loss= 0.253370 acc= 0.952045 cost_time= 0.316893s \n",
      "Epoch:   2544 loss= 1.133401 acc= 0.865105 cost_time= 0.307333s \n",
      "Epoch:   2545 loss= 0.305054 acc= 0.943886 cost_time= 0.326698s \n",
      "Epoch:   2546 loss= 0.559464 acc= 0.861195 cost_time= 0.311712s \n",
      "Epoch:   2547 loss= 0.378142 acc= 0.922369 cost_time= 0.318705s \n",
      "Epoch:   2548 loss= 0.354187 acc= 0.931898 cost_time= 0.309062s \n",
      "Epoch:   2549 loss= 0.427789 acc= 0.898335 cost_time= 0.308566s \n",
      "Epoch:   2550 loss= 0.508882 acc= 0.883012 cost_time= 0.309135s \n",
      "Epoch:   2551 loss= 1.320666 acc= 0.851274 cost_time= 0.308520s \n",
      "Epoch:   2552 loss= 0.357160 acc= 0.933787 cost_time= 0.309825s \n",
      "Epoch:   2553 loss= 0.559782 acc= 0.880894 cost_time= 0.306448s \n",
      "Epoch:   2554 loss= 0.232230 acc= 0.944751 cost_time= 0.317143s \n",
      "Epoch:   2555 loss= 0.283456 acc= 0.942115 cost_time= 0.316134s \n",
      "Epoch:   2556 loss= 0.265763 acc= 0.942572 cost_time= 0.310238s \n",
      "Epoch:   2557 loss= 0.354716 acc= 0.922494 cost_time= 0.318272s \n",
      "Epoch:   2558 loss= 0.307150 acc= 0.918792 cost_time= 0.309007s \n",
      "Epoch:   2559 loss= 0.651291 acc= 0.893166 cost_time= 0.330206s \n",
      "Epoch:   2560 loss= 0.522981 acc= 0.893504 cost_time= 0.307067s \n",
      "Epoch:   2561 loss= 0.552791 acc= 0.901461 cost_time= 0.309147s \n",
      "Epoch:   2562 loss= 0.306130 acc= 0.958238 cost_time= 0.312696s \n",
      "Epoch:   2563 loss= 0.512395 acc= 0.903358 cost_time= 0.309783s \n",
      "Epoch:   2564 loss= 0.251463 acc= 0.945906 cost_time= 0.312613s \n",
      "Epoch:   2565 loss= 1.435302 acc= 0.832785 cost_time= 0.305186s \n",
      "Epoch:   2566 loss= 0.384720 acc= 0.915531 cost_time= 0.329710s \n",
      "Epoch:   2567 loss= 0.316556 acc= 0.935858 cost_time= 0.312181s \n",
      "Epoch:   2568 loss= 0.913494 acc= 0.908443 cost_time= 0.314640s \n",
      "Epoch:   2569 loss= 0.631980 acc= 0.909600 cost_time= 0.312943s \n",
      "Epoch:   2570 loss= 0.901704 acc= 0.899091 cost_time= 0.315368s \n",
      "Epoch:   2571 loss= 0.337282 acc= 0.936611 cost_time= 0.315309s \n",
      "Epoch:   2572 loss= 0.439715 acc= 0.900156 cost_time= 0.303843s \n",
      "Epoch:   2573 loss= 0.484628 acc= 0.902263 cost_time= 0.309375s \n",
      "Epoch:   2574 loss= 0.447675 acc= 0.931502 cost_time= 0.316079s \n",
      "Epoch:   2575 loss= 0.295044 acc= 0.944422 cost_time= 0.312487s \n",
      "Epoch:   2576 loss= 0.446288 acc= 0.923187 cost_time= 0.303528s \n",
      "Epoch:   2577 loss= 0.574517 acc= 0.916011 cost_time= 0.310697s \n",
      "Epoch:   2578 loss= 0.455387 acc= 0.928269 cost_time= 0.310176s \n",
      "Epoch:   2579 loss= 0.381040 acc= 0.921255 cost_time= 0.306758s \n",
      "Epoch:   2580 loss= 0.388094 acc= 0.912996 cost_time= 0.306856s \n",
      "Epoch:   2581 loss= 0.345614 acc= 0.931451 cost_time= 0.312315s \n",
      "Epoch:   2582 loss= 0.639942 acc= 0.910805 cost_time= 0.309361s \n",
      "Epoch:   2583 loss= 0.272071 acc= 0.945264 cost_time= 0.330960s \n",
      "Epoch:   2584 loss= 0.554152 acc= 0.886244 cost_time= 0.301031s \n",
      "Epoch:   2585 loss= 0.798981 acc= 0.906131 cost_time= 0.330158s \n",
      "Epoch:   2586 loss= 0.405950 acc= 0.925484 cost_time= 0.305311s \n",
      "Epoch:   2587 loss= 1.698597 acc= 0.836042 cost_time= 0.328881s \n",
      "Epoch:   2588 loss= 0.255053 acc= 0.951127 cost_time= 0.307649s \n",
      "Epoch:   2589 loss= 0.978589 acc= 0.863590 cost_time= 0.317329s \n",
      "Epoch:   2590 loss= 0.359199 acc= 0.933557 cost_time= 0.309963s \n",
      "Epoch:   2591 loss= 0.290448 acc= 0.937613 cost_time= 0.319174s \n",
      "Epoch:   2592 loss= 1.574970 acc= 0.852135 cost_time= 0.317164s \n",
      "Epoch:   2593 loss= 0.337845 acc= 0.934655 cost_time= 0.308689s \n",
      "Epoch:   2594 loss= 0.462763 acc= 0.883693 cost_time= 0.306756s \n",
      "Epoch:   2595 loss= 1.024590 acc= 0.893680 cost_time= 0.316855s \n",
      "Epoch:   2596 loss= 0.470187 acc= 0.890590 cost_time= 0.310252s \n",
      "Epoch:   2597 loss= 1.626010 acc= 0.831232 cost_time= 0.308197s \n",
      "Epoch:   2598 loss= 0.374913 acc= 0.931253 cost_time= 0.307744s \n",
      "Epoch:   2599 loss= 1.187975 acc= 0.905593 cost_time= 0.309757s \n",
      "Epoch:   2600 loss= 0.257668 acc= 0.957233 cost_time= 0.313411s \n",
      "Epoch:   2601 loss= 0.310639 acc= 0.943176 cost_time= 0.310216s \n",
      "Epoch:   2602 loss= 0.404721 acc= 0.942953 cost_time= 0.303857s \n",
      "Epoch:   2603 loss= 0.251609 acc= 0.954204 cost_time= 0.320979s \n",
      "Epoch:   2604 loss= 0.255915 acc= 0.936617 cost_time= 0.309314s \n",
      "Epoch:   2605 loss= 0.289038 acc= 0.948815 cost_time= 0.311080s \n",
      "Epoch:   2606 loss= 0.360248 acc= 0.935818 cost_time= 0.335368s \n",
      "Epoch:   2607 loss= 0.443460 acc= 0.907606 cost_time= 0.307800s \n",
      "Epoch:   2608 loss= 0.944555 acc= 0.884773 cost_time= 0.310210s \n",
      "Epoch:   2609 loss= 0.372630 acc= 0.923389 cost_time= 0.311099s \n",
      "Epoch:   2610 loss= 0.285220 acc= 0.960182 cost_time= 0.317491s \n",
      "Epoch:   2611 loss= 1.170858 acc= 0.901474 cost_time= 0.313689s \n",
      "Epoch:   2612 loss= 0.224547 acc= 0.957470 cost_time= 0.310519s \n",
      "Epoch:   2613 loss= 0.395642 acc= 0.906886 cost_time= 0.305542s \n",
      "Epoch:   2614 loss= 0.338641 acc= 0.945995 cost_time= 0.308223s \n",
      "Epoch:   2615 loss= 0.306181 acc= 0.939320 cost_time= 0.333092s \n",
      "Epoch:   2616 loss= 0.300115 acc= 0.944749 cost_time= 0.308968s \n",
      "Epoch:   2617 loss= 1.195398 acc= 0.847223 cost_time= 0.314825s \n",
      "Epoch:   2618 loss= 0.532918 acc= 0.916897 cost_time= 0.309905s \n",
      "Epoch:   2619 loss= 0.289802 acc= 0.956471 cost_time= 0.314198s \n",
      "Epoch:   2620 loss= 0.281512 acc= 0.956103 cost_time= 0.310828s \n",
      "Epoch:   2621 loss= 0.500290 acc= 0.906995 cost_time= 0.305699s \n",
      "Epoch:   2622 loss= 0.559696 acc= 0.873632 cost_time= 0.308909s \n",
      "Epoch:   2623 loss= 0.282889 acc= 0.944318 cost_time= 0.308529s \n",
      "Epoch:   2624 loss= 0.480880 acc= 0.877023 cost_time= 0.313708s \n",
      "Epoch:   2625 loss= 0.693353 acc= 0.889574 cost_time= 0.308438s \n",
      "Epoch:   2626 loss= 0.247952 acc= 0.937278 cost_time= 0.313449s \n",
      "Epoch:   2627 loss= 0.250935 acc= 0.936401 cost_time= 0.317848s \n",
      "Epoch:   2628 loss= 0.269898 acc= 0.947034 cost_time= 0.311167s \n",
      "Epoch:   2629 loss= 0.266606 acc= 0.946431 cost_time= 0.331950s \n",
      "Epoch:   2630 loss= 0.579484 acc= 0.907132 cost_time= 0.309289s \n",
      "Epoch:   2631 loss= 0.297609 acc= 0.943051 cost_time= 0.322996s \n",
      "Epoch:   2632 loss= 1.239358 acc= 0.813618 cost_time= 0.312270s \n",
      "Epoch:   2633 loss= 0.489335 acc= 0.907651 cost_time= 0.310334s \n",
      "Epoch:   2634 loss= 0.247316 acc= 0.953657 cost_time= 0.315742s \n",
      "Epoch:   2635 loss= 0.290110 acc= 0.948678 cost_time= 0.312800s \n",
      "Epoch:   2636 loss= 0.495239 acc= 0.891550 cost_time= 0.308118s \n",
      "Epoch:   2637 loss= 0.257685 acc= 0.939991 cost_time= 0.306609s \n",
      "Epoch:   2638 loss= 0.296694 acc= 0.935331 cost_time= 0.311660s \n",
      "Epoch:   2639 loss= 0.322030 acc= 0.931272 cost_time= 0.310128s \n",
      "Epoch:   2640 loss= 0.420945 acc= 0.930753 cost_time= 0.313396s \n",
      "Epoch:   2641 loss= 0.806495 acc= 0.915139 cost_time= 0.312752s \n",
      "Epoch:   2642 loss= 0.315663 acc= 0.945462 cost_time= 0.308849s \n",
      "Epoch:   2643 loss= 0.432762 acc= 0.912981 cost_time= 0.328554s \n",
      "Epoch:   2644 loss= 0.798940 acc= 0.924551 cost_time= 0.308234s \n",
      "Epoch:   2645 loss= 0.376165 acc= 0.921848 cost_time= 0.314434s \n",
      "Epoch:   2646 loss= 0.449975 acc= 0.903858 cost_time= 0.305953s \n",
      "Epoch:   2647 loss= 0.236077 acc= 0.956062 cost_time= 0.311151s \n",
      "Epoch:   2648 loss= 0.480883 acc= 0.898832 cost_time= 0.313457s \n",
      "Epoch:   2649 loss= 0.479315 acc= 0.895534 cost_time= 0.304310s \n",
      "Epoch:   2650 loss= 1.000711 acc= 0.858825 cost_time= 0.309613s \n",
      "Epoch:   2651 loss= 0.910429 acc= 0.896677 cost_time= 0.308686s \n",
      "Epoch:   2652 loss= 0.351245 acc= 0.930728 cost_time= 0.318060s \n",
      "Epoch:   2653 loss= 0.287441 acc= 0.942354 cost_time= 0.315724s \n",
      "Epoch:   2654 loss= 0.907801 acc= 0.909705 cost_time= 0.308309s \n",
      "Epoch:   2655 loss= 0.849762 acc= 0.866281 cost_time= 0.307471s \n",
      "Epoch:   2656 loss= 0.404549 acc= 0.923429 cost_time= 0.311289s \n",
      "Epoch:   2657 loss= 1.132939 acc= 0.877682 cost_time= 0.331093s \n",
      "Epoch:   2658 loss= 0.276969 acc= 0.946526 cost_time= 0.319698s \n",
      "Epoch:   2659 loss= 0.225891 acc= 0.959302 cost_time= 0.312084s \n",
      "Epoch:   2660 loss= 0.245615 acc= 0.948658 cost_time= 0.315425s \n",
      "Epoch:   2661 loss= 0.614832 acc= 0.920809 cost_time= 0.309919s \n",
      "Epoch:   2662 loss= 0.264567 acc= 0.949406 cost_time= 0.312710s \n",
      "Epoch:   2663 loss= 1.080102 acc= 0.879773 cost_time= 0.307101s \n",
      "Epoch:   2664 loss= 0.432786 acc= 0.921450 cost_time= 0.332650s \n",
      "Epoch:   2665 loss= 0.256069 acc= 0.961343 cost_time= 0.314375s \n",
      "Epoch:   2666 loss= 0.609601 acc= 0.888883 cost_time= 0.310482s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2667 loss= 0.310227 acc= 0.941559 cost_time= 0.312066s \n",
      "Epoch:   2668 loss= 0.550581 acc= 0.916952 cost_time= 0.314400s \n",
      "Epoch:   2669 loss= 0.245143 acc= 0.959357 cost_time= 0.310712s \n",
      "Epoch:   2670 loss= 0.286562 acc= 0.948422 cost_time= 0.306691s \n",
      "Epoch:   2671 loss= 0.979978 acc= 0.899014 cost_time= 0.326402s \n",
      "Epoch:   2672 loss= 0.320043 acc= 0.941418 cost_time= 0.310029s \n",
      "Epoch:   2673 loss= 0.275908 acc= 0.946693 cost_time= 0.308484s \n",
      "Epoch:   2674 loss= 0.352112 acc= 0.935024 cost_time= 0.313923s \n",
      "Epoch:   2675 loss= 0.387839 acc= 0.932460 cost_time= 0.315563s \n",
      "Epoch:   2676 loss= 0.418468 acc= 0.921282 cost_time= 0.329465s \n",
      "Epoch:   2677 loss= 0.570522 acc= 0.909855 cost_time= 0.311099s \n",
      "Epoch:   2678 loss= 0.310709 acc= 0.947667 cost_time= 0.311013s \n",
      "Epoch:   2679 loss= 0.256016 acc= 0.950066 cost_time= 0.312483s \n",
      "Epoch:   2680 loss= 0.297921 acc= 0.939909 cost_time= 0.314279s \n",
      "Epoch:   2681 loss= 0.570600 acc= 0.905079 cost_time= 0.307645s \n",
      "Epoch:   2682 loss= 0.706693 acc= 0.914726 cost_time= 0.309815s \n",
      "Epoch:   2683 loss= 0.450916 acc= 0.904890 cost_time= 0.304571s \n",
      "Epoch:   2684 loss= 0.329614 acc= 0.937128 cost_time= 0.313299s \n",
      "Epoch:   2685 loss= 0.280507 acc= 0.942146 cost_time= 0.337572s \n",
      "Epoch:   2686 loss= 0.245260 acc= 0.953561 cost_time= 0.311656s \n",
      "Epoch:   2687 loss= 0.380753 acc= 0.921009 cost_time= 0.317609s \n",
      "Epoch:   2688 loss= 0.503174 acc= 0.911316 cost_time= 0.310339s \n",
      "Epoch:   2689 loss= 0.337525 acc= 0.932781 cost_time= 0.314917s \n",
      "Epoch:   2690 loss= 0.304579 acc= 0.942856 cost_time= 0.309063s \n",
      "Epoch:   2691 loss= 0.280837 acc= 0.951811 cost_time= 0.318680s \n",
      "Epoch:   2692 loss= 0.974299 acc= 0.883044 cost_time= 0.315836s \n",
      "Epoch:   2693 loss= 0.398097 acc= 0.923722 cost_time= 0.311988s \n",
      "Epoch:   2694 loss= 0.335176 acc= 0.928820 cost_time= 0.312113s \n",
      "Epoch:   2695 loss= 0.269916 acc= 0.950562 cost_time= 0.311057s \n",
      "Epoch:   2696 loss= 0.473412 acc= 0.897617 cost_time= 0.310798s \n",
      "Epoch:   2697 loss= 0.930206 acc= 0.886807 cost_time= 0.307424s \n",
      "Epoch:   2698 loss= 1.131078 acc= 0.885822 cost_time= 0.309830s \n",
      "Epoch:   2699 loss= 0.266940 acc= 0.939730 cost_time= 0.330610s \n",
      "Epoch:   2700 loss= 0.390023 acc= 0.931025 cost_time= 0.309539s \n",
      "Epoch:   2701 loss= 0.282272 acc= 0.950182 cost_time= 0.314318s \n",
      "Epoch:   2702 loss= 0.492450 acc= 0.899678 cost_time= 0.302372s \n",
      "Epoch:   2703 loss= 0.305535 acc= 0.955503 cost_time= 0.313804s \n",
      "Epoch:   2704 loss= 0.451201 acc= 0.905549 cost_time= 0.331328s \n",
      "Epoch:   2705 loss= 0.604974 acc= 0.911223 cost_time= 0.316166s \n",
      "Epoch:   2706 loss= 0.375033 acc= 0.930385 cost_time= 0.315096s \n",
      "Epoch:   2707 loss= 0.580862 acc= 0.916150 cost_time= 0.315856s \n",
      "Epoch:   2708 loss= 0.337540 acc= 0.947433 cost_time= 0.312055s \n",
      "Epoch:   2709 loss= 0.259681 acc= 0.951628 cost_time= 0.331544s \n",
      "Epoch:   2710 loss= 0.274069 acc= 0.935919 cost_time= 0.315322s \n",
      "Epoch:   2711 loss= 0.294273 acc= 0.940424 cost_time= 0.313457s \n",
      "Epoch:   2712 loss= 1.087378 acc= 0.797826 cost_time= 0.297463s \n",
      "Epoch:   2713 loss= 0.863318 acc= 0.895025 cost_time= 0.332591s \n",
      "Epoch:   2714 loss= 0.395240 acc= 0.932197 cost_time= 0.316712s \n",
      "Epoch:   2715 loss= 0.268982 acc= 0.940631 cost_time= 0.321184s \n",
      "Epoch:   2716 loss= 0.334350 acc= 0.932638 cost_time= 0.309066s \n",
      "Epoch:   2717 loss= 0.587708 acc= 0.920056 cost_time= 0.312117s \n",
      "Epoch:   2718 loss= 0.281598 acc= 0.933939 cost_time= 0.315782s \n",
      "Epoch:   2719 loss= 0.397470 acc= 0.906916 cost_time= 0.310593s \n",
      "Epoch:   2720 loss= 1.066682 acc= 0.827588 cost_time= 0.308154s \n",
      "Epoch:   2721 loss= 0.416525 acc= 0.910048 cost_time= 0.309018s \n",
      "Epoch:   2722 loss= 0.275707 acc= 0.937795 cost_time= 0.320896s \n",
      "Epoch:   2723 loss= 0.294848 acc= 0.945533 cost_time= 0.305075s \n",
      "Epoch:   2724 loss= 0.276380 acc= 0.948576 cost_time= 0.318562s \n",
      "Epoch:   2725 loss= 0.350695 acc= 0.917364 cost_time= 0.308438s \n",
      "Epoch:   2726 loss= 0.320998 acc= 0.916946 cost_time= 0.308605s \n",
      "Epoch:   2727 loss= 0.372959 acc= 0.939146 cost_time= 0.316683s \n",
      "Epoch:   2728 loss= 1.238317 acc= 0.838968 cost_time= 0.305603s \n",
      "Epoch:   2729 loss= 0.341495 acc= 0.941328 cost_time= 0.317176s \n",
      "Epoch:   2730 loss= 0.614356 acc= 0.870150 cost_time= 0.305104s \n",
      "Epoch:   2731 loss= 0.392658 acc= 0.927712 cost_time= 0.307559s \n",
      "Epoch:   2732 loss= 0.336395 acc= 0.916724 cost_time= 0.309419s \n",
      "Epoch:   2733 loss= 0.893409 acc= 0.891110 cost_time= 0.309295s \n",
      "Epoch:   2734 loss= 0.827268 acc= 0.883687 cost_time= 0.330412s \n",
      "Epoch:   2735 loss= 0.450953 acc= 0.899132 cost_time= 0.306730s \n",
      "Epoch:   2736 loss= 0.329842 acc= 0.935160 cost_time= 0.324136s \n",
      "Epoch:   2737 loss= 0.812372 acc= 0.860997 cost_time= 0.306525s \n",
      "Epoch:   2738 loss= 0.493519 acc= 0.884602 cost_time= 0.306123s \n",
      "Epoch:   2739 loss= 0.498698 acc= 0.913231 cost_time= 0.306496s \n",
      "Epoch:   2740 loss= 1.548797 acc= 0.894745 cost_time= 0.310959s \n",
      "Epoch:   2741 loss= 0.610694 acc= 0.868807 cost_time= 0.306414s \n",
      "Epoch:   2742 loss= 0.776773 acc= 0.901238 cost_time= 0.312791s \n",
      "Epoch:   2743 loss= 0.317225 acc= 0.942535 cost_time= 0.318881s \n",
      "Epoch:   2744 loss= 0.334689 acc= 0.938939 cost_time= 0.314729s \n",
      "Epoch:   2745 loss= 0.317373 acc= 0.940293 cost_time= 0.309690s \n",
      "Epoch:   2746 loss= 0.388537 acc= 0.926793 cost_time= 0.306436s \n",
      "Epoch:   2747 loss= 0.369286 acc= 0.917526 cost_time= 0.310259s \n",
      "Epoch:   2748 loss= 0.226709 acc= 0.951057 cost_time= 0.314272s \n",
      "Epoch:   2749 loss= 0.384268 acc= 0.917064 cost_time= 0.331457s \n",
      "Epoch:   2750 loss= 0.274058 acc= 0.951428 cost_time= 0.315482s \n",
      "Epoch:   2751 loss= 1.251742 acc= 0.875109 cost_time= 0.307513s \n",
      "Epoch:   2752 loss= 0.266756 acc= 0.938094 cost_time= 0.310007s \n",
      "Epoch:   2753 loss= 0.340921 acc= 0.939331 cost_time= 0.314331s \n",
      "Epoch:   2754 loss= 0.806365 acc= 0.851551 cost_time= 0.311768s \n",
      "Epoch:   2755 loss= 0.312504 acc= 0.940344 cost_time= 0.331686s \n",
      "Epoch:   2756 loss= 0.272658 acc= 0.945012 cost_time= 0.308357s \n",
      "Epoch:   2757 loss= 0.359074 acc= 0.915483 cost_time= 0.317253s \n",
      "Epoch:   2758 loss= 0.345841 acc= 0.924271 cost_time= 0.310449s \n",
      "Epoch:   2759 loss= 0.499870 acc= 0.906603 cost_time= 0.305732s \n",
      "Epoch:   2760 loss= 0.334077 acc= 0.933179 cost_time= 0.311241s \n",
      "Epoch:   2761 loss= 0.522690 acc= 0.899678 cost_time= 0.313068s \n",
      "Epoch:   2762 loss= 0.618776 acc= 0.914609 cost_time= 0.315823s \n",
      "Epoch:   2763 loss= 0.376243 acc= 0.935723 cost_time= 0.307333s \n",
      "Epoch:   2764 loss= 0.303800 acc= 0.941814 cost_time= 0.319314s \n",
      "Epoch:   2765 loss= 0.711693 acc= 0.926798 cost_time= 0.312690s \n",
      "Epoch:   2766 loss= 0.372740 acc= 0.942923 cost_time= 0.308951s \n",
      "Epoch:   2767 loss= 0.327545 acc= 0.948483 cost_time= 0.309384s \n",
      "Epoch:   2768 loss= 0.746799 acc= 0.900482 cost_time= 0.309376s \n",
      "Epoch:   2769 loss= 0.373177 acc= 0.917163 cost_time= 0.328856s \n",
      "Epoch:   2770 loss= 0.290740 acc= 0.956465 cost_time= 0.310109s \n",
      "Epoch:   2771 loss= 0.395076 acc= 0.922503 cost_time= 0.313245s \n",
      "Epoch:   2772 loss= 0.288271 acc= 0.958729 cost_time= 0.310668s \n",
      "Epoch:   2773 loss= 0.271648 acc= 0.937078 cost_time= 0.308716s \n",
      "Epoch:   2774 loss= 1.529266 acc= 0.896495 cost_time= 0.312656s \n",
      "Epoch:   2775 loss= 0.391629 acc= 0.931776 cost_time= 0.309084s \n",
      "Epoch:   2776 loss= 0.851777 acc= 0.873140 cost_time= 0.329760s \n",
      "Epoch:   2777 loss= 0.445874 acc= 0.901744 cost_time= 0.313475s \n",
      "Epoch:   2778 loss= 0.282853 acc= 0.941930 cost_time= 0.318882s \n",
      "Epoch:   2779 loss= 0.389270 acc= 0.924569 cost_time= 0.312954s \n",
      "Epoch:   2780 loss= 0.507554 acc= 0.897342 cost_time= 0.309508s \n",
      "Epoch:   2781 loss= 0.384955 acc= 0.923040 cost_time= 0.311543s \n",
      "Epoch:   2782 loss= 0.493273 acc= 0.930280 cost_time= 0.310818s \n",
      "Epoch:   2783 loss= 0.606554 acc= 0.893781 cost_time= 0.316046s \n",
      "Epoch:   2784 loss= 0.686838 acc= 0.837362 cost_time= 0.303283s \n",
      "Epoch:   2785 loss= 0.309960 acc= 0.934820 cost_time= 0.310140s \n",
      "Epoch:   2786 loss= 0.531229 acc= 0.907935 cost_time= 0.309119s \n",
      "Epoch:   2787 loss= 0.324624 acc= 0.929189 cost_time= 0.310767s \n",
      "Epoch:   2788 loss= 0.535996 acc= 0.911980 cost_time= 0.334331s \n",
      "Epoch:   2789 loss= 0.639287 acc= 0.840963 cost_time= 0.306646s \n",
      "Epoch:   2790 loss= 0.325170 acc= 0.929559 cost_time= 0.312468s \n",
      "Epoch:   2791 loss= 0.359056 acc= 0.930566 cost_time= 0.317262s \n",
      "Epoch:   2792 loss= 0.250183 acc= 0.955974 cost_time= 0.312330s \n",
      "Epoch:   2793 loss= 0.336059 acc= 0.926444 cost_time= 0.306514s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2794 loss= 0.378429 acc= 0.924104 cost_time= 0.305574s \n",
      "Epoch:   2795 loss= 0.276487 acc= 0.947618 cost_time= 0.316927s \n",
      "Epoch:   2796 loss= 0.353630 acc= 0.933351 cost_time= 0.310376s \n",
      "Epoch:   2797 loss= 0.304909 acc= 0.931234 cost_time= 0.311509s \n",
      "Epoch:   2798 loss= 0.666937 acc= 0.910515 cost_time= 0.314126s \n",
      "Epoch:   2799 loss= 0.253664 acc= 0.952371 cost_time= 0.326558s \n",
      "Epoch:   2800 loss= 0.279958 acc= 0.952145 cost_time= 0.316532s \n",
      "Epoch:   2801 loss= 0.396934 acc= 0.935846 cost_time= 0.311087s \n",
      "Epoch:   2802 loss= 0.258693 acc= 0.956594 cost_time= 0.325795s \n",
      "Epoch:   2803 loss= 0.298254 acc= 0.945408 cost_time= 0.328250s \n",
      "Epoch:   2804 loss= 0.443375 acc= 0.909859 cost_time= 0.330277s \n",
      "Epoch:   2805 loss= 0.352002 acc= 0.936367 cost_time= 0.317905s \n",
      "Epoch:   2806 loss= 0.275109 acc= 0.949300 cost_time= 0.330263s \n",
      "Epoch:   2807 loss= 0.326919 acc= 0.942004 cost_time= 0.309252s \n",
      "Epoch:   2808 loss= 0.412851 acc= 0.907399 cost_time= 0.311139s \n",
      "Epoch:   2809 loss= 0.308503 acc= 0.948260 cost_time= 0.331888s \n",
      "Epoch:   2810 loss= 0.329165 acc= 0.918390 cost_time= 0.306575s \n",
      "Epoch:   2811 loss= 0.404252 acc= 0.933309 cost_time= 0.308192s \n",
      "Epoch:   2812 loss= 0.614187 acc= 0.873080 cost_time= 0.306003s \n",
      "Epoch:   2813 loss= 0.480150 acc= 0.903169 cost_time= 0.330023s \n",
      "Epoch:   2814 loss= 0.388626 acc= 0.915196 cost_time= 0.309958s \n",
      "Epoch:   2815 loss= 0.251212 acc= 0.950587 cost_time= 0.320055s \n",
      "Epoch:   2816 loss= 1.297422 acc= 0.852525 cost_time= 0.311410s \n",
      "Epoch:   2817 loss= 0.724674 acc= 0.904306 cost_time= 0.311421s \n",
      "Epoch:   2818 loss= 0.297991 acc= 0.934783 cost_time= 0.331201s \n",
      "Epoch:   2819 loss= 0.828783 acc= 0.869247 cost_time= 0.312653s \n",
      "Epoch:   2820 loss= 0.260331 acc= 0.936344 cost_time= 0.335277s \n",
      "Epoch:   2821 loss= 0.415377 acc= 0.898682 cost_time= 0.305613s \n",
      "Epoch:   2822 loss= 0.343090 acc= 0.931928 cost_time= 0.311428s \n",
      "Epoch:   2823 loss= 0.940144 acc= 0.832088 cost_time= 0.307998s \n",
      "Epoch:   2824 loss= 0.326012 acc= 0.940026 cost_time= 0.308485s \n",
      "Epoch:   2825 loss= 0.236616 acc= 0.949070 cost_time= 0.312238s \n",
      "Epoch:   2826 loss= 0.374025 acc= 0.915551 cost_time= 0.312114s \n",
      "Epoch:   2827 loss= 0.645964 acc= 0.905541 cost_time= 0.332825s \n",
      "Epoch:   2828 loss= 0.822373 acc= 0.892495 cost_time= 0.305331s \n",
      "Epoch:   2829 loss= 1.783075 acc= 0.888950 cost_time= 0.313087s \n",
      "Epoch:   2830 loss= 0.741928 acc= 0.913639 cost_time= 0.309567s \n",
      "Epoch:   2831 loss= 0.308973 acc= 0.940907 cost_time= 0.312357s \n",
      "Epoch:   2832 loss= 0.306831 acc= 0.934313 cost_time= 0.313352s \n",
      "Epoch:   2833 loss= 0.891657 acc= 0.886301 cost_time= 0.318537s \n",
      "Epoch:   2834 loss= 0.813743 acc= 0.841988 cost_time= 0.312748s \n",
      "Epoch:   2835 loss= 0.328745 acc= 0.938571 cost_time= 0.308846s \n",
      "Epoch:   2836 loss= 0.287992 acc= 0.934078 cost_time= 0.315020s \n",
      "Epoch:   2837 loss= 0.271769 acc= 0.938324 cost_time= 0.330364s \n",
      "Epoch:   2838 loss= 0.514290 acc= 0.898286 cost_time= 0.306875s \n",
      "Epoch:   2839 loss= 1.056609 acc= 0.897347 cost_time= 0.331581s \n",
      "Epoch:   2840 loss= 0.784002 acc= 0.872587 cost_time= 0.314562s \n",
      "Epoch:   2841 loss= 0.377638 acc= 0.927330 cost_time= 0.314729s \n",
      "Epoch:   2842 loss= 1.491351 acc= 0.838448 cost_time= 0.304079s \n",
      "Epoch:   2843 loss= 0.855939 acc= 0.873997 cost_time= 0.319653s \n",
      "Epoch:   2844 loss= 0.793097 acc= 0.875225 cost_time= 0.309077s \n",
      "Epoch:   2845 loss= 0.407343 acc= 0.925294 cost_time= 0.306923s \n",
      "Epoch:   2846 loss= 0.266295 acc= 0.957739 cost_time= 0.311355s \n",
      "Epoch:   2847 loss= 0.374935 acc= 0.941198 cost_time= 0.312940s \n",
      "Epoch:   2848 loss= 0.285774 acc= 0.934620 cost_time= 0.310268s \n",
      "Epoch:   2849 loss= 1.077115 acc= 0.875619 cost_time= 0.309314s \n",
      "Epoch:   2850 loss= 0.345306 acc= 0.931463 cost_time= 0.311068s \n",
      "Epoch:   2851 loss= 1.950540 acc= 0.824591 cost_time= 0.314354s \n",
      "Epoch:   2852 loss= 0.579829 acc= 0.888513 cost_time= 0.306733s \n",
      "Epoch:   2853 loss= 0.317696 acc= 0.951321 cost_time= 0.312090s \n",
      "Epoch:   2854 loss= 0.371386 acc= 0.918374 cost_time= 0.306638s \n",
      "Epoch:   2855 loss= 0.418060 acc= 0.914962 cost_time= 0.326655s \n",
      "Epoch:   2856 loss= 0.271031 acc= 0.950251 cost_time= 0.310351s \n",
      "Epoch:   2857 loss= 0.374304 acc= 0.918745 cost_time= 0.312821s \n",
      "Epoch:   2858 loss= 0.335201 acc= 0.930899 cost_time= 0.311246s \n",
      "Epoch:   2859 loss= 0.296492 acc= 0.951065 cost_time= 0.311095s \n",
      "Epoch:   2860 loss= 0.314775 acc= 0.936239 cost_time= 0.309315s \n",
      "Epoch:   2861 loss= 0.631234 acc= 0.900629 cost_time= 0.305464s \n",
      "Epoch:   2862 loss= 0.424792 acc= 0.914872 cost_time= 0.315715s \n",
      "Epoch:   2863 loss= 0.721877 acc= 0.921903 cost_time= 0.315824s \n",
      "Epoch:   2864 loss= 1.536625 acc= 0.732346 cost_time= 0.299439s \n",
      "Epoch:   2865 loss= 0.226728 acc= 0.948555 cost_time= 0.313229s \n",
      "Epoch:   2866 loss= 0.296816 acc= 0.956906 cost_time= 0.312944s \n",
      "Epoch:   2867 loss= 0.420283 acc= 0.914321 cost_time= 0.310882s \n",
      "Epoch:   2868 loss= 0.335843 acc= 0.934557 cost_time= 0.311262s \n",
      "Epoch:   2869 loss= 0.451036 acc= 0.903691 cost_time= 0.322525s \n",
      "Epoch:   2870 loss= 0.377117 acc= 0.942846 cost_time= 0.310667s \n",
      "Epoch:   2871 loss= 0.286343 acc= 0.944112 cost_time= 0.311647s \n",
      "Epoch:   2872 loss= 0.299564 acc= 0.946470 cost_time= 0.311467s \n",
      "Epoch:   2873 loss= 0.311568 acc= 0.933492 cost_time= 0.309520s \n",
      "Epoch:   2874 loss= 0.549168 acc= 0.871905 cost_time= 0.307838s \n",
      "Epoch:   2875 loss= 0.398491 acc= 0.921597 cost_time= 0.308744s \n",
      "Epoch:   2876 loss= 0.690372 acc= 0.890005 cost_time= 0.333635s \n",
      "Epoch:   2877 loss= 1.398268 acc= 0.804083 cost_time= 0.309285s \n",
      "Epoch:   2878 loss= 0.523770 acc= 0.876016 cost_time= 0.313047s \n",
      "Epoch:   2879 loss= 0.498815 acc= 0.909054 cost_time= 0.307903s \n",
      "Epoch:   2880 loss= 0.777616 acc= 0.912889 cost_time= 0.313555s \n",
      "Epoch:   2881 loss= 0.279204 acc= 0.944523 cost_time= 0.310456s \n",
      "Epoch:   2882 loss= 0.461331 acc= 0.909477 cost_time= 0.304672s \n",
      "Epoch:   2883 loss= 0.261178 acc= 0.950952 cost_time= 0.333467s \n",
      "Epoch:   2884 loss= 0.237258 acc= 0.947565 cost_time= 0.310333s \n",
      "Epoch:   2885 loss= 0.260404 acc= 0.943166 cost_time= 0.306298s \n",
      "Epoch:   2886 loss= 0.254293 acc= 0.945101 cost_time= 0.317454s \n",
      "Epoch:   2887 loss= 0.292722 acc= 0.951242 cost_time= 0.308977s \n",
      "Epoch:   2888 loss= 0.258890 acc= 0.938602 cost_time= 0.314402s \n",
      "Epoch:   2889 loss= 0.345735 acc= 0.932262 cost_time= 0.310200s \n",
      "Epoch:   2890 loss= 0.698600 acc= 0.908440 cost_time= 0.306808s \n",
      "Epoch:   2891 loss= 0.522832 acc= 0.914745 cost_time= 0.331649s \n",
      "Epoch:   2892 loss= 0.329580 acc= 0.934449 cost_time= 0.316252s \n",
      "Epoch:   2893 loss= 0.326343 acc= 0.922977 cost_time= 0.311138s \n",
      "Epoch:   2894 loss= 0.334250 acc= 0.937036 cost_time= 0.308969s \n",
      "Epoch:   2895 loss= 0.359897 acc= 0.936604 cost_time= 0.304287s \n",
      "Epoch:   2896 loss= 0.341426 acc= 0.917105 cost_time= 0.310662s \n",
      "Epoch:   2897 loss= 0.869452 acc= 0.911183 cost_time= 0.330266s \n",
      "Epoch:   2898 loss= 0.571214 acc= 0.915171 cost_time= 0.312151s \n",
      "Epoch:   2899 loss= 0.391947 acc= 0.940047 cost_time= 0.317636s \n",
      "Epoch:   2900 loss= 0.303738 acc= 0.945271 cost_time= 0.305956s \n",
      "Epoch:   2901 loss= 0.262188 acc= 0.953215 cost_time= 0.316296s \n",
      "Epoch:   2902 loss= 0.286147 acc= 0.957353 cost_time= 0.313133s \n",
      "Epoch:   2903 loss= 0.586095 acc= 0.862396 cost_time= 0.303104s \n",
      "Epoch:   2904 loss= 0.337053 acc= 0.933217 cost_time= 0.310404s \n",
      "Epoch:   2905 loss= 0.207994 acc= 0.962607 cost_time= 0.315195s \n",
      "Epoch:   2906 loss= 0.272729 acc= 0.940190 cost_time= 0.319842s \n",
      "Epoch:   2907 loss= 0.278122 acc= 0.941340 cost_time= 0.309364s \n",
      "Epoch:   2908 loss= 0.628456 acc= 0.849238 cost_time= 0.308622s \n",
      "Epoch:   2909 loss= 0.244322 acc= 0.941472 cost_time= 0.309736s \n",
      "Epoch:   2910 loss= 0.357546 acc= 0.926245 cost_time= 0.307302s \n",
      "Epoch:   2911 loss= 0.276240 acc= 0.945184 cost_time= 0.332348s \n",
      "Epoch:   2912 loss= 0.869439 acc= 0.904483 cost_time= 0.311726s \n",
      "Epoch:   2913 loss= 0.353859 acc= 0.935953 cost_time= 0.320646s \n",
      "Epoch:   2914 loss= 0.751170 acc= 0.864687 cost_time= 0.308018s \n",
      "Epoch:   2915 loss= 0.310192 acc= 0.939236 cost_time= 0.313198s \n",
      "Epoch:   2916 loss= 0.636662 acc= 0.887171 cost_time= 0.314004s \n",
      "Epoch:   2917 loss= 0.921636 acc= 0.869910 cost_time= 0.305346s \n",
      "Epoch:   2918 loss= 0.600987 acc= 0.922547 cost_time= 0.308414s \n",
      "Epoch:   2919 loss= 0.362041 acc= 0.940062 cost_time= 0.313988s \n",
      "Epoch:   2920 loss= 0.548829 acc= 0.892570 cost_time= 0.311769s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   2921 loss= 0.266440 acc= 0.947228 cost_time= 0.318274s \n",
      "Epoch:   2922 loss= 1.031550 acc= 0.811671 cost_time= 0.313171s \n",
      "Epoch:   2923 loss= 0.643293 acc= 0.920632 cost_time= 0.311319s \n",
      "Epoch:   2924 loss= 0.367252 acc= 0.916314 cost_time= 0.307367s \n",
      "Epoch:   2925 loss= 0.237953 acc= 0.950125 cost_time= 0.334620s \n",
      "Epoch:   2926 loss= 0.289153 acc= 0.928470 cost_time= 0.308690s \n",
      "Epoch:   2927 loss= 0.670291 acc= 0.872836 cost_time= 0.316278s \n",
      "Epoch:   2928 loss= 0.436776 acc= 0.905610 cost_time= 0.306153s \n",
      "Epoch:   2929 loss= 0.314455 acc= 0.940463 cost_time= 0.306705s \n",
      "Epoch:   2930 loss= 0.368332 acc= 0.935540 cost_time= 0.308377s \n",
      "Epoch:   2931 loss= 0.280008 acc= 0.948247 cost_time= 0.314386s \n",
      "Epoch:   2932 loss= 0.229107 acc= 0.948799 cost_time= 0.311516s \n",
      "Epoch:   2933 loss= 0.337151 acc= 0.927258 cost_time= 0.313727s \n",
      "Epoch:   2934 loss= 0.259149 acc= 0.945055 cost_time= 0.316552s \n",
      "Epoch:   2935 loss= 0.338283 acc= 0.929802 cost_time= 0.311994s \n",
      "Epoch:   2936 loss= 0.229043 acc= 0.948425 cost_time= 0.310887s \n",
      "Epoch:   2937 loss= 0.301597 acc= 0.918685 cost_time= 0.313853s \n",
      "Epoch:   2938 loss= 0.332816 acc= 0.921644 cost_time= 0.304859s \n",
      "Epoch:   2939 loss= 0.263719 acc= 0.955475 cost_time= 0.330081s \n",
      "Epoch:   2940 loss= 1.120831 acc= 0.880114 cost_time= 0.309875s \n",
      "Epoch:   2941 loss= 0.313596 acc= 0.941963 cost_time= 0.311061s \n",
      "Epoch:   2942 loss= 0.307464 acc= 0.938578 cost_time= 0.310980s \n",
      "Epoch:   2943 loss= 0.216028 acc= 0.957274 cost_time= 0.313821s \n",
      "Epoch:   2944 loss= 0.293695 acc= 0.946225 cost_time= 0.330119s \n",
      "Epoch:   2945 loss= 1.480314 acc= 0.837348 cost_time= 0.309581s \n",
      "Epoch:   2946 loss= 0.326774 acc= 0.941200 cost_time= 0.311207s \n",
      "Epoch:   2947 loss= 0.336713 acc= 0.931151 cost_time= 0.309893s \n",
      "Epoch:   2948 loss= 0.392227 acc= 0.906335 cost_time= 0.315064s \n",
      "Epoch:   2949 loss= 0.233821 acc= 0.948319 cost_time= 0.312400s \n",
      "Epoch:   2950 loss= 0.263869 acc= 0.953360 cost_time= 0.318252s \n",
      "Epoch:   2951 loss= 0.528913 acc= 0.870481 cost_time= 0.306413s \n",
      "Epoch:   2952 loss= 0.345383 acc= 0.934238 cost_time= 0.313591s \n",
      "Epoch:   2953 loss= 0.794956 acc= 0.919188 cost_time= 0.312853s \n",
      "Epoch:   2954 loss= 0.358945 acc= 0.926166 cost_time= 0.310309s \n",
      "Epoch:   2955 loss= 0.454866 acc= 0.907210 cost_time= 0.317228s \n",
      "Epoch:   2956 loss= 0.360577 acc= 0.929731 cost_time= 0.315729s \n",
      "Epoch:   2957 loss= 0.415122 acc= 0.918601 cost_time= 0.306962s \n",
      "Epoch:   2958 loss= 0.234491 acc= 0.945459 cost_time= 0.311730s \n",
      "Epoch:   2959 loss= 0.638550 acc= 0.915658 cost_time= 0.314695s \n",
      "Epoch:   2960 loss= 0.677423 acc= 0.892195 cost_time= 0.306049s \n",
      "Epoch:   2961 loss= 0.223643 acc= 0.949108 cost_time= 0.312020s \n",
      "Epoch:   2962 loss= 0.392102 acc= 0.931970 cost_time= 0.310277s \n",
      "Epoch:   2963 loss= 0.414803 acc= 0.910697 cost_time= 0.311159s \n",
      "Epoch:   2964 loss= 0.342365 acc= 0.922165 cost_time= 0.313437s \n",
      "Epoch:   2965 loss= 0.261593 acc= 0.945259 cost_time= 0.315521s \n",
      "Epoch:   2966 loss= 0.290577 acc= 0.955217 cost_time= 0.316898s \n",
      "Epoch:   2967 loss= 0.339721 acc= 0.927567 cost_time= 0.328949s \n",
      "Epoch:   2968 loss= 0.448735 acc= 0.869506 cost_time= 0.308697s \n",
      "Epoch:   2969 loss= 0.471134 acc= 0.899523 cost_time= 0.316361s \n",
      "Epoch:   2970 loss= 0.775274 acc= 0.901983 cost_time= 0.315313s \n",
      "Epoch:   2971 loss= 0.303819 acc= 0.941574 cost_time= 0.307127s \n",
      "Epoch:   2972 loss= 0.302204 acc= 0.920415 cost_time= 0.306609s \n",
      "Epoch:   2973 loss= 0.833551 acc= 0.865350 cost_time= 0.311331s \n",
      "Epoch:   2974 loss= 0.995320 acc= 0.861226 cost_time= 0.325988s \n",
      "Epoch:   2975 loss= 1.088243 acc= 0.867944 cost_time= 0.309345s \n",
      "Epoch:   2976 loss= 0.460588 acc= 0.908672 cost_time= 0.312832s \n",
      "Epoch:   2977 loss= 0.734336 acc= 0.915716 cost_time= 0.312835s \n",
      "Epoch:   2978 loss= 0.274187 acc= 0.940514 cost_time= 0.308625s \n",
      "Epoch:   2979 loss= 0.343601 acc= 0.938163 cost_time= 0.315162s \n",
      "Epoch:   2980 loss= 0.338900 acc= 0.922711 cost_time= 0.309089s \n",
      "Epoch:   2981 loss= 0.409668 acc= 0.886509 cost_time= 0.304698s \n",
      "Epoch:   2982 loss= 0.448939 acc= 0.919214 cost_time= 0.310646s \n",
      "Epoch:   2983 loss= 0.370001 acc= 0.910824 cost_time= 0.317839s \n",
      "Epoch:   2984 loss= 0.629303 acc= 0.916239 cost_time= 0.331352s \n",
      "Epoch:   2985 loss= 1.457428 acc= 0.883162 cost_time= 0.312226s \n",
      "Epoch:   2986 loss= 0.329490 acc= 0.945195 cost_time= 0.327129s \n",
      "Epoch:   2987 loss= 0.231885 acc= 0.958566 cost_time= 0.314195s \n",
      "Epoch:   2988 loss= 0.588988 acc= 0.918546 cost_time= 0.309422s \n",
      "Epoch:   2989 loss= 0.300306 acc= 0.947979 cost_time= 0.312424s \n",
      "Epoch:   2990 loss= 0.332839 acc= 0.926765 cost_time= 0.308141s \n",
      "Epoch:   2991 loss= 1.340639 acc= 0.858804 cost_time= 0.312835s \n",
      "Epoch:   2992 loss= 1.064853 acc= 0.877802 cost_time= 0.308838s \n",
      "Epoch:   2993 loss= 0.823920 acc= 0.892677 cost_time= 0.312870s \n",
      "Epoch:   2994 loss= 0.372346 acc= 0.926364 cost_time= 0.310617s \n",
      "Epoch:   2995 loss= 0.635211 acc= 0.895820 cost_time= 0.334229s \n",
      "Epoch:   2996 loss= 0.295430 acc= 0.944790 cost_time= 0.313205s \n",
      "Epoch:   2997 loss= 0.830746 acc= 0.820722 cost_time= 0.301330s \n",
      "Epoch:   2998 loss= 1.092151 acc= 0.836941 cost_time= 0.311074s \n",
      "Epoch:   2999 loss= 0.772155 acc= 0.893109 cost_time= 0.314118s \n",
      "Epoch:   3000 loss= 0.229539 acc= 0.942843 cost_time= 0.331192s \n",
      "Epoch:   3001 loss= 0.356880 acc= 0.934891 cost_time= 0.308195s \n",
      "Epoch:   3002 loss= 0.231954 acc= 0.947633 cost_time= 0.309540s \n",
      "Epoch:   3003 loss= 0.273097 acc= 0.946537 cost_time= 0.317256s \n",
      "Epoch:   3004 loss= 0.512427 acc= 0.899447 cost_time= 0.303732s \n",
      "Epoch:   3005 loss= 0.924115 acc= 0.894875 cost_time= 0.307792s \n",
      "Epoch:   3006 loss= 0.384419 acc= 0.927849 cost_time= 0.310187s \n",
      "Epoch:   3007 loss= 0.237766 acc= 0.960551 cost_time= 0.313348s \n",
      "Epoch:   3008 loss= 1.111279 acc= 0.857316 cost_time= 0.305647s \n",
      "Epoch:   3009 loss= 0.580035 acc= 0.894197 cost_time= 0.309219s \n",
      "Epoch:   3010 loss= 1.106551 acc= 0.875319 cost_time= 0.308007s \n",
      "Epoch:   3011 loss= 0.433821 acc= 0.887009 cost_time= 0.316877s \n",
      "Epoch:   3012 loss= 0.230068 acc= 0.951703 cost_time= 0.311337s \n",
      "Epoch:   3013 loss= 1.503055 acc= 0.846517 cost_time= 0.315004s \n",
      "Epoch:   3014 loss= 0.480711 acc= 0.917198 cost_time= 0.309136s \n",
      "Epoch:   3015 loss= 0.335597 acc= 0.940745 cost_time= 0.314610s \n",
      "Epoch:   3016 loss= 0.378053 acc= 0.917431 cost_time= 0.310680s \n",
      "Epoch:   3017 loss= 0.410689 acc= 0.902644 cost_time= 0.309383s \n",
      "Epoch:   3018 loss= 0.215405 acc= 0.953183 cost_time= 0.311853s \n",
      "Epoch:   3019 loss= 0.390065 acc= 0.917078 cost_time= 0.326838s \n",
      "Epoch:   3020 loss= 0.304406 acc= 0.932723 cost_time= 0.316452s \n",
      "Epoch:   3021 loss= 0.360809 acc= 0.936042 cost_time= 0.315575s \n",
      "Epoch:   3022 loss= 1.647266 acc= 0.860123 cost_time= 0.308157s \n",
      "Epoch:   3023 loss= 0.271130 acc= 0.949523 cost_time= 0.332450s \n",
      "Epoch:   3024 loss= 0.448434 acc= 0.897560 cost_time= 0.305697s \n",
      "Epoch:   3025 loss= 1.146065 acc= 0.884368 cost_time= 0.317745s \n",
      "Epoch:   3026 loss= 0.579484 acc= 0.867492 cost_time= 0.304801s \n",
      "Epoch:   3027 loss= 0.223583 acc= 0.954693 cost_time= 0.318496s \n",
      "Epoch:   3028 loss= 0.869317 acc= 0.879661 cost_time= 0.307723s \n",
      "Epoch:   3029 loss= 0.273534 acc= 0.936985 cost_time= 0.316838s \n",
      "Epoch:   3030 loss= 0.579835 acc= 0.924842 cost_time= 0.329750s \n",
      "Epoch:   3031 loss= 0.417527 acc= 0.917503 cost_time= 0.310698s \n",
      "Epoch:   3032 loss= 0.381733 acc= 0.924286 cost_time= 0.312817s \n",
      "Epoch:   3033 loss= 0.284021 acc= 0.930722 cost_time= 0.313780s \n",
      "Epoch:   3034 loss= 0.274799 acc= 0.948442 cost_time= 0.310358s \n",
      "Epoch:   3035 loss= 0.939681 acc= 0.885181 cost_time= 0.311545s \n",
      "Epoch:   3036 loss= 0.376061 acc= 0.934832 cost_time= 0.310550s \n",
      "Epoch:   3037 loss= 0.254175 acc= 0.939873 cost_time= 0.332944s \n",
      "Epoch:   3038 loss= 0.402130 acc= 0.923416 cost_time= 0.310816s \n",
      "Epoch:   3039 loss= 0.235774 acc= 0.944610 cost_time= 0.321194s \n",
      "Epoch:   3040 loss= 0.252995 acc= 0.947168 cost_time= 0.314651s \n",
      "Epoch:   3041 loss= 0.250822 acc= 0.953879 cost_time= 0.319638s \n",
      "Epoch:   3042 loss= 0.858282 acc= 0.895652 cost_time= 0.310744s \n",
      "Epoch:   3043 loss= 0.268392 acc= 0.944507 cost_time= 0.314497s \n",
      "Epoch:   3044 loss= 0.339504 acc= 0.937853 cost_time= 0.309857s \n",
      "Epoch:   3045 loss= 0.325429 acc= 0.944108 cost_time= 0.307592s \n",
      "Epoch:   3046 loss= 1.135507 acc= 0.859818 cost_time= 0.309819s \n",
      "Epoch:   3047 loss= 0.511527 acc= 0.931105 cost_time= 0.306826s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3048 loss= 0.297514 acc= 0.941160 cost_time= 0.309772s \n",
      "Epoch:   3049 loss= 0.331662 acc= 0.933108 cost_time= 0.304802s \n",
      "Epoch:   3050 loss= 0.347323 acc= 0.920094 cost_time= 0.306309s \n",
      "Epoch:   3051 loss= 0.409077 acc= 0.913784 cost_time= 0.326977s \n",
      "Epoch:   3052 loss= 0.283567 acc= 0.942458 cost_time= 0.311298s \n",
      "Epoch:   3053 loss= 0.405525 acc= 0.932447 cost_time= 0.322751s \n",
      "Epoch:   3054 loss= 0.577553 acc= 0.869156 cost_time= 0.306552s \n",
      "Epoch:   3055 loss= 0.254926 acc= 0.949404 cost_time= 0.310034s \n",
      "Epoch:   3056 loss= 0.385256 acc= 0.928100 cost_time= 0.313675s \n",
      "Epoch:   3057 loss= 0.248906 acc= 0.944769 cost_time= 0.316560s \n",
      "Epoch:   3058 loss= 0.273567 acc= 0.934660 cost_time= 0.306778s \n",
      "Epoch:   3059 loss= 0.424497 acc= 0.909572 cost_time= 0.311434s \n",
      "Epoch:   3060 loss= 0.323204 acc= 0.930193 cost_time= 0.315118s \n",
      "Epoch:   3061 loss= 0.988655 acc= 0.907722 cost_time= 0.312680s \n",
      "Epoch:   3062 loss= 0.270535 acc= 0.949754 cost_time= 0.309941s \n",
      "Epoch:   3063 loss= 0.281944 acc= 0.942343 cost_time= 0.314181s \n",
      "Epoch:   3064 loss= 0.564954 acc= 0.887829 cost_time= 0.311360s \n",
      "Epoch:   3065 loss= 0.285403 acc= 0.952103 cost_time= 0.310343s \n",
      "Epoch:   3066 loss= 0.252081 acc= 0.946172 cost_time= 0.312373s \n",
      "Epoch:   3067 loss= 0.436731 acc= 0.920912 cost_time= 0.316192s \n",
      "Epoch:   3068 loss= 0.345098 acc= 0.927221 cost_time= 0.315355s \n",
      "Epoch:   3069 loss= 0.341254 acc= 0.924799 cost_time= 0.307363s \n",
      "Epoch:   3070 loss= 0.511423 acc= 0.877132 cost_time= 0.314357s \n",
      "Epoch:   3071 loss= 0.895770 acc= 0.808461 cost_time= 0.305970s \n",
      "Epoch:   3072 loss= 0.280060 acc= 0.938156 cost_time= 0.335127s \n",
      "Epoch:   3073 loss= 0.965674 acc= 0.903740 cost_time= 0.312889s \n",
      "Epoch:   3074 loss= 0.444111 acc= 0.873273 cost_time= 0.308768s \n",
      "Epoch:   3075 loss= 0.381920 acc= 0.918273 cost_time= 0.312134s \n",
      "Epoch:   3076 loss= 0.277735 acc= 0.956467 cost_time= 0.312051s \n",
      "Epoch:   3077 loss= 1.130575 acc= 0.900909 cost_time= 0.316427s \n",
      "Epoch:   3078 loss= 0.276247 acc= 0.943961 cost_time= 0.309994s \n",
      "Epoch:   3079 loss= 0.315543 acc= 0.937472 cost_time= 0.330623s \n",
      "Epoch:   3080 loss= 0.247898 acc= 0.953252 cost_time= 0.320014s \n",
      "Epoch:   3081 loss= 0.336696 acc= 0.938541 cost_time= 0.311225s \n",
      "Epoch:   3082 loss= 0.257734 acc= 0.952113 cost_time= 0.309078s \n",
      "Epoch:   3083 loss= 0.271965 acc= 0.945309 cost_time= 0.309600s \n",
      "Epoch:   3084 loss= 0.221098 acc= 0.946564 cost_time= 0.319859s \n",
      "Epoch:   3085 loss= 1.323216 acc= 0.881178 cost_time= 0.313787s \n",
      "Epoch:   3086 loss= 0.250110 acc= 0.944109 cost_time= 0.317700s \n",
      "Epoch:   3087 loss= 0.579631 acc= 0.917688 cost_time= 0.313310s \n",
      "Epoch:   3088 loss= 0.910514 acc= 0.886821 cost_time= 0.310204s \n",
      "Epoch:   3089 loss= 0.253509 acc= 0.951606 cost_time= 0.309906s \n",
      "Epoch:   3090 loss= 1.057983 acc= 0.889937 cost_time= 0.318010s \n",
      "Epoch:   3091 loss= 1.069664 acc= 0.901678 cost_time= 0.310850s \n",
      "Epoch:   3092 loss= 0.363440 acc= 0.924010 cost_time= 0.308827s \n",
      "Epoch:   3093 loss= 0.368264 acc= 0.929913 cost_time= 0.316679s \n",
      "Epoch:   3094 loss= 0.238781 acc= 0.953379 cost_time= 0.312934s \n",
      "Epoch:   3095 loss= 0.285824 acc= 0.942430 cost_time= 0.325706s \n",
      "Epoch:   3096 loss= 0.376094 acc= 0.946402 cost_time= 0.311024s \n",
      "Epoch:   3097 loss= 0.368912 acc= 0.920536 cost_time= 0.308872s \n",
      "Epoch:   3098 loss= 0.301763 acc= 0.938168 cost_time= 0.330575s \n",
      "Epoch:   3099 loss= 0.342449 acc= 0.926579 cost_time= 0.315971s \n",
      "Epoch:   3100 loss= 0.272561 acc= 0.947815 cost_time= 0.309900s \n",
      "Epoch:   3101 loss= 0.250573 acc= 0.947880 cost_time= 0.316165s \n",
      "Epoch:   3102 loss= 0.254071 acc= 0.947862 cost_time= 0.320198s \n",
      "Epoch:   3103 loss= 0.273011 acc= 0.946566 cost_time= 0.312627s \n",
      "Epoch:   3104 loss= 0.322259 acc= 0.950522 cost_time= 0.309098s \n",
      "Epoch:   3105 loss= 0.266229 acc= 0.940859 cost_time= 0.310338s \n",
      "Epoch:   3106 loss= 0.800154 acc= 0.906582 cost_time= 0.312028s \n",
      "Epoch:   3107 loss= 0.253021 acc= 0.940634 cost_time= 0.310598s \n",
      "Epoch:   3108 loss= 0.284886 acc= 0.947700 cost_time= 0.308956s \n",
      "Epoch:   3109 loss= 0.485223 acc= 0.901736 cost_time= 0.314739s \n",
      "Epoch:   3110 loss= 0.340984 acc= 0.934567 cost_time= 0.309044s \n",
      "Epoch:   3111 loss= 0.234297 acc= 0.962911 cost_time= 0.313769s \n",
      "Epoch:   3112 loss= 0.517868 acc= 0.896346 cost_time= 0.312454s \n",
      "Epoch:   3113 loss= 0.264691 acc= 0.948521 cost_time= 0.311926s \n",
      "Epoch:   3114 loss= 0.262038 acc= 0.951813 cost_time= 0.311121s \n",
      "Epoch:   3115 loss= 0.409353 acc= 0.922233 cost_time= 0.310833s \n",
      "Epoch:   3116 loss= 0.337343 acc= 0.935660 cost_time= 0.317313s \n",
      "Epoch:   3117 loss= 1.135631 acc= 0.832558 cost_time= 0.311363s \n",
      "Epoch:   3118 loss= 0.242212 acc= 0.948531 cost_time= 0.312091s \n",
      "Epoch:   3119 loss= 0.441826 acc= 0.913062 cost_time= 0.308576s \n",
      "Epoch:   3120 loss= 1.078657 acc= 0.881067 cost_time= 0.309129s \n",
      "Epoch:   3121 loss= 0.270371 acc= 0.941523 cost_time= 0.308610s \n",
      "Epoch:   3122 loss= 1.062462 acc= 0.832656 cost_time= 0.306581s \n",
      "Epoch:   3123 loss= 0.475082 acc= 0.879767 cost_time= 0.308165s \n",
      "Epoch:   3124 loss= 0.330814 acc= 0.920794 cost_time= 0.332304s \n",
      "Epoch:   3125 loss= 0.203403 acc= 0.947522 cost_time= 0.314358s \n",
      "Epoch:   3126 loss= 0.338379 acc= 0.935821 cost_time= 0.332811s \n",
      "Epoch:   3127 loss= 0.241656 acc= 0.947150 cost_time= 0.314115s \n",
      "Epoch:   3128 loss= 0.708990 acc= 0.915351 cost_time= 0.314857s \n",
      "Epoch:   3129 loss= 0.296846 acc= 0.928921 cost_time= 0.314251s \n",
      "Epoch:   3130 loss= 0.224710 acc= 0.961181 cost_time= 0.319883s \n",
      "Epoch:   3131 loss= 1.229279 acc= 0.885272 cost_time= 0.314416s \n",
      "Epoch:   3132 loss= 0.251184 acc= 0.944176 cost_time= 0.311247s \n",
      "Epoch:   3133 loss= 0.311775 acc= 0.937779 cost_time= 0.313561s \n",
      "Epoch:   3134 loss= 0.324727 acc= 0.925012 cost_time= 0.309453s \n",
      "Epoch:   3135 loss= 0.826598 acc= 0.891868 cost_time= 0.309995s \n",
      "Epoch:   3136 loss= 0.242597 acc= 0.947462 cost_time= 0.314663s \n",
      "Epoch:   3137 loss= 0.455410 acc= 0.927192 cost_time= 0.321381s \n",
      "Epoch:   3138 loss= 1.246700 acc= 0.881112 cost_time= 0.318799s \n",
      "Epoch:   3139 loss= 0.245379 acc= 0.958283 cost_time= 0.317285s \n",
      "Epoch:   3140 loss= 0.263234 acc= 0.946174 cost_time= 0.315908s \n",
      "Epoch:   3141 loss= 0.602871 acc= 0.860178 cost_time= 0.304734s \n",
      "Epoch:   3142 loss= 0.934466 acc= 0.867983 cost_time= 0.315172s \n",
      "Epoch:   3143 loss= 0.229005 acc= 0.947779 cost_time= 0.309680s \n",
      "Epoch:   3144 loss= 0.280543 acc= 0.943124 cost_time= 0.318351s \n",
      "Epoch:   3145 loss= 0.301412 acc= 0.947340 cost_time= 0.310060s \n",
      "Epoch:   3146 loss= 0.232108 acc= 0.953389 cost_time= 0.310520s \n",
      "Epoch:   3147 loss= 0.322816 acc= 0.950208 cost_time= 0.315553s \n",
      "Epoch:   3148 loss= 0.586825 acc= 0.914021 cost_time= 0.309776s \n",
      "Epoch:   3149 loss= 1.262933 acc= 0.858127 cost_time= 0.330224s \n",
      "Epoch:   3150 loss= 0.250002 acc= 0.958371 cost_time= 0.312468s \n",
      "Epoch:   3151 loss= 0.343928 acc= 0.929851 cost_time= 0.307770s \n",
      "Epoch:   3152 loss= 0.606640 acc= 0.909053 cost_time= 0.314690s \n",
      "Epoch:   3153 loss= 0.280245 acc= 0.939553 cost_time= 0.312939s \n",
      "Epoch:   3154 loss= 0.244604 acc= 0.941982 cost_time= 0.312518s \n",
      "Epoch:   3155 loss= 0.285753 acc= 0.935189 cost_time= 0.310719s \n",
      "Epoch:   3156 loss= 0.277011 acc= 0.941960 cost_time= 0.330671s \n",
      "Epoch:   3157 loss= 0.308676 acc= 0.931286 cost_time= 0.308755s \n",
      "Epoch:   3158 loss= 0.468461 acc= 0.904411 cost_time= 0.315610s \n",
      "Epoch:   3159 loss= 0.297747 acc= 0.943289 cost_time= 0.310693s \n",
      "Epoch:   3160 loss= 0.223427 acc= 0.950635 cost_time= 0.313890s \n",
      "Epoch:   3161 loss= 0.342594 acc= 0.945191 cost_time= 0.305146s \n",
      "Epoch:   3162 loss= 0.834578 acc= 0.890761 cost_time= 0.307574s \n",
      "Epoch:   3163 loss= 0.496636 acc= 0.926457 cost_time= 0.308613s \n",
      "Epoch:   3164 loss= 0.237009 acc= 0.946560 cost_time= 0.315851s \n",
      "Epoch:   3165 loss= 0.297212 acc= 0.940301 cost_time= 0.310629s \n",
      "Epoch:   3166 loss= 0.545328 acc= 0.909015 cost_time= 0.309345s \n",
      "Epoch:   3167 loss= 0.273315 acc= 0.942484 cost_time= 0.310626s \n",
      "Epoch:   3168 loss= 0.337823 acc= 0.931257 cost_time= 0.308905s \n",
      "Epoch:   3169 loss= 0.380686 acc= 0.928790 cost_time= 0.315218s \n",
      "Epoch:   3170 loss= 0.462149 acc= 0.894263 cost_time= 0.327399s \n",
      "Epoch:   3171 loss= 0.241732 acc= 0.950341 cost_time= 0.318073s \n",
      "Epoch:   3172 loss= 0.947643 acc= 0.905530 cost_time= 0.308231s \n",
      "Epoch:   3173 loss= 0.842702 acc= 0.913764 cost_time= 0.313237s \n",
      "Epoch:   3174 loss= 0.260249 acc= 0.950607 cost_time= 0.307119s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3175 loss= 0.382333 acc= 0.918371 cost_time= 0.328651s \n",
      "Epoch:   3176 loss= 0.950344 acc= 0.865522 cost_time= 0.303907s \n",
      "Epoch:   3177 loss= 0.303599 acc= 0.944819 cost_time= 0.308247s \n",
      "Epoch:   3178 loss= 0.383795 acc= 0.929266 cost_time= 0.308850s \n",
      "Epoch:   3179 loss= 0.403952 acc= 0.921377 cost_time= 0.306690s \n",
      "Epoch:   3180 loss= 0.928727 acc= 0.867543 cost_time= 0.328091s \n",
      "Epoch:   3181 loss= 0.289802 acc= 0.952659 cost_time= 0.311930s \n",
      "Epoch:   3182 loss= 0.302319 acc= 0.945828 cost_time= 0.307795s \n",
      "Epoch:   3183 loss= 0.344201 acc= 0.934214 cost_time= 0.306940s \n",
      "Epoch:   3184 loss= 0.292392 acc= 0.942085 cost_time= 0.333063s \n",
      "Epoch:   3185 loss= 0.885703 acc= 0.904973 cost_time= 0.307743s \n",
      "Epoch:   3186 loss= 0.394040 acc= 0.933440 cost_time= 0.312486s \n",
      "Epoch:   3187 loss= 0.199282 acc= 0.942425 cost_time= 0.311273s \n",
      "Epoch:   3188 loss= 1.447801 acc= 0.904046 cost_time= 0.310576s \n",
      "Epoch:   3189 loss= 0.564148 acc= 0.905202 cost_time= 0.311528s \n",
      "Epoch:   3190 loss= 0.253770 acc= 0.947847 cost_time= 0.312602s \n",
      "Epoch:   3191 loss= 0.304911 acc= 0.940838 cost_time= 0.311217s \n",
      "Epoch:   3192 loss= 0.292278 acc= 0.937618 cost_time= 0.307930s \n",
      "Epoch:   3193 loss= 0.619563 acc= 0.917337 cost_time= 0.314506s \n",
      "Epoch:   3194 loss= 0.670973 acc= 0.905189 cost_time= 0.313726s \n",
      "Epoch:   3195 loss= 0.229233 acc= 0.950941 cost_time= 0.312675s \n",
      "Epoch:   3196 loss= 0.587984 acc= 0.889170 cost_time= 0.306424s \n",
      "Epoch:   3197 loss= 0.312396 acc= 0.938852 cost_time= 0.309348s \n",
      "Epoch:   3198 loss= 1.094012 acc= 0.908504 cost_time= 0.328492s \n",
      "Epoch:   3199 loss= 0.316946 acc= 0.946326 cost_time= 0.308605s \n",
      "Epoch:   3200 loss= 0.322917 acc= 0.941578 cost_time= 0.318187s \n",
      "Epoch:   3201 loss= 0.577528 acc= 0.909940 cost_time= 0.306455s \n",
      "Epoch:   3202 loss= 0.303763 acc= 0.918475 cost_time= 0.309582s \n",
      "Epoch:   3203 loss= 0.382100 acc= 0.923902 cost_time= 0.310628s \n",
      "Epoch:   3204 loss= 0.322801 acc= 0.942269 cost_time= 0.310838s \n",
      "Epoch:   3205 loss= 0.447035 acc= 0.917988 cost_time= 0.308598s \n",
      "Epoch:   3206 loss= 0.370755 acc= 0.936259 cost_time= 0.332762s \n",
      "Epoch:   3207 loss= 0.496481 acc= 0.906407 cost_time= 0.326966s \n",
      "Epoch:   3208 loss= 0.240080 acc= 0.947195 cost_time= 0.308668s \n",
      "Epoch:   3209 loss= 0.269022 acc= 0.944029 cost_time= 0.315924s \n",
      "Epoch:   3210 loss= 1.135770 acc= 0.886661 cost_time= 0.310116s \n",
      "Epoch:   3211 loss= 0.512382 acc= 0.877621 cost_time= 0.306493s \n",
      "Epoch:   3212 loss= 0.269502 acc= 0.944303 cost_time= 0.312365s \n",
      "Epoch:   3213 loss= 0.776883 acc= 0.868406 cost_time= 0.312985s \n",
      "Epoch:   3214 loss= 0.300722 acc= 0.933034 cost_time= 0.331874s \n",
      "Epoch:   3215 loss= 0.336286 acc= 0.924104 cost_time= 0.319417s \n",
      "Epoch:   3216 loss= 0.246723 acc= 0.953564 cost_time= 0.306928s \n",
      "Epoch:   3217 loss= 0.372537 acc= 0.923674 cost_time= 0.310801s \n",
      "Epoch:   3218 loss= 0.393417 acc= 0.918933 cost_time= 0.304352s \n",
      "Epoch:   3219 loss= 0.987254 acc= 0.899332 cost_time= 0.323310s \n",
      "Epoch:   3220 loss= 0.253421 acc= 0.955438 cost_time= 0.313405s \n",
      "Epoch:   3221 loss= 0.287265 acc= 0.941877 cost_time= 0.307884s \n",
      "Epoch:   3222 loss= 0.336633 acc= 0.929839 cost_time= 0.312080s \n",
      "Epoch:   3223 loss= 0.310151 acc= 0.926306 cost_time= 0.319393s \n",
      "Epoch:   3224 loss= 0.397579 acc= 0.926050 cost_time= 0.310257s \n",
      "Epoch:   3225 loss= 0.224597 acc= 0.946148 cost_time= 0.310119s \n",
      "Epoch:   3226 loss= 0.528195 acc= 0.916353 cost_time= 0.312177s \n",
      "Epoch:   3227 loss= 0.283643 acc= 0.945516 cost_time= 0.317291s \n",
      "Epoch:   3228 loss= 0.634672 acc= 0.868184 cost_time= 0.306392s \n",
      "Epoch:   3229 loss= 0.250793 acc= 0.955003 cost_time= 0.312597s \n",
      "Epoch:   3230 loss= 1.109908 acc= 0.881472 cost_time= 0.308266s \n",
      "Epoch:   3231 loss= 0.257400 acc= 0.939968 cost_time= 0.307620s \n",
      "Epoch:   3232 loss= 0.320225 acc= 0.927141 cost_time= 0.308690s \n",
      "Epoch:   3233 loss= 1.095223 acc= 0.895756 cost_time= 0.312521s \n",
      "Epoch:   3234 loss= 0.440236 acc= 0.915290 cost_time= 0.304132s \n",
      "Epoch:   3235 loss= 0.292870 acc= 0.952002 cost_time= 0.336251s \n",
      "Epoch:   3236 loss= 0.393604 acc= 0.924799 cost_time= 0.306390s \n",
      "Epoch:   3237 loss= 0.684843 acc= 0.876130 cost_time= 0.323012s \n",
      "Epoch:   3238 loss= 0.491263 acc= 0.923989 cost_time= 0.312119s \n",
      "Epoch:   3239 loss= 0.386634 acc= 0.930244 cost_time= 0.310179s \n",
      "Epoch:   3240 loss= 0.262109 acc= 0.939142 cost_time= 0.312179s \n",
      "Epoch:   3241 loss= 0.326514 acc= 0.942085 cost_time= 0.308421s \n",
      "Epoch:   3242 loss= 0.266682 acc= 0.941444 cost_time= 0.308482s \n",
      "Epoch:   3243 loss= 0.301830 acc= 0.936693 cost_time= 0.313379s \n",
      "Epoch:   3244 loss= 0.400985 acc= 0.912193 cost_time= 0.317144s \n",
      "Epoch:   3245 loss= 0.330429 acc= 0.942730 cost_time= 0.316818s \n",
      "Epoch:   3246 loss= 0.292314 acc= 0.953342 cost_time= 0.307410s \n",
      "Epoch:   3247 loss= 0.343003 acc= 0.934051 cost_time= 0.309186s \n",
      "Epoch:   3248 loss= 0.549770 acc= 0.887254 cost_time= 0.307173s \n",
      "Epoch:   3249 loss= 0.414943 acc= 0.920033 cost_time= 0.329612s \n",
      "Epoch:   3250 loss= 0.385056 acc= 0.925242 cost_time= 0.313589s \n",
      "Epoch:   3251 loss= 0.422270 acc= 0.923020 cost_time= 0.321984s \n",
      "Epoch:   3252 loss= 0.449890 acc= 0.920623 cost_time= 0.313405s \n",
      "Epoch:   3253 loss= 0.260710 acc= 0.940959 cost_time= 0.310852s \n",
      "Epoch:   3254 loss= 0.276269 acc= 0.939806 cost_time= 0.313939s \n",
      "Epoch:   3255 loss= 0.432947 acc= 0.902173 cost_time= 0.308061s \n",
      "Epoch:   3256 loss= 0.439199 acc= 0.913900 cost_time= 0.324953s \n",
      "Epoch:   3257 loss= 0.308668 acc= 0.940561 cost_time= 0.311764s \n",
      "Epoch:   3258 loss= 0.739665 acc= 0.915071 cost_time= 0.317609s \n",
      "Epoch:   3259 loss= 0.345279 acc= 0.928037 cost_time= 0.304474s \n",
      "Epoch:   3260 loss= 0.425395 acc= 0.915395 cost_time= 0.308768s \n",
      "Epoch:   3261 loss= 0.335528 acc= 0.934028 cost_time= 0.316794s \n",
      "Epoch:   3262 loss= 0.247109 acc= 0.946951 cost_time= 0.312438s \n",
      "Epoch:   3263 loss= 0.197207 acc= 0.969402 cost_time= 0.335255s \n",
      "Epoch:   3264 loss= 0.462355 acc= 0.900569 cost_time= 0.307531s \n",
      "Epoch:   3265 loss= 0.290228 acc= 0.941120 cost_time= 0.315488s \n",
      "Epoch:   3266 loss= 0.355248 acc= 0.934232 cost_time= 0.308407s \n",
      "Epoch:   3267 loss= 0.338761 acc= 0.929924 cost_time= 0.308923s \n",
      "Epoch:   3268 loss= 0.353254 acc= 0.921911 cost_time= 0.309466s \n",
      "Epoch:   3269 loss= 0.589857 acc= 0.906479 cost_time= 0.311010s \n",
      "Epoch:   3270 loss= 0.387158 acc= 0.912927 cost_time= 0.330939s \n",
      "Epoch:   3271 loss= 0.334934 acc= 0.929992 cost_time= 0.309106s \n",
      "Epoch:   3272 loss= 0.235468 acc= 0.945548 cost_time= 0.322915s \n",
      "Epoch:   3273 loss= 0.273724 acc= 0.948423 cost_time= 0.312536s \n",
      "Epoch:   3274 loss= 0.918395 acc= 0.910804 cost_time= 0.308964s \n",
      "Epoch:   3275 loss= 0.306117 acc= 0.933303 cost_time= 0.308162s \n",
      "Epoch:   3276 loss= 0.552153 acc= 0.911326 cost_time= 0.307932s \n",
      "Epoch:   3277 loss= 0.352922 acc= 0.938397 cost_time= 0.329914s \n",
      "Epoch:   3278 loss= 0.793703 acc= 0.875007 cost_time= 0.304860s \n",
      "Epoch:   3279 loss= 0.737515 acc= 0.892265 cost_time= 0.326111s \n",
      "Epoch:   3280 loss= 0.284677 acc= 0.936956 cost_time= 0.313975s \n",
      "Epoch:   3281 loss= 0.300388 acc= 0.936711 cost_time= 0.316649s \n",
      "Epoch:   3282 loss= 0.530800 acc= 0.862635 cost_time= 0.302587s \n",
      "Epoch:   3283 loss= 0.345006 acc= 0.921023 cost_time= 0.310697s \n",
      "Epoch:   3284 loss= 0.355838 acc= 0.928347 cost_time= 0.302470s \n",
      "Epoch:   3285 loss= 0.281812 acc= 0.944679 cost_time= 0.308851s \n",
      "Epoch:   3286 loss= 0.306909 acc= 0.934353 cost_time= 0.321695s \n",
      "Epoch:   3287 loss= 0.255757 acc= 0.946258 cost_time= 0.312261s \n",
      "Epoch:   3288 loss= 0.267725 acc= 0.939061 cost_time= 0.312089s \n",
      "Epoch:   3289 loss= 1.310148 acc= 0.889811 cost_time= 0.313360s \n",
      "Epoch:   3290 loss= 0.829766 acc= 0.870478 cost_time= 0.308258s \n",
      "Epoch:   3291 loss= 0.535541 acc= 0.897336 cost_time= 0.306525s \n",
      "Epoch:   3292 loss= 1.247092 acc= 0.886899 cost_time= 0.313895s \n",
      "Epoch:   3293 loss= 0.312446 acc= 0.938677 cost_time= 0.315687s \n",
      "Epoch:   3294 loss= 0.278200 acc= 0.949941 cost_time= 0.313262s \n",
      "Epoch:   3295 loss= 0.280373 acc= 0.931825 cost_time= 0.314207s \n",
      "Epoch:   3296 loss= 0.296932 acc= 0.948944 cost_time= 0.311677s \n",
      "Epoch:   3297 loss= 0.305983 acc= 0.934106 cost_time= 0.307464s \n",
      "Epoch:   3298 loss= 0.311904 acc= 0.943783 cost_time= 0.332408s \n",
      "Epoch:   3299 loss= 0.354367 acc= 0.944732 cost_time= 0.307524s \n",
      "Epoch:   3300 loss= 1.126237 acc= 0.837900 cost_time= 0.318739s \n",
      "Epoch:   3301 loss= 0.245141 acc= 0.951901 cost_time= 0.316634s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3302 loss= 0.268066 acc= 0.930467 cost_time= 0.310455s \n",
      "Epoch:   3303 loss= 0.492062 acc= 0.910383 cost_time= 0.303188s \n",
      "Epoch:   3304 loss= 0.449747 acc= 0.889476 cost_time= 0.306447s \n",
      "Epoch:   3305 loss= 0.372670 acc= 0.938927 cost_time= 0.324326s \n",
      "Epoch:   3306 loss= 0.379146 acc= 0.921415 cost_time= 0.310303s \n",
      "Epoch:   3307 loss= 0.696333 acc= 0.833987 cost_time= 0.309617s \n",
      "Epoch:   3308 loss= 0.245269 acc= 0.945077 cost_time= 0.310293s \n",
      "Epoch:   3309 loss= 0.285304 acc= 0.941874 cost_time= 0.313222s \n",
      "Epoch:   3310 loss= 0.825607 acc= 0.904546 cost_time= 0.327145s \n",
      "Epoch:   3311 loss= 0.615202 acc= 0.914262 cost_time= 0.316389s \n",
      "Epoch:   3312 loss= 0.313188 acc= 0.930619 cost_time= 0.311512s \n",
      "Epoch:   3313 loss= 0.230352 acc= 0.950648 cost_time= 0.312434s \n",
      "Epoch:   3314 loss= 0.270449 acc= 0.951061 cost_time= 0.309482s \n",
      "Epoch:   3315 loss= 0.362141 acc= 0.918944 cost_time= 0.304476s \n",
      "Epoch:   3316 loss= 0.355935 acc= 0.936220 cost_time= 0.307489s \n",
      "Epoch:   3317 loss= 0.213480 acc= 0.955450 cost_time= 0.330350s \n",
      "Epoch:   3318 loss= 0.274879 acc= 0.933529 cost_time= 0.310216s \n",
      "Epoch:   3319 loss= 0.351428 acc= 0.930907 cost_time= 0.315512s \n",
      "Epoch:   3320 loss= 0.241055 acc= 0.954178 cost_time= 0.311630s \n",
      "Epoch:   3321 loss= 0.668428 acc= 0.869452 cost_time= 0.315246s \n",
      "Epoch:   3322 loss= 0.276369 acc= 0.946235 cost_time= 0.314000s \n",
      "Epoch:   3323 loss= 0.294255 acc= 0.941942 cost_time= 0.313221s \n",
      "Epoch:   3324 loss= 0.372229 acc= 0.918889 cost_time= 0.311807s \n",
      "Epoch:   3325 loss= 0.432912 acc= 0.921825 cost_time= 0.308088s \n",
      "Epoch:   3326 loss= 0.440149 acc= 0.924326 cost_time= 0.310250s \n",
      "Epoch:   3327 loss= 0.345442 acc= 0.920997 cost_time= 0.308016s \n",
      "Epoch:   3328 loss= 0.626226 acc= 0.874041 cost_time= 0.311979s \n",
      "Epoch:   3329 loss= 0.307142 acc= 0.946108 cost_time= 0.310133s \n",
      "Epoch:   3330 loss= 0.374712 acc= 0.932557 cost_time= 0.304890s \n",
      "Epoch:   3331 loss= 0.335671 acc= 0.937719 cost_time= 0.310361s \n",
      "Epoch:   3332 loss= 0.276288 acc= 0.939477 cost_time= 0.311198s \n",
      "Epoch:   3333 loss= 0.676249 acc= 0.821901 cost_time= 0.326094s \n",
      "Epoch:   3334 loss= 1.326574 acc= 0.846663 cost_time= 0.313066s \n",
      "Epoch:   3335 loss= 0.218409 acc= 0.956058 cost_time= 0.312319s \n",
      "Epoch:   3336 loss= 0.398162 acc= 0.933071 cost_time= 0.309892s \n",
      "Epoch:   3337 loss= 0.213905 acc= 0.954765 cost_time= 0.313794s \n",
      "Epoch:   3338 loss= 0.272821 acc= 0.955870 cost_time= 0.309391s \n",
      "Epoch:   3339 loss= 0.290576 acc= 0.942350 cost_time= 0.313409s \n",
      "Epoch:   3340 loss= 0.310284 acc= 0.943358 cost_time= 0.311743s \n",
      "Epoch:   3341 loss= 0.241778 acc= 0.951200 cost_time= 0.332101s \n",
      "Epoch:   3342 loss= 0.459923 acc= 0.883853 cost_time= 0.312677s \n",
      "Epoch:   3343 loss= 0.322275 acc= 0.947257 cost_time= 0.314059s \n",
      "Epoch:   3344 loss= 0.336694 acc= 0.933982 cost_time= 0.308553s \n",
      "Epoch:   3345 loss= 0.349237 acc= 0.938983 cost_time= 0.315415s \n",
      "Epoch:   3346 loss= 0.450770 acc= 0.911367 cost_time= 0.308888s \n",
      "Epoch:   3347 loss= 0.231096 acc= 0.954999 cost_time= 0.332850s \n",
      "Epoch:   3348 loss= 0.555184 acc= 0.918820 cost_time= 0.313491s \n",
      "Epoch:   3349 loss= 0.275955 acc= 0.942530 cost_time= 0.314725s \n",
      "Epoch:   3350 loss= 1.131832 acc= 0.836012 cost_time= 0.304085s \n",
      "Epoch:   3351 loss= 0.265214 acc= 0.944379 cost_time= 0.310463s \n",
      "Epoch:   3352 loss= 0.616922 acc= 0.892325 cost_time= 0.309696s \n",
      "Epoch:   3353 loss= 0.400736 acc= 0.911100 cost_time= 0.307436s \n",
      "Epoch:   3354 loss= 0.669841 acc= 0.905908 cost_time= 0.328940s \n",
      "Epoch:   3355 loss= 0.294672 acc= 0.922860 cost_time= 0.306132s \n",
      "Epoch:   3356 loss= 0.773532 acc= 0.898688 cost_time= 0.317261s \n",
      "Epoch:   3357 loss= 0.325590 acc= 0.936746 cost_time= 0.310807s \n",
      "Epoch:   3358 loss= 0.272711 acc= 0.939405 cost_time= 0.313741s \n",
      "Epoch:   3359 loss= 0.298731 acc= 0.945902 cost_time= 0.310766s \n",
      "Epoch:   3360 loss= 0.302224 acc= 0.949679 cost_time= 0.313089s \n",
      "Epoch:   3361 loss= 0.350893 acc= 0.938237 cost_time= 0.309382s \n",
      "Epoch:   3362 loss= 0.277249 acc= 0.942682 cost_time= 0.312846s \n",
      "Epoch:   3363 loss= 1.077585 acc= 0.857058 cost_time= 0.312604s \n",
      "Epoch:   3364 loss= 0.215024 acc= 0.950978 cost_time= 0.315163s \n",
      "Epoch:   3365 loss= 0.290291 acc= 0.943362 cost_time= 0.309695s \n",
      "Epoch:   3366 loss= 1.016868 acc= 0.889437 cost_time= 0.310414s \n",
      "Epoch:   3367 loss= 0.251638 acc= 0.946934 cost_time= 0.312444s \n",
      "Epoch:   3368 loss= 0.404433 acc= 0.913728 cost_time= 0.328398s \n",
      "Epoch:   3369 loss= 0.303284 acc= 0.944052 cost_time= 0.310259s \n",
      "Epoch:   3370 loss= 0.276138 acc= 0.933697 cost_time= 0.308231s \n",
      "Epoch:   3371 loss= 0.304952 acc= 0.939282 cost_time= 0.310942s \n",
      "Epoch:   3372 loss= 0.229177 acc= 0.952122 cost_time= 0.310582s \n",
      "Epoch:   3373 loss= 0.345015 acc= 0.931138 cost_time= 0.312216s \n",
      "Epoch:   3374 loss= 0.267790 acc= 0.950431 cost_time= 0.311899s \n",
      "Epoch:   3375 loss= 0.249259 acc= 0.951294 cost_time= 0.331260s \n",
      "Epoch:   3376 loss= 0.304723 acc= 0.944172 cost_time= 0.317906s \n",
      "Epoch:   3377 loss= 0.328195 acc= 0.930215 cost_time= 0.308618s \n",
      "Epoch:   3378 loss= 0.377950 acc= 0.924129 cost_time= 0.306788s \n",
      "Epoch:   3379 loss= 0.345530 acc= 0.927498 cost_time= 0.308581s \n",
      "Epoch:   3380 loss= 0.484225 acc= 0.897763 cost_time= 0.327171s \n",
      "Epoch:   3381 loss= 0.277391 acc= 0.943681 cost_time= 0.308280s \n",
      "Epoch:   3382 loss= 0.232138 acc= 0.941763 cost_time= 0.311753s \n",
      "Epoch:   3383 loss= 0.311813 acc= 0.931759 cost_time= 0.310230s \n",
      "Epoch:   3384 loss= 0.267511 acc= 0.947609 cost_time= 0.307360s \n",
      "Epoch:   3385 loss= 0.282963 acc= 0.926849 cost_time= 0.310327s \n",
      "Epoch:   3386 loss= 1.354822 acc= 0.896112 cost_time= 0.313683s \n",
      "Epoch:   3387 loss= 0.437751 acc= 0.908999 cost_time= 0.327578s \n",
      "Epoch:   3388 loss= 0.232623 acc= 0.962143 cost_time= 0.310111s \n",
      "Epoch:   3389 loss= 0.292391 acc= 0.951594 cost_time= 0.310581s \n",
      "Epoch:   3390 loss= 0.404996 acc= 0.926707 cost_time= 0.308421s \n",
      "Epoch:   3391 loss= 0.242891 acc= 0.941003 cost_time= 0.324653s \n",
      "Epoch:   3392 loss= 0.329535 acc= 0.911633 cost_time= 0.304610s \n",
      "Epoch:   3393 loss= 0.234084 acc= 0.948049 cost_time= 0.315315s \n",
      "Epoch:   3394 loss= 0.282539 acc= 0.945094 cost_time= 0.308877s \n",
      "Epoch:   3395 loss= 0.445733 acc= 0.903542 cost_time= 0.307552s \n",
      "Epoch:   3396 loss= 0.269446 acc= 0.939893 cost_time= 0.330333s \n",
      "Epoch:   3397 loss= 0.284057 acc= 0.943439 cost_time= 0.314204s \n",
      "Epoch:   3398 loss= 1.845395 acc= 0.846549 cost_time= 0.317431s \n",
      "Epoch:   3399 loss= 0.809225 acc= 0.852909 cost_time= 0.305568s \n",
      "Epoch:   3400 loss= 0.283244 acc= 0.942925 cost_time= 0.310842s \n",
      "Epoch:   3401 loss= 0.205934 acc= 0.952569 cost_time= 0.312432s \n",
      "Epoch:   3402 loss= 0.754235 acc= 0.895804 cost_time= 0.330261s \n",
      "Epoch:   3403 loss= 0.239864 acc= 0.952459 cost_time= 0.311698s \n",
      "Epoch:   3404 loss= 0.253714 acc= 0.959443 cost_time= 0.334177s \n",
      "Epoch:   3405 loss= 0.384972 acc= 0.935641 cost_time= 0.330525s \n",
      "Epoch:   3406 loss= 0.208060 acc= 0.955249 cost_time= 0.311502s \n",
      "Epoch:   3407 loss= 0.945970 acc= 0.880335 cost_time= 0.311943s \n",
      "Epoch:   3408 loss= 0.850441 acc= 0.914498 cost_time= 0.308534s \n",
      "Epoch:   3409 loss= 0.274578 acc= 0.939209 cost_time= 0.311335s \n",
      "Epoch:   3410 loss= 0.557773 acc= 0.853485 cost_time= 0.333219s \n",
      "Epoch:   3411 loss= 0.264345 acc= 0.931274 cost_time= 0.308255s \n",
      "Epoch:   3412 loss= 0.309465 acc= 0.953863 cost_time= 0.314595s \n",
      "Epoch:   3413 loss= 0.300794 acc= 0.936598 cost_time= 0.309952s \n",
      "Epoch:   3414 loss= 0.383132 acc= 0.930169 cost_time= 0.310391s \n",
      "Epoch:   3415 loss= 0.936086 acc= 0.885793 cost_time= 0.311061s \n",
      "Epoch:   3416 loss= 0.243691 acc= 0.949017 cost_time= 0.310544s \n",
      "Epoch:   3417 loss= 0.334986 acc= 0.932850 cost_time= 0.330414s \n",
      "Epoch:   3418 loss= 1.113157 acc= 0.888911 cost_time= 0.311161s \n",
      "Epoch:   3419 loss= 0.293707 acc= 0.944394 cost_time= 0.329473s \n",
      "Epoch:   3420 loss= 0.495063 acc= 0.875482 cost_time= 0.306129s \n",
      "Epoch:   3421 loss= 0.252192 acc= 0.948793 cost_time= 0.309450s \n",
      "Epoch:   3422 loss= 0.354179 acc= 0.945582 cost_time= 0.311556s \n",
      "Epoch:   3423 loss= 0.420446 acc= 0.903040 cost_time= 0.312707s \n",
      "Epoch:   3424 loss= 0.293251 acc= 0.932471 cost_time= 0.310178s \n",
      "Epoch:   3425 loss= 0.504962 acc= 0.883632 cost_time= 0.309479s \n",
      "Epoch:   3426 loss= 0.244386 acc= 0.952378 cost_time= 0.331913s \n",
      "Epoch:   3427 loss= 0.325649 acc= 0.938856 cost_time= 0.315780s \n",
      "Epoch:   3428 loss= 0.337392 acc= 0.930082 cost_time= 0.317944s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3429 loss= 0.501837 acc= 0.899435 cost_time= 0.313933s \n",
      "Epoch:   3430 loss= 0.725555 acc= 0.856961 cost_time= 0.305098s \n",
      "Epoch:   3431 loss= 0.255395 acc= 0.946239 cost_time= 0.310691s \n",
      "Epoch:   3432 loss= 0.244912 acc= 0.952003 cost_time= 0.310761s \n",
      "Epoch:   3433 loss= 0.422870 acc= 0.916389 cost_time= 0.306660s \n",
      "Epoch:   3434 loss= 0.289409 acc= 0.945920 cost_time= 0.308449s \n",
      "Epoch:   3435 loss= 0.391137 acc= 0.926907 cost_time= 0.313560s \n",
      "Epoch:   3436 loss= 0.286107 acc= 0.944229 cost_time= 0.310796s \n",
      "Epoch:   3437 loss= 0.219008 acc= 0.950461 cost_time= 0.311809s \n",
      "Epoch:   3438 loss= 1.245918 acc= 0.866192 cost_time= 0.309540s \n",
      "Epoch:   3439 loss= 0.463960 acc= 0.864218 cost_time= 0.311211s \n",
      "Epoch:   3440 loss= 1.246840 acc= 0.869711 cost_time= 0.304906s \n",
      "Epoch:   3441 loss= 0.812829 acc= 0.894708 cost_time= 0.318046s \n",
      "Epoch:   3442 loss= 0.267092 acc= 0.946253 cost_time= 0.307618s \n",
      "Epoch:   3443 loss= 0.308716 acc= 0.939290 cost_time= 0.331813s \n",
      "Epoch:   3444 loss= 0.382387 acc= 0.922151 cost_time= 0.308648s \n",
      "Epoch:   3445 loss= 0.219098 acc= 0.947211 cost_time= 0.333178s \n",
      "Epoch:   3446 loss= 0.439183 acc= 0.908426 cost_time= 0.307883s \n",
      "Epoch:   3447 loss= 0.466009 acc= 0.897026 cost_time= 0.309027s \n",
      "Epoch:   3448 loss= 0.235890 acc= 0.952162 cost_time= 0.313887s \n",
      "Epoch:   3449 loss= 0.295939 acc= 0.931363 cost_time= 0.314985s \n",
      "Epoch:   3450 loss= 0.271830 acc= 0.939673 cost_time= 0.327016s \n",
      "Epoch:   3451 loss= 0.263666 acc= 0.940506 cost_time= 0.311861s \n",
      "Epoch:   3452 loss= 0.277344 acc= 0.938631 cost_time= 0.315292s \n",
      "Epoch:   3453 loss= 0.466476 acc= 0.910168 cost_time= 0.310377s \n",
      "Epoch:   3454 loss= 0.601402 acc= 0.908304 cost_time= 0.333337s \n",
      "Epoch:   3455 loss= 0.420892 acc= 0.912827 cost_time= 0.306210s \n",
      "Epoch:   3456 loss= 0.325476 acc= 0.922078 cost_time= 0.312397s \n",
      "Epoch:   3457 loss= 0.597319 acc= 0.841347 cost_time= 0.305915s \n",
      "Epoch:   3458 loss= 0.459713 acc= 0.889647 cost_time= 0.304789s \n",
      "Epoch:   3459 loss= 0.367713 acc= 0.926420 cost_time= 0.312296s \n",
      "Epoch:   3460 loss= 0.974332 acc= 0.891778 cost_time= 0.307005s \n",
      "Epoch:   3461 loss= 0.274112 acc= 0.948135 cost_time= 0.330760s \n",
      "Epoch:   3462 loss= 0.529378 acc= 0.923664 cost_time= 0.311545s \n",
      "Epoch:   3463 loss= 0.299666 acc= 0.951180 cost_time= 0.308872s \n",
      "Epoch:   3464 loss= 0.463338 acc= 0.915121 cost_time= 0.309858s \n",
      "Epoch:   3465 loss= 0.218668 acc= 0.951560 cost_time= 0.315073s \n",
      "Epoch:   3466 loss= 0.216446 acc= 0.958052 cost_time= 0.332359s \n",
      "Epoch:   3467 loss= 0.694788 acc= 0.910209 cost_time= 0.321246s \n",
      "Epoch:   3468 loss= 0.277982 acc= 0.934868 cost_time= 0.334026s \n",
      "Epoch:   3469 loss= 0.754455 acc= 0.870442 cost_time= 0.308558s \n",
      "Epoch:   3470 loss= 0.504870 acc= 0.872390 cost_time= 0.306023s \n",
      "Epoch:   3471 loss= 0.254530 acc= 0.940687 cost_time= 0.313139s \n",
      "Epoch:   3472 loss= 0.725526 acc= 0.901616 cost_time= 0.310354s \n",
      "Epoch:   3473 loss= 0.319967 acc= 0.931023 cost_time= 0.312369s \n",
      "Epoch:   3474 loss= 0.165327 acc= 0.960503 cost_time= 0.315592s \n",
      "Epoch:   3475 loss= 0.528050 acc= 0.863724 cost_time= 0.331012s \n",
      "Epoch:   3476 loss= 0.406485 acc= 0.904241 cost_time= 0.311512s \n",
      "Epoch:   3477 loss= 0.262531 acc= 0.949469 cost_time= 0.310926s \n",
      "Epoch:   3478 loss= 0.924059 acc= 0.876914 cost_time= 0.306648s \n",
      "Epoch:   3479 loss= 0.268324 acc= 0.947732 cost_time= 0.309656s \n",
      "Epoch:   3480 loss= 0.418784 acc= 0.906955 cost_time= 0.305093s \n",
      "Epoch:   3481 loss= 0.382950 acc= 0.922154 cost_time= 0.307918s \n",
      "Epoch:   3482 loss= 0.314934 acc= 0.944072 cost_time= 0.333822s \n",
      "Epoch:   3483 loss= 0.492069 acc= 0.908791 cost_time= 0.306903s \n",
      "Epoch:   3484 loss= 0.268204 acc= 0.942595 cost_time= 0.322060s \n",
      "Epoch:   3485 loss= 0.647147 acc= 0.900836 cost_time= 0.309060s \n",
      "Epoch:   3486 loss= 0.292831 acc= 0.931896 cost_time= 0.318969s \n",
      "Epoch:   3487 loss= 0.522976 acc= 0.917816 cost_time= 0.308320s \n",
      "Epoch:   3488 loss= 0.331843 acc= 0.937212 cost_time= 0.310265s \n",
      "Epoch:   3489 loss= 0.258497 acc= 0.953801 cost_time= 0.312397s \n",
      "Epoch:   3490 loss= 0.383850 acc= 0.914821 cost_time= 0.309039s \n",
      "Epoch:   3491 loss= 0.293755 acc= 0.932467 cost_time= 0.314309s \n",
      "Epoch:   3492 loss= 0.305295 acc= 0.945645 cost_time= 0.313469s \n",
      "Epoch:   3493 loss= 0.317692 acc= 0.923373 cost_time= 0.309844s \n",
      "Epoch:   3494 loss= 0.866009 acc= 0.885701 cost_time= 0.307221s \n",
      "Epoch:   3495 loss= 0.519060 acc= 0.902304 cost_time= 0.311470s \n",
      "Epoch:   3496 loss= 0.249569 acc= 0.947967 cost_time= 0.314425s \n",
      "Epoch:   3497 loss= 0.295732 acc= 0.937334 cost_time= 0.308700s \n",
      "Epoch:   3498 loss= 0.480247 acc= 0.892335 cost_time= 0.306106s \n",
      "Epoch:   3499 loss= 0.987855 acc= 0.904831 cost_time= 0.315650s \n",
      "Epoch:   3500 loss= 0.236453 acc= 0.955625 cost_time= 0.318721s \n",
      "Epoch:   3501 loss= 0.251678 acc= 0.943125 cost_time= 0.309687s \n",
      "Epoch:   3502 loss= 0.248895 acc= 0.945003 cost_time= 0.311056s \n",
      "Epoch:   3503 loss= 0.222423 acc= 0.948574 cost_time= 0.331038s \n",
      "Epoch:   3504 loss= 0.416014 acc= 0.907890 cost_time= 0.309786s \n",
      "Epoch:   3505 loss= 0.245020 acc= 0.946327 cost_time= 0.316964s \n",
      "Epoch:   3506 loss= 0.296840 acc= 0.945303 cost_time= 0.323611s \n",
      "Epoch:   3507 loss= 0.361185 acc= 0.903315 cost_time= 0.309696s \n",
      "Epoch:   3508 loss= 0.444740 acc= 0.895497 cost_time= 0.327057s \n",
      "Epoch:   3509 loss= 0.297905 acc= 0.943406 cost_time= 0.310220s \n",
      "Epoch:   3510 loss= 0.362994 acc= 0.922996 cost_time= 0.306293s \n",
      "Epoch:   3511 loss= 0.266538 acc= 0.937301 cost_time= 0.306643s \n",
      "Epoch:   3512 loss= 0.491284 acc= 0.870631 cost_time= 0.317171s \n",
      "Epoch:   3513 loss= 0.182434 acc= 0.946561 cost_time= 0.315877s \n",
      "Epoch:   3514 loss= 0.616876 acc= 0.862006 cost_time= 0.306524s \n",
      "Epoch:   3515 loss= 0.240227 acc= 0.943678 cost_time= 0.310061s \n",
      "Epoch:   3516 loss= 0.309743 acc= 0.944107 cost_time= 0.313998s \n",
      "Epoch:   3517 loss= 0.241961 acc= 0.945611 cost_time= 0.316535s \n",
      "Epoch:   3518 loss= 0.667302 acc= 0.924888 cost_time= 0.313304s \n",
      "Epoch:   3519 loss= 0.283528 acc= 0.946916 cost_time= 0.320187s \n",
      "Epoch:   3520 loss= 0.248016 acc= 0.957825 cost_time= 0.313255s \n",
      "Epoch:   3521 loss= 0.276354 acc= 0.947986 cost_time= 0.312252s \n",
      "Epoch:   3522 loss= 0.466406 acc= 0.889174 cost_time= 0.305228s \n",
      "Epoch:   3523 loss= 1.196409 acc= 0.884971 cost_time= 0.309875s \n",
      "Epoch:   3524 loss= 0.560310 acc= 0.877343 cost_time= 0.307306s \n",
      "Epoch:   3525 loss= 0.852726 acc= 0.894512 cost_time= 0.317352s \n",
      "Epoch:   3526 loss= 0.253962 acc= 0.953664 cost_time= 0.310732s \n",
      "Epoch:   3527 loss= 0.272567 acc= 0.940924 cost_time= 0.332359s \n",
      "Epoch:   3528 loss= 0.309549 acc= 0.942369 cost_time= 0.309384s \n",
      "Epoch:   3529 loss= 0.400091 acc= 0.928792 cost_time= 0.327552s \n",
      "Epoch:   3530 loss= 0.740848 acc= 0.902394 cost_time= 0.311879s \n",
      "Epoch:   3531 loss= 0.668020 acc= 0.904150 cost_time= 0.307484s \n",
      "Epoch:   3532 loss= 0.370841 acc= 0.931170 cost_time= 0.315322s \n",
      "Epoch:   3533 loss= 0.267630 acc= 0.950400 cost_time= 0.333715s \n",
      "Epoch:   3534 loss= 0.326761 acc= 0.930790 cost_time= 0.308279s \n",
      "Epoch:   3535 loss= 0.476528 acc= 0.926361 cost_time= 0.323550s \n",
      "Epoch:   3536 loss= 0.246307 acc= 0.955262 cost_time= 0.315217s \n",
      "Epoch:   3537 loss= 0.246741 acc= 0.953008 cost_time= 0.315231s \n",
      "Epoch:   3538 loss= 0.285915 acc= 0.940866 cost_time= 0.318343s \n",
      "Epoch:   3539 loss= 0.432751 acc= 0.901462 cost_time= 0.311620s \n",
      "Epoch:   3540 loss= 0.253011 acc= 0.944873 cost_time= 0.313584s \n",
      "Epoch:   3541 loss= 0.711961 acc= 0.861222 cost_time= 0.299754s \n",
      "Epoch:   3542 loss= 0.292773 acc= 0.945841 cost_time= 0.322424s \n",
      "Epoch:   3543 loss= 0.283234 acc= 0.957566 cost_time= 0.311393s \n",
      "Epoch:   3544 loss= 0.215272 acc= 0.947109 cost_time= 0.314575s \n",
      "Epoch:   3545 loss= 0.276059 acc= 0.948361 cost_time= 0.308269s \n",
      "Epoch:   3546 loss= 0.460953 acc= 0.906494 cost_time= 0.309900s \n",
      "Epoch:   3547 loss= 0.756880 acc= 0.927991 cost_time= 0.314552s \n",
      "Epoch:   3548 loss= 0.364794 acc= 0.935276 cost_time= 0.307292s \n",
      "Epoch:   3549 loss= 0.445969 acc= 0.912113 cost_time= 0.309601s \n",
      "Epoch:   3550 loss= 0.327646 acc= 0.942777 cost_time= 0.309580s \n",
      "Epoch:   3551 loss= 0.436769 acc= 0.927830 cost_time= 0.312459s \n",
      "Epoch:   3552 loss= 0.732082 acc= 0.865052 cost_time= 0.305996s \n",
      "Epoch:   3553 loss= 0.237078 acc= 0.949261 cost_time= 0.317383s \n",
      "Epoch:   3554 loss= 0.496575 acc= 0.925379 cost_time= 0.311400s \n",
      "Epoch:   3555 loss= 0.240602 acc= 0.948600 cost_time= 0.336161s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3556 loss= 0.359705 acc= 0.932276 cost_time= 0.322630s \n",
      "Epoch:   3557 loss= 0.363378 acc= 0.921741 cost_time= 0.309780s \n",
      "Epoch:   3558 loss= 0.267378 acc= 0.937896 cost_time= 0.312238s \n",
      "Epoch:   3559 loss= 0.298386 acc= 0.939013 cost_time= 0.316359s \n",
      "Epoch:   3560 loss= 0.212272 acc= 0.954694 cost_time= 0.311175s \n",
      "Epoch:   3561 loss= 0.433935 acc= 0.910823 cost_time= 0.308505s \n",
      "Epoch:   3562 loss= 0.310164 acc= 0.953682 cost_time= 0.315104s \n",
      "Epoch:   3563 loss= 1.433589 acc= 0.903812 cost_time= 0.321978s \n",
      "Epoch:   3564 loss= 0.445368 acc= 0.912984 cost_time= 0.312419s \n",
      "Epoch:   3565 loss= 0.210210 acc= 0.962016 cost_time= 0.312850s \n",
      "Epoch:   3566 loss= 0.661812 acc= 0.882565 cost_time= 0.310524s \n",
      "Epoch:   3567 loss= 0.304087 acc= 0.929686 cost_time= 0.309401s \n",
      "Epoch:   3568 loss= 0.258653 acc= 0.950474 cost_time= 0.311463s \n",
      "Epoch:   3569 loss= 0.426093 acc= 0.921312 cost_time= 0.308917s \n",
      "Epoch:   3570 loss= 0.335599 acc= 0.924715 cost_time= 0.314518s \n",
      "Epoch:   3571 loss= 0.371154 acc= 0.910526 cost_time= 0.306144s \n",
      "Epoch:   3572 loss= 0.316903 acc= 0.929558 cost_time= 0.310407s \n",
      "Epoch:   3573 loss= 0.217841 acc= 0.964674 cost_time= 0.308386s \n",
      "Epoch:   3574 loss= 0.725290 acc= 0.879890 cost_time= 0.310869s \n",
      "Epoch:   3575 loss= 0.713134 acc= 0.874308 cost_time= 0.331601s \n",
      "Epoch:   3576 loss= 0.334402 acc= 0.923765 cost_time= 0.308641s \n",
      "Epoch:   3577 loss= 0.313251 acc= 0.920388 cost_time= 0.307020s \n",
      "Epoch:   3578 loss= 0.228161 acc= 0.938645 cost_time= 0.311345s \n",
      "Epoch:   3579 loss= 0.262595 acc= 0.928102 cost_time= 0.311477s \n",
      "Epoch:   3580 loss= 0.208250 acc= 0.942782 cost_time= 0.315738s \n",
      "Epoch:   3581 loss= 0.626038 acc= 0.919273 cost_time= 0.308285s \n",
      "Epoch:   3582 loss= 0.482834 acc= 0.925413 cost_time= 0.331535s \n",
      "Epoch:   3583 loss= 0.390987 acc= 0.913658 cost_time= 0.312258s \n",
      "Epoch:   3584 loss= 0.286530 acc= 0.940015 cost_time= 0.309039s \n",
      "Epoch:   3585 loss= 0.599691 acc= 0.888177 cost_time= 0.312225s \n",
      "Epoch:   3586 loss= 0.327956 acc= 0.938081 cost_time= 0.314813s \n",
      "Epoch:   3587 loss= 0.310168 acc= 0.950260 cost_time= 0.311840s \n",
      "Epoch:   3588 loss= 0.747608 acc= 0.903279 cost_time= 0.316620s \n",
      "Epoch:   3589 loss= 0.697066 acc= 0.904993 cost_time= 0.334510s \n",
      "Epoch:   3590 loss= 0.251596 acc= 0.961309 cost_time= 0.314385s \n",
      "Epoch:   3591 loss= 0.320287 acc= 0.932864 cost_time= 0.311082s \n",
      "Epoch:   3592 loss= 0.437622 acc= 0.907257 cost_time= 0.313624s \n",
      "Epoch:   3593 loss= 0.287103 acc= 0.949992 cost_time= 0.315409s \n",
      "Epoch:   3594 loss= 0.330513 acc= 0.936713 cost_time= 0.331267s \n",
      "Epoch:   3595 loss= 1.253743 acc= 0.866209 cost_time= 0.307815s \n",
      "Epoch:   3596 loss= 0.206716 acc= 0.964445 cost_time= 0.333300s \n",
      "Epoch:   3597 loss= 0.723350 acc= 0.858188 cost_time= 0.302377s \n",
      "Epoch:   3598 loss= 0.866229 acc= 0.883109 cost_time= 0.305801s \n",
      "Epoch:   3599 loss= 0.239816 acc= 0.945287 cost_time= 0.309892s \n",
      "Epoch:   3600 loss= 0.295201 acc= 0.945477 cost_time= 0.312753s \n",
      "Epoch:   3601 loss= 0.745823 acc= 0.895016 cost_time= 0.313405s \n",
      "Epoch:   3602 loss= 0.508381 acc= 0.883653 cost_time= 0.307421s \n",
      "Epoch:   3603 loss= 0.272905 acc= 0.945315 cost_time= 0.311996s \n",
      "Epoch:   3604 loss= 0.358966 acc= 0.934969 cost_time= 0.327466s \n",
      "Epoch:   3605 loss= 0.339370 acc= 0.938623 cost_time= 0.307742s \n",
      "Epoch:   3606 loss= 0.502712 acc= 0.927103 cost_time= 0.335282s \n",
      "Epoch:   3607 loss= 0.899014 acc= 0.836381 cost_time= 0.304376s \n",
      "Epoch:   3608 loss= 0.211925 acc= 0.957582 cost_time= 0.332758s \n",
      "Epoch:   3609 loss= 0.536231 acc= 0.921074 cost_time= 0.310002s \n",
      "Epoch:   3610 loss= 0.246258 acc= 0.940941 cost_time= 0.312047s \n",
      "Epoch:   3611 loss= 0.261940 acc= 0.951837 cost_time= 0.309565s \n",
      "Epoch:   3612 loss= 0.369801 acc= 0.943938 cost_time= 0.332257s \n",
      "Epoch:   3613 loss= 0.417316 acc= 0.907441 cost_time= 0.311858s \n",
      "Epoch:   3614 loss= 0.296281 acc= 0.940561 cost_time= 0.313386s \n",
      "Epoch:   3615 loss= 0.225974 acc= 0.950850 cost_time= 0.313523s \n",
      "Epoch:   3616 loss= 0.259445 acc= 0.954135 cost_time= 0.308904s \n",
      "Epoch:   3617 loss= 0.262025 acc= 0.944351 cost_time= 0.306274s \n",
      "Epoch:   3618 loss= 0.237389 acc= 0.950813 cost_time= 0.311100s \n",
      "Epoch:   3619 loss= 0.235219 acc= 0.948560 cost_time= 0.331974s \n",
      "Epoch:   3620 loss= 0.229519 acc= 0.943829 cost_time= 0.316803s \n",
      "Epoch:   3621 loss= 0.550587 acc= 0.868053 cost_time= 0.308423s \n",
      "Epoch:   3622 loss= 0.602337 acc= 0.878626 cost_time= 0.304743s \n",
      "Epoch:   3623 loss= 0.393766 acc= 0.923598 cost_time= 0.311410s \n",
      "Epoch:   3624 loss= 0.400997 acc= 0.906353 cost_time= 0.327108s \n",
      "Epoch:   3625 loss= 0.380107 acc= 0.915086 cost_time= 0.302724s \n",
      "Epoch:   3626 loss= 0.299316 acc= 0.924309 cost_time= 0.332933s \n",
      "Epoch:   3627 loss= 0.266517 acc= 0.956644 cost_time= 0.315254s \n",
      "Epoch:   3628 loss= 0.718107 acc= 0.918825 cost_time= 0.311619s \n",
      "Epoch:   3629 loss= 0.354460 acc= 0.920072 cost_time= 0.309046s \n",
      "Epoch:   3630 loss= 0.256515 acc= 0.945677 cost_time= 0.310431s \n",
      "Epoch:   3631 loss= 0.216944 acc= 0.959108 cost_time= 0.332772s \n",
      "Epoch:   3632 loss= 0.319007 acc= 0.931250 cost_time= 0.314853s \n",
      "Epoch:   3633 loss= 0.395201 acc= 0.918312 cost_time= 0.304777s \n",
      "Epoch:   3634 loss= 1.345578 acc= 0.906321 cost_time= 0.318992s \n",
      "Epoch:   3635 loss= 0.583382 acc= 0.922087 cost_time= 0.314950s \n",
      "Epoch:   3636 loss= 0.372571 acc= 0.902156 cost_time= 0.313932s \n",
      "Epoch:   3637 loss= 0.556503 acc= 0.914897 cost_time= 0.320489s \n",
      "Epoch:   3638 loss= 0.320693 acc= 0.927432 cost_time= 0.317684s \n",
      "Epoch:   3639 loss= 0.344137 acc= 0.944631 cost_time= 0.311137s \n",
      "Epoch:   3640 loss= 0.333671 acc= 0.938960 cost_time= 0.335964s \n",
      "Epoch:   3641 loss= 0.261211 acc= 0.942115 cost_time= 0.310684s \n",
      "Epoch:   3642 loss= 0.355568 acc= 0.934924 cost_time= 0.308514s \n",
      "Epoch:   3643 loss= 0.271303 acc= 0.943679 cost_time= 0.309494s \n",
      "Epoch:   3644 loss= 0.252244 acc= 0.947582 cost_time= 0.310881s \n",
      "Epoch:   3645 loss= 0.774148 acc= 0.903085 cost_time= 0.309418s \n",
      "Epoch:   3646 loss= 0.258021 acc= 0.940688 cost_time= 0.314322s \n",
      "Epoch:   3647 loss= 0.314657 acc= 0.943708 cost_time= 0.308078s \n",
      "Epoch:   3648 loss= 0.517318 acc= 0.904858 cost_time= 0.313911s \n",
      "Epoch:   3649 loss= 0.274646 acc= 0.952285 cost_time= 0.307843s \n",
      "Epoch:   3650 loss= 0.239975 acc= 0.956505 cost_time= 0.315270s \n",
      "Epoch:   3651 loss= 0.243076 acc= 0.955264 cost_time= 0.328828s \n",
      "Epoch:   3652 loss= 0.234337 acc= 0.949433 cost_time= 0.335061s \n",
      "Epoch:   3653 loss= 0.245257 acc= 0.952735 cost_time= 0.310630s \n",
      "Epoch:   3654 loss= 0.751316 acc= 0.884778 cost_time= 0.310726s \n",
      "Epoch:   3655 loss= 0.360047 acc= 0.909189 cost_time= 0.308086s \n",
      "Epoch:   3656 loss= 0.425583 acc= 0.900372 cost_time= 0.313735s \n",
      "Epoch:   3657 loss= 0.554514 acc= 0.912709 cost_time= 0.311592s \n",
      "Epoch:   3658 loss= 0.309651 acc= 0.938501 cost_time= 0.309877s \n",
      "Epoch:   3659 loss= 0.183418 acc= 0.948792 cost_time= 0.310034s \n",
      "Epoch:   3660 loss= 0.249157 acc= 0.948795 cost_time= 0.313895s \n",
      "Epoch:   3661 loss= 0.338442 acc= 0.941569 cost_time= 0.329477s \n",
      "Epoch:   3662 loss= 0.333860 acc= 0.928432 cost_time= 0.309184s \n",
      "Epoch:   3663 loss= 0.343062 acc= 0.932395 cost_time= 0.320495s \n",
      "Epoch:   3664 loss= 0.377897 acc= 0.916608 cost_time= 0.310162s \n",
      "Epoch:   3665 loss= 0.297009 acc= 0.935646 cost_time= 0.308881s \n",
      "Epoch:   3666 loss= 0.285238 acc= 0.938126 cost_time= 0.307168s \n",
      "Epoch:   3667 loss= 0.357716 acc= 0.931924 cost_time= 0.308436s \n",
      "Epoch:   3668 loss= 0.332663 acc= 0.940054 cost_time= 0.329753s \n",
      "Epoch:   3669 loss= 0.187894 acc= 0.958809 cost_time= 0.307466s \n",
      "Epoch:   3670 loss= 0.217979 acc= 0.958723 cost_time= 0.324306s \n",
      "Epoch:   3671 loss= 0.286352 acc= 0.951336 cost_time= 0.309742s \n",
      "Epoch:   3672 loss= 0.474474 acc= 0.927504 cost_time= 0.312357s \n",
      "Epoch:   3673 loss= 0.264979 acc= 0.945286 cost_time= 0.311414s \n",
      "Epoch:   3674 loss= 0.232453 acc= 0.948878 cost_time= 0.309126s \n",
      "Epoch:   3675 loss= 0.335078 acc= 0.937685 cost_time= 0.306564s \n",
      "Epoch:   3676 loss= 1.311764 acc= 0.768252 cost_time= 0.299687s \n",
      "Epoch:   3677 loss= 1.640468 acc= 0.829283 cost_time= 0.310935s \n",
      "Epoch:   3678 loss= 0.583060 acc= 0.904885 cost_time= 0.333746s \n",
      "Epoch:   3679 loss= 0.386524 acc= 0.913334 cost_time= 0.310360s \n",
      "Epoch:   3680 loss= 0.352095 acc= 0.933594 cost_time= 0.309128s \n",
      "Epoch:   3681 loss= 0.398785 acc= 0.922042 cost_time= 0.311420s \n",
      "Epoch:   3682 loss= 0.308502 acc= 0.925785 cost_time= 0.330834s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3683 loss= 0.201369 acc= 0.961586 cost_time= 0.313344s \n",
      "Epoch:   3684 loss= 0.254733 acc= 0.941482 cost_time= 0.318329s \n",
      "Epoch:   3685 loss= 0.233984 acc= 0.959518 cost_time= 0.315822s \n",
      "Epoch:   3686 loss= 0.543518 acc= 0.883708 cost_time= 0.309707s \n",
      "Epoch:   3687 loss= 1.212222 acc= 0.857699 cost_time= 0.307934s \n",
      "Epoch:   3688 loss= 0.361974 acc= 0.935575 cost_time= 0.310697s \n",
      "Epoch:   3689 loss= 0.258793 acc= 0.934213 cost_time= 0.333770s \n",
      "Epoch:   3690 loss= 0.267004 acc= 0.936227 cost_time= 0.312497s \n",
      "Epoch:   3691 loss= 0.264447 acc= 0.947659 cost_time= 0.312076s \n",
      "Epoch:   3692 loss= 0.180310 acc= 0.959725 cost_time= 0.316905s \n",
      "Epoch:   3693 loss= 0.223023 acc= 0.950712 cost_time= 0.316309s \n",
      "Epoch:   3694 loss= 0.289669 acc= 0.942520 cost_time= 0.310984s \n",
      "Epoch:   3695 loss= 0.286868 acc= 0.949558 cost_time= 0.307374s \n",
      "Epoch:   3696 loss= 0.280922 acc= 0.934260 cost_time= 0.331423s \n",
      "Epoch:   3697 loss= 0.334119 acc= 0.934114 cost_time= 0.310773s \n",
      "Epoch:   3698 loss= 0.557401 acc= 0.920085 cost_time= 0.313551s \n",
      "Epoch:   3699 loss= 0.834838 acc= 0.896533 cost_time= 0.312635s \n",
      "Epoch:   3700 loss= 0.266799 acc= 0.953865 cost_time= 0.312910s \n",
      "Epoch:   3701 loss= 0.347153 acc= 0.937382 cost_time= 0.309035s \n",
      "Epoch:   3702 loss= 0.222370 acc= 0.941958 cost_time= 0.322633s \n",
      "Epoch:   3703 loss= 0.426828 acc= 0.894907 cost_time= 0.307964s \n",
      "Epoch:   3704 loss= 0.276930 acc= 0.941893 cost_time= 0.323480s \n",
      "Epoch:   3705 loss= 0.274981 acc= 0.939821 cost_time= 0.317935s \n",
      "Epoch:   3706 loss= 1.063620 acc= 0.889036 cost_time= 0.309049s \n",
      "Epoch:   3707 loss= 0.293007 acc= 0.930595 cost_time= 0.317388s \n",
      "Epoch:   3708 loss= 0.930751 acc= 0.905362 cost_time= 0.305465s \n",
      "Epoch:   3709 loss= 0.796643 acc= 0.900659 cost_time= 0.308170s \n",
      "Epoch:   3710 loss= 0.532724 acc= 0.881397 cost_time= 0.329325s \n",
      "Epoch:   3711 loss= 0.242946 acc= 0.947729 cost_time= 0.315367s \n",
      "Epoch:   3712 loss= 0.283292 acc= 0.935082 cost_time= 0.314572s \n",
      "Epoch:   3713 loss= 0.380664 acc= 0.928669 cost_time= 0.312041s \n",
      "Epoch:   3714 loss= 0.307799 acc= 0.930929 cost_time= 0.318426s \n",
      "Epoch:   3715 loss= 0.400846 acc= 0.891983 cost_time= 0.307818s \n",
      "Epoch:   3716 loss= 0.276685 acc= 0.954129 cost_time= 0.313953s \n",
      "Epoch:   3717 loss= 0.681303 acc= 0.881480 cost_time= 0.320631s \n",
      "Epoch:   3718 loss= 0.264552 acc= 0.943321 cost_time= 0.312777s \n",
      "Epoch:   3719 loss= 0.602314 acc= 0.916570 cost_time= 0.309183s \n",
      "Epoch:   3720 loss= 0.458853 acc= 0.904399 cost_time= 0.311950s \n",
      "Epoch:   3721 loss= 0.205478 acc= 0.948707 cost_time= 0.313411s \n",
      "Epoch:   3722 loss= 0.291286 acc= 0.949203 cost_time= 0.313059s \n",
      "Epoch:   3723 loss= 0.230629 acc= 0.950197 cost_time= 0.311782s \n",
      "Epoch:   3724 loss= 0.972633 acc= 0.901337 cost_time= 0.337347s \n",
      "Epoch:   3725 loss= 0.617173 acc= 0.903932 cost_time= 0.316400s \n",
      "Epoch:   3726 loss= 0.245812 acc= 0.943174 cost_time= 0.312591s \n",
      "Epoch:   3727 loss= 0.346920 acc= 0.931414 cost_time= 0.308847s \n",
      "Epoch:   3728 loss= 0.600354 acc= 0.873649 cost_time= 0.309117s \n",
      "Epoch:   3729 loss= 0.450893 acc= 0.929712 cost_time= 0.315796s \n",
      "Epoch:   3730 loss= 0.241428 acc= 0.955330 cost_time= 0.314322s \n",
      "Epoch:   3731 loss= 0.307378 acc= 0.931649 cost_time= 0.308866s \n",
      "Epoch:   3732 loss= 0.285151 acc= 0.945770 cost_time= 0.309669s \n",
      "Epoch:   3733 loss= 1.930519 acc= 0.770071 cost_time= 0.314286s \n",
      "Epoch:   3734 loss= 0.319729 acc= 0.931197 cost_time= 0.312866s \n",
      "Epoch:   3735 loss= 0.419818 acc= 0.891357 cost_time= 0.307931s \n",
      "Epoch:   3736 loss= 0.242980 acc= 0.957692 cost_time= 0.314961s \n",
      "Epoch:   3737 loss= 0.292305 acc= 0.943810 cost_time= 0.309062s \n",
      "Epoch:   3738 loss= 0.271840 acc= 0.948072 cost_time= 0.328125s \n",
      "Epoch:   3739 loss= 1.139440 acc= 0.875224 cost_time= 0.315828s \n",
      "Epoch:   3740 loss= 0.284617 acc= 0.945628 cost_time= 0.313199s \n",
      "Epoch:   3741 loss= 0.268919 acc= 0.943218 cost_time= 0.313819s \n",
      "Epoch:   3742 loss= 0.329339 acc= 0.938298 cost_time= 0.306184s \n",
      "Epoch:   3743 loss= 0.380984 acc= 0.905349 cost_time= 0.307925s \n",
      "Epoch:   3744 loss= 0.288872 acc= 0.949112 cost_time= 0.312982s \n",
      "Epoch:   3745 loss= 0.223522 acc= 0.955945 cost_time= 0.317271s \n",
      "Epoch:   3746 loss= 0.312877 acc= 0.938884 cost_time= 0.317923s \n",
      "Epoch:   3747 loss= 0.427311 acc= 0.906284 cost_time= 0.309298s \n",
      "Epoch:   3748 loss= 0.201423 acc= 0.956312 cost_time= 0.311507s \n",
      "Epoch:   3749 loss= 0.231438 acc= 0.949605 cost_time= 0.313692s \n",
      "Epoch:   3750 loss= 0.449268 acc= 0.924276 cost_time= 0.313381s \n",
      "Epoch:   3751 loss= 0.798450 acc= 0.829138 cost_time= 0.299862s \n",
      "Epoch:   3752 loss= 0.352411 acc= 0.926940 cost_time= 0.329044s \n",
      "Epoch:   3753 loss= 1.055797 acc= 0.802619 cost_time= 0.307768s \n",
      "Epoch:   3754 loss= 0.240592 acc= 0.952642 cost_time= 0.316201s \n",
      "Epoch:   3755 loss= 0.214609 acc= 0.957116 cost_time= 0.312234s \n",
      "Epoch:   3756 loss= 0.833418 acc= 0.863431 cost_time= 0.317943s \n",
      "Epoch:   3757 loss= 0.281301 acc= 0.939235 cost_time= 0.318989s \n",
      "Epoch:   3758 loss= 0.244957 acc= 0.951918 cost_time= 0.309355s \n",
      "Epoch:   3759 loss= 0.395803 acc= 0.901522 cost_time= 0.312753s \n",
      "Epoch:   3760 loss= 0.357159 acc= 0.923085 cost_time= 0.316009s \n",
      "Epoch:   3761 loss= 0.849721 acc= 0.870489 cost_time= 0.315350s \n",
      "Epoch:   3762 loss= 0.218325 acc= 0.950589 cost_time= 0.313713s \n",
      "Epoch:   3763 loss= 0.340962 acc= 0.950183 cost_time= 0.311699s \n",
      "Epoch:   3764 loss= 0.265415 acc= 0.936180 cost_time= 0.314894s \n",
      "Epoch:   3765 loss= 0.714260 acc= 0.902479 cost_time= 0.312683s \n",
      "Epoch:   3766 loss= 1.510497 acc= 0.803114 cost_time= 0.328120s \n",
      "Epoch:   3767 loss= 0.388832 acc= 0.931939 cost_time= 0.319092s \n",
      "Epoch:   3768 loss= 0.464248 acc= 0.925068 cost_time= 0.315686s \n",
      "Epoch:   3769 loss= 1.010597 acc= 0.877287 cost_time= 0.305634s \n",
      "Epoch:   3770 loss= 0.258087 acc= 0.945677 cost_time= 0.309364s \n",
      "Epoch:   3771 loss= 0.386033 acc= 0.904035 cost_time= 0.307423s \n",
      "Epoch:   3772 loss= 0.865234 acc= 0.845421 cost_time= 0.306165s \n",
      "Epoch:   3773 loss= 0.354997 acc= 0.918852 cost_time= 0.312124s \n",
      "Epoch:   3774 loss= 1.116832 acc= 0.879507 cost_time= 0.310767s \n",
      "Epoch:   3775 loss= 0.200492 acc= 0.953789 cost_time= 0.318396s \n",
      "Epoch:   3776 loss= 0.441282 acc= 0.915652 cost_time= 0.312086s \n",
      "Epoch:   3777 loss= 0.683793 acc= 0.860316 cost_time= 0.306210s \n",
      "Epoch:   3778 loss= 0.248592 acc= 0.946470 cost_time= 0.321853s \n",
      "Epoch:   3779 loss= 0.245439 acc= 0.939588 cost_time= 0.311451s \n",
      "Epoch:   3780 loss= 0.213625 acc= 0.961860 cost_time= 0.332755s \n",
      "Epoch:   3781 loss= 0.436074 acc= 0.893965 cost_time= 0.312054s \n",
      "Epoch:   3782 loss= 0.219104 acc= 0.948102 cost_time= 0.322551s \n",
      "Epoch:   3783 loss= 0.315412 acc= 0.928725 cost_time= 0.313452s \n",
      "Epoch:   3784 loss= 0.212689 acc= 0.957279 cost_time= 0.308724s \n",
      "Epoch:   3785 loss= 0.246990 acc= 0.954695 cost_time= 0.312612s \n",
      "Epoch:   3786 loss= 0.215695 acc= 0.957505 cost_time= 0.313143s \n",
      "Epoch:   3787 loss= 0.536540 acc= 0.911497 cost_time= 0.330506s \n",
      "Epoch:   3788 loss= 0.322267 acc= 0.922629 cost_time= 0.309512s \n",
      "Epoch:   3789 loss= 0.426590 acc= 0.900452 cost_time= 0.315801s \n",
      "Epoch:   3790 loss= 0.298377 acc= 0.928482 cost_time= 0.312777s \n",
      "Epoch:   3791 loss= 1.179252 acc= 0.891604 cost_time= 0.316525s \n",
      "Epoch:   3792 loss= 1.072722 acc= 0.895718 cost_time= 0.314440s \n",
      "Epoch:   3793 loss= 0.309597 acc= 0.926429 cost_time= 0.310528s \n",
      "Epoch:   3794 loss= 0.269243 acc= 0.948611 cost_time= 0.333056s \n",
      "Epoch:   3795 loss= 0.257491 acc= 0.943907 cost_time= 0.306168s \n",
      "Epoch:   3796 loss= 0.437923 acc= 0.915139 cost_time= 0.308533s \n",
      "Epoch:   3797 loss= 0.281802 acc= 0.944737 cost_time= 0.310203s \n",
      "Epoch:   3798 loss= 0.593736 acc= 0.919485 cost_time= 0.310839s \n",
      "Epoch:   3799 loss= 0.479403 acc= 0.904071 cost_time= 0.325736s \n",
      "Epoch:   3800 loss= 0.227089 acc= 0.956534 cost_time= 0.312957s \n",
      "Epoch:   3801 loss= 0.230615 acc= 0.946915 cost_time= 0.310610s \n",
      "Epoch:   3802 loss= 0.221330 acc= 0.954842 cost_time= 0.313330s \n",
      "Epoch:   3803 loss= 0.296169 acc= 0.930659 cost_time= 0.311163s \n",
      "Epoch:   3804 loss= 0.490120 acc= 0.930502 cost_time= 0.313268s \n",
      "Epoch:   3805 loss= 0.296455 acc= 0.927188 cost_time= 0.305002s \n",
      "Epoch:   3806 loss= 0.257421 acc= 0.946612 cost_time= 0.314354s \n",
      "Epoch:   3807 loss= 0.431309 acc= 0.905092 cost_time= 0.309118s \n",
      "Epoch:   3808 loss= 0.222625 acc= 0.949575 cost_time= 0.330492s \n",
      "Epoch:   3809 loss= 0.450487 acc= 0.897717 cost_time= 0.309170s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3810 loss= 1.026144 acc= 0.901153 cost_time= 0.317412s \n",
      "Epoch:   3811 loss= 0.267114 acc= 0.947103 cost_time= 0.306931s \n",
      "Epoch:   3812 loss= 0.343927 acc= 0.935306 cost_time= 0.306518s \n",
      "Epoch:   3813 loss= 0.275473 acc= 0.946824 cost_time= 0.308976s \n",
      "Epoch:   3814 loss= 0.285489 acc= 0.945887 cost_time= 0.316341s \n",
      "Epoch:   3815 loss= 0.419084 acc= 0.933151 cost_time= 0.334745s \n",
      "Epoch:   3816 loss= 0.267276 acc= 0.951493 cost_time= 0.312137s \n",
      "Epoch:   3817 loss= 0.202019 acc= 0.947646 cost_time= 0.313476s \n",
      "Epoch:   3818 loss= 0.297062 acc= 0.927469 cost_time= 0.304836s \n",
      "Epoch:   3819 loss= 0.244455 acc= 0.946957 cost_time= 0.310719s \n",
      "Epoch:   3820 loss= 0.312673 acc= 0.949060 cost_time= 0.309113s \n",
      "Epoch:   3821 loss= 0.321172 acc= 0.934607 cost_time= 0.314873s \n",
      "Epoch:   3822 loss= 0.390515 acc= 0.915985 cost_time= 0.331971s \n",
      "Epoch:   3823 loss= 0.699822 acc= 0.908516 cost_time= 0.313628s \n",
      "Epoch:   3824 loss= 0.259826 acc= 0.956093 cost_time= 0.310803s \n",
      "Epoch:   3825 loss= 0.241632 acc= 0.952432 cost_time= 0.311392s \n",
      "Epoch:   3826 loss= 0.518892 acc= 0.917857 cost_time= 0.314064s \n",
      "Epoch:   3827 loss= 0.302006 acc= 0.945858 cost_time= 0.309796s \n",
      "Epoch:   3828 loss= 1.187507 acc= 0.910284 cost_time= 0.313573s \n",
      "Epoch:   3829 loss= 0.392963 acc= 0.928038 cost_time= 0.312122s \n",
      "Epoch:   3830 loss= 0.247534 acc= 0.946231 cost_time= 0.329309s \n",
      "Epoch:   3831 loss= 0.704714 acc= 0.911379 cost_time= 0.309011s \n",
      "Epoch:   3832 loss= 0.203433 acc= 0.960526 cost_time= 0.313584s \n",
      "Epoch:   3833 loss= 0.235244 acc= 0.948748 cost_time= 0.310706s \n",
      "Epoch:   3834 loss= 0.223048 acc= 0.943770 cost_time= 0.336422s \n",
      "Epoch:   3835 loss= 0.711220 acc= 0.855303 cost_time= 0.309833s \n",
      "Epoch:   3836 loss= 0.624091 acc= 0.896807 cost_time= 0.333304s \n",
      "Epoch:   3837 loss= 0.320572 acc= 0.924450 cost_time= 0.309348s \n",
      "Epoch:   3838 loss= 0.343598 acc= 0.932741 cost_time= 0.314973s \n",
      "Epoch:   3839 loss= 0.495862 acc= 0.915992 cost_time= 0.310954s \n",
      "Epoch:   3840 loss= 0.226588 acc= 0.946334 cost_time= 0.311304s \n",
      "Epoch:   3841 loss= 1.126567 acc= 0.785826 cost_time= 0.303874s \n",
      "Epoch:   3842 loss= 0.460854 acc= 0.927285 cost_time= 0.312773s \n",
      "Epoch:   3843 loss= 0.472333 acc= 0.897718 cost_time= 0.326750s \n",
      "Epoch:   3844 loss= 0.647976 acc= 0.883243 cost_time= 0.311801s \n",
      "Epoch:   3845 loss= 0.940201 acc= 0.895362 cost_time= 0.315533s \n",
      "Epoch:   3846 loss= 1.447363 acc= 0.805602 cost_time= 0.306527s \n",
      "Epoch:   3847 loss= 0.217229 acc= 0.956211 cost_time= 0.313422s \n",
      "Epoch:   3848 loss= 0.196058 acc= 0.958056 cost_time= 0.328168s \n",
      "Epoch:   3849 loss= 0.284011 acc= 0.939147 cost_time= 0.309605s \n",
      "Epoch:   3850 loss= 0.911054 acc= 0.895970 cost_time= 0.329314s \n",
      "Epoch:   3851 loss= 0.338365 acc= 0.923925 cost_time= 0.309727s \n",
      "Epoch:   3852 loss= 0.236497 acc= 0.949825 cost_time= 0.309888s \n",
      "Epoch:   3853 loss= 0.811147 acc= 0.915219 cost_time= 0.309878s \n",
      "Epoch:   3854 loss= 0.487094 acc= 0.898075 cost_time= 0.309539s \n",
      "Epoch:   3855 loss= 0.376741 acc= 0.911858 cost_time= 0.307020s \n",
      "Epoch:   3856 loss= 0.374326 acc= 0.918057 cost_time= 0.311771s \n",
      "Epoch:   3857 loss= 0.227230 acc= 0.945102 cost_time= 0.318885s \n",
      "Epoch:   3858 loss= 0.302628 acc= 0.928673 cost_time= 0.303700s \n",
      "Epoch:   3859 loss= 0.249738 acc= 0.948956 cost_time= 0.308043s \n",
      "Epoch:   3860 loss= 0.399680 acc= 0.904932 cost_time= 0.331244s \n",
      "Epoch:   3861 loss= 0.252268 acc= 0.942439 cost_time= 0.313773s \n",
      "Epoch:   3862 loss= 0.314066 acc= 0.944658 cost_time= 0.318621s \n",
      "Epoch:   3863 loss= 0.500479 acc= 0.908481 cost_time= 0.313589s \n",
      "Epoch:   3864 loss= 1.455067 acc= 0.854761 cost_time= 0.306370s \n",
      "Epoch:   3865 loss= 0.373905 acc= 0.904715 cost_time= 0.331808s \n",
      "Epoch:   3866 loss= 0.401230 acc= 0.918631 cost_time= 0.315906s \n",
      "Epoch:   3867 loss= 0.492699 acc= 0.927790 cost_time= 0.311679s \n",
      "Epoch:   3868 loss= 0.251406 acc= 0.947191 cost_time= 0.315814s \n",
      "Epoch:   3869 loss= 0.204039 acc= 0.957691 cost_time= 0.313469s \n",
      "Epoch:   3870 loss= 0.275488 acc= 0.947261 cost_time= 0.332598s \n",
      "Epoch:   3871 loss= 0.323818 acc= 0.928222 cost_time= 0.326374s \n",
      "Epoch:   3872 loss= 0.284060 acc= 0.946896 cost_time= 0.312167s \n",
      "Epoch:   3873 loss= 0.429276 acc= 0.919854 cost_time= 0.312737s \n",
      "Epoch:   3874 loss= 0.219073 acc= 0.949699 cost_time= 0.312809s \n",
      "Epoch:   3875 loss= 0.201911 acc= 0.958524 cost_time= 0.319054s \n",
      "Epoch:   3876 loss= 0.304318 acc= 0.938774 cost_time= 0.309836s \n",
      "Epoch:   3877 loss= 0.275637 acc= 0.946078 cost_time= 0.311092s \n",
      "Epoch:   3878 loss= 0.202749 acc= 0.957942 cost_time= 0.335801s \n",
      "Epoch:   3879 loss= 0.211991 acc= 0.948256 cost_time= 0.313371s \n",
      "Epoch:   3880 loss= 0.224608 acc= 0.957653 cost_time= 0.306024s \n",
      "Epoch:   3881 loss= 0.507422 acc= 0.918670 cost_time= 0.313265s \n",
      "Epoch:   3882 loss= 0.305473 acc= 0.915546 cost_time= 0.308598s \n",
      "Epoch:   3883 loss= 0.698864 acc= 0.840843 cost_time= 0.323120s \n",
      "Epoch:   3884 loss= 0.587646 acc= 0.865764 cost_time= 0.303015s \n",
      "Epoch:   3885 loss= 0.425707 acc= 0.923466 cost_time= 0.328815s \n",
      "Epoch:   3886 loss= 0.326216 acc= 0.926083 cost_time= 0.306777s \n",
      "Epoch:   3887 loss= 0.678035 acc= 0.895610 cost_time= 0.309072s \n",
      "Epoch:   3888 loss= 0.307849 acc= 0.938068 cost_time= 0.311909s \n",
      "Epoch:   3889 loss= 0.251337 acc= 0.950361 cost_time= 0.310215s \n",
      "Epoch:   3890 loss= 0.323824 acc= 0.939379 cost_time= 0.328193s \n",
      "Epoch:   3891 loss= 0.266852 acc= 0.960026 cost_time= 0.308529s \n",
      "Epoch:   3892 loss= 0.236553 acc= 0.966880 cost_time= 0.307181s \n",
      "Epoch:   3893 loss= 0.402374 acc= 0.919403 cost_time= 0.311439s \n",
      "Epoch:   3894 loss= 0.199109 acc= 0.953775 cost_time= 0.311890s \n",
      "Epoch:   3895 loss= 0.217859 acc= 0.956350 cost_time= 0.311868s \n",
      "Epoch:   3896 loss= 0.548198 acc= 0.884439 cost_time= 0.308230s \n",
      "Epoch:   3897 loss= 0.295186 acc= 0.946387 cost_time= 0.304112s \n",
      "Epoch:   3898 loss= 0.247655 acc= 0.952212 cost_time= 0.331616s \n",
      "Epoch:   3899 loss= 0.348693 acc= 0.935306 cost_time= 0.333840s \n",
      "Epoch:   3900 loss= 0.329016 acc= 0.939672 cost_time= 0.312437s \n",
      "Epoch:   3901 loss= 0.329381 acc= 0.934711 cost_time= 0.313634s \n",
      "Epoch:   3902 loss= 0.332484 acc= 0.911562 cost_time= 0.313797s \n",
      "Epoch:   3903 loss= 0.256818 acc= 0.947970 cost_time= 0.307743s \n",
      "Epoch:   3904 loss= 1.060925 acc= 0.913477 cost_time= 0.312238s \n",
      "Epoch:   3905 loss= 0.244803 acc= 0.949092 cost_time= 0.312554s \n",
      "Epoch:   3906 loss= 0.435628 acc= 0.901654 cost_time= 0.323428s \n",
      "Epoch:   3907 loss= 0.309862 acc= 0.929729 cost_time= 0.308181s \n",
      "Epoch:   3908 loss= 0.363230 acc= 0.925079 cost_time= 0.309011s \n",
      "Epoch:   3909 loss= 0.285547 acc= 0.942744 cost_time= 0.308861s \n",
      "Epoch:   3910 loss= 1.146956 acc= 0.869450 cost_time= 0.312482s \n",
      "Epoch:   3911 loss= 0.356262 acc= 0.935443 cost_time= 0.309456s \n",
      "Epoch:   3912 loss= 0.368685 acc= 0.931426 cost_time= 0.307961s \n",
      "Epoch:   3913 loss= 0.786934 acc= 0.894906 cost_time= 0.314333s \n",
      "Epoch:   3914 loss= 0.286210 acc= 0.938637 cost_time= 0.313724s \n",
      "Epoch:   3915 loss= 0.627735 acc= 0.870683 cost_time= 0.305890s \n",
      "Epoch:   3916 loss= 0.309381 acc= 0.945994 cost_time= 0.325985s \n",
      "Epoch:   3917 loss= 0.248799 acc= 0.948876 cost_time= 0.313596s \n",
      "Epoch:   3918 loss= 0.775419 acc= 0.881881 cost_time= 0.330264s \n",
      "Epoch:   3919 loss= 0.238821 acc= 0.945999 cost_time= 0.310251s \n",
      "Epoch:   3920 loss= 0.297821 acc= 0.944018 cost_time= 0.313797s \n",
      "Epoch:   3921 loss= 0.264821 acc= 0.945580 cost_time= 0.309820s \n",
      "Epoch:   3922 loss= 0.259812 acc= 0.942868 cost_time= 0.314453s \n",
      "Epoch:   3923 loss= 0.259676 acc= 0.946656 cost_time= 0.306933s \n",
      "Epoch:   3924 loss= 0.375303 acc= 0.914854 cost_time= 0.317209s \n",
      "Epoch:   3925 loss= 0.304119 acc= 0.940460 cost_time= 0.311092s \n",
      "Epoch:   3926 loss= 0.252765 acc= 0.939647 cost_time= 0.308627s \n",
      "Epoch:   3927 loss= 0.352666 acc= 0.934899 cost_time= 0.312800s \n",
      "Epoch:   3928 loss= 0.729342 acc= 0.840478 cost_time= 0.328995s \n",
      "Epoch:   3929 loss= 0.188996 acc= 0.957080 cost_time= 0.317328s \n",
      "Epoch:   3930 loss= 0.267857 acc= 0.947035 cost_time= 0.305760s \n",
      "Epoch:   3931 loss= 0.372513 acc= 0.905074 cost_time= 0.308705s \n",
      "Epoch:   3932 loss= 0.231781 acc= 0.930691 cost_time= 0.309903s \n",
      "Epoch:   3933 loss= 0.329638 acc= 0.937316 cost_time= 0.312416s \n",
      "Epoch:   3934 loss= 0.221352 acc= 0.936889 cost_time= 0.330353s \n",
      "Epoch:   3935 loss= 1.500891 acc= 0.841834 cost_time= 0.314412s \n",
      "Epoch:   3936 loss= 0.249445 acc= 0.960130 cost_time= 0.307280s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   3937 loss= 0.255347 acc= 0.948136 cost_time= 0.308225s \n",
      "Epoch:   3938 loss= 0.255969 acc= 0.934808 cost_time= 0.313166s \n",
      "Epoch:   3939 loss= 0.206701 acc= 0.950607 cost_time= 0.319728s \n",
      "Epoch:   3940 loss= 0.340809 acc= 0.928974 cost_time= 0.307583s \n",
      "Epoch:   3941 loss= 0.359607 acc= 0.930617 cost_time= 0.329846s \n",
      "Epoch:   3942 loss= 0.314510 acc= 0.942315 cost_time= 0.310567s \n",
      "Epoch:   3943 loss= 0.285698 acc= 0.943678 cost_time= 0.325755s \n",
      "Epoch:   3944 loss= 0.241179 acc= 0.952983 cost_time= 0.317750s \n",
      "Epoch:   3945 loss= 0.290922 acc= 0.944508 cost_time= 0.310370s \n",
      "Epoch:   3946 loss= 0.274710 acc= 0.955213 cost_time= 0.311118s \n",
      "Epoch:   3947 loss= 0.441218 acc= 0.929704 cost_time= 0.313735s \n",
      "Epoch:   3948 loss= 0.226257 acc= 0.958994 cost_time= 0.311854s \n",
      "Epoch:   3949 loss= 0.377223 acc= 0.901272 cost_time= 0.315880s \n",
      "Epoch:   3950 loss= 0.294925 acc= 0.942169 cost_time= 0.310924s \n",
      "Epoch:   3951 loss= 0.238914 acc= 0.937691 cost_time= 0.315009s \n",
      "Epoch:   3952 loss= 0.835717 acc= 0.873337 cost_time= 0.310181s \n",
      "Epoch:   3953 loss= 0.256393 acc= 0.942889 cost_time= 0.313868s \n",
      "Epoch:   3954 loss= 0.275094 acc= 0.938406 cost_time= 0.309345s \n",
      "Epoch:   3955 loss= 0.965538 acc= 0.899043 cost_time= 0.330975s \n",
      "Epoch:   3956 loss= 0.288414 acc= 0.945997 cost_time= 0.311980s \n",
      "Epoch:   3957 loss= 0.272763 acc= 0.942550 cost_time= 0.313135s \n",
      "Epoch:   3958 loss= 0.399104 acc= 0.918798 cost_time= 0.308497s \n",
      "Epoch:   3959 loss= 0.351515 acc= 0.919764 cost_time= 0.303471s \n",
      "Epoch:   3960 loss= 0.242604 acc= 0.951156 cost_time= 0.312912s \n",
      "Epoch:   3961 loss= 0.250744 acc= 0.947494 cost_time= 0.311192s \n",
      "Epoch:   3962 loss= 0.321301 acc= 0.936984 cost_time= 0.328827s \n",
      "Epoch:   3963 loss= 0.271314 acc= 0.949142 cost_time= 0.319762s \n",
      "Epoch:   3964 loss= 0.433556 acc= 0.898953 cost_time= 0.307441s \n",
      "Epoch:   3965 loss= 0.268249 acc= 0.934908 cost_time= 0.317140s \n",
      "Epoch:   3966 loss= 0.280583 acc= 0.931537 cost_time= 0.313439s \n",
      "Epoch:   3967 loss= 0.537296 acc= 0.912349 cost_time= 0.327644s \n",
      "Epoch:   3968 loss= 0.590358 acc= 0.878373 cost_time= 0.307368s \n",
      "Epoch:   3969 loss= 0.312492 acc= 0.941541 cost_time= 0.329262s \n",
      "Epoch:   3970 loss= 0.365037 acc= 0.919821 cost_time= 0.311105s \n",
      "Epoch:   3971 loss= 0.671667 acc= 0.891651 cost_time= 0.313514s \n",
      "Epoch:   3972 loss= 0.279815 acc= 0.949359 cost_time= 0.309803s \n",
      "Epoch:   3973 loss= 0.292795 acc= 0.933877 cost_time= 0.313497s \n",
      "Epoch:   3974 loss= 0.561326 acc= 0.860358 cost_time= 0.311016s \n",
      "Epoch:   3975 loss= 0.453544 acc= 0.922825 cost_time= 0.309452s \n",
      "Epoch:   3976 loss= 0.349814 acc= 0.937764 cost_time= 0.329778s \n",
      "Epoch:   3977 loss= 0.257231 acc= 0.956577 cost_time= 0.314665s \n",
      "Epoch:   3978 loss= 0.266834 acc= 0.929859 cost_time= 0.314570s \n",
      "Epoch:   3979 loss= 0.263159 acc= 0.940466 cost_time= 0.309381s \n",
      "Epoch:   3980 loss= 0.216372 acc= 0.949111 cost_time= 0.315010s \n",
      "Epoch:   3981 loss= 0.275142 acc= 0.939022 cost_time= 0.312383s \n",
      "Epoch:   3982 loss= 0.958235 acc= 0.878890 cost_time= 0.310642s \n",
      "Epoch:   3983 loss= 0.394604 acc= 0.935562 cost_time= 0.318227s \n",
      "Epoch:   3984 loss= 0.227875 acc= 0.953395 cost_time= 0.313623s \n",
      "Epoch:   3985 loss= 0.277570 acc= 0.949139 cost_time= 0.310222s \n",
      "Epoch:   3986 loss= 0.235461 acc= 0.942910 cost_time= 0.333807s \n",
      "Epoch:   3987 loss= 0.212680 acc= 0.957023 cost_time= 0.311123s \n",
      "Epoch:   3988 loss= 0.277485 acc= 0.943014 cost_time= 0.334720s \n",
      "Epoch:   3989 loss= 0.369934 acc= 0.934276 cost_time= 0.313331s \n",
      "Epoch:   3990 loss= 0.254165 acc= 0.939074 cost_time= 0.311881s \n",
      "Epoch:   3991 loss= 0.388491 acc= 0.921718 cost_time= 0.305921s \n",
      "Epoch:   3992 loss= 0.237710 acc= 0.948547 cost_time= 0.315464s \n",
      "Epoch:   3993 loss= 0.264789 acc= 0.947496 cost_time= 0.314541s \n",
      "Epoch:   3994 loss= 0.342863 acc= 0.939972 cost_time= 0.307283s \n",
      "Epoch:   3995 loss= 0.218320 acc= 0.949286 cost_time= 0.328707s \n",
      "Epoch:   3996 loss= 0.302550 acc= 0.955522 cost_time= 0.311087s \n",
      "Epoch:   3997 loss= 0.362191 acc= 0.902820 cost_time= 0.307657s \n",
      "Epoch:   3998 loss= 0.268169 acc= 0.947806 cost_time= 0.310657s \n",
      "Epoch:   3999 loss= 0.236879 acc= 0.953700 cost_time= 0.326046s \n",
      "Epoch:   4000 loss= 0.294767 acc= 0.929726 cost_time= 0.308243s \n",
      "Epoch:   4001 loss= 0.336101 acc= 0.910653 cost_time= 0.309208s \n",
      "Epoch:   4002 loss= 0.250004 acc= 0.946475 cost_time= 0.308448s \n",
      "Epoch:   4003 loss= 0.532877 acc= 0.883900 cost_time= 0.305911s \n",
      "Epoch:   4004 loss= 0.297706 acc= 0.930349 cost_time= 0.311631s \n",
      "Epoch:   4005 loss= 0.686955 acc= 0.833233 cost_time= 0.301454s \n",
      "Epoch:   4006 loss= 0.239534 acc= 0.949620 cost_time= 0.309508s \n",
      "Epoch:   4007 loss= 0.436343 acc= 0.902190 cost_time= 0.309357s \n",
      "Epoch:   4008 loss= 0.195638 acc= 0.954234 cost_time= 0.317354s \n",
      "Epoch:   4009 loss= 0.337527 acc= 0.926484 cost_time= 0.307652s \n",
      "Epoch:   4010 loss= 0.328088 acc= 0.933874 cost_time= 0.310201s \n",
      "Epoch:   4011 loss= 0.270022 acc= 0.943944 cost_time= 0.308779s \n",
      "Epoch:   4012 loss= 0.398650 acc= 0.926486 cost_time= 0.309220s \n",
      "Epoch:   4013 loss= 0.266197 acc= 0.951887 cost_time= 0.312352s \n",
      "Epoch:   4014 loss= 0.261333 acc= 0.954477 cost_time= 0.332600s \n",
      "Epoch:   4015 loss= 0.868248 acc= 0.915189 cost_time= 0.312653s \n",
      "Epoch:   4016 loss= 0.637950 acc= 0.909422 cost_time= 0.329293s \n",
      "Epoch:   4017 loss= 0.267754 acc= 0.924480 cost_time= 0.305664s \n",
      "Epoch:   4018 loss= 0.237648 acc= 0.961319 cost_time= 0.307725s \n",
      "Epoch:   4019 loss= 0.465217 acc= 0.907714 cost_time= 0.309618s \n",
      "Epoch:   4020 loss= 0.460134 acc= 0.910759 cost_time= 0.312525s \n",
      "Epoch:   4021 loss= 0.504965 acc= 0.901934 cost_time= 0.310564s \n",
      "Epoch:   4022 loss= 0.267121 acc= 0.939029 cost_time= 0.306091s \n",
      "Epoch:   4023 loss= 0.465296 acc= 0.935736 cost_time= 0.330488s \n",
      "Epoch:   4024 loss= 0.213217 acc= 0.948658 cost_time= 0.315714s \n",
      "Epoch:   4025 loss= 0.244975 acc= 0.960363 cost_time= 0.311240s \n",
      "Epoch:   4026 loss= 0.414574 acc= 0.922876 cost_time= 0.308226s \n",
      "Epoch:   4027 loss= 0.238037 acc= 0.952512 cost_time= 0.316062s \n",
      "Epoch:   4028 loss= 0.187759 acc= 0.960077 cost_time= 0.312857s \n",
      "Epoch:   4029 loss= 0.268494 acc= 0.951405 cost_time= 0.309509s \n",
      "Epoch:   4030 loss= 0.267402 acc= 0.943694 cost_time= 0.307616s \n",
      "Epoch:   4031 loss= 0.329188 acc= 0.932476 cost_time= 0.309561s \n",
      "Epoch:   4032 loss= 0.240553 acc= 0.958459 cost_time= 0.334176s \n",
      "Epoch:   4033 loss= 0.278888 acc= 0.954571 cost_time= 0.312667s \n",
      "Epoch:   4034 loss= 0.198437 acc= 0.950992 cost_time= 0.317234s \n",
      "Epoch:   4035 loss= 0.308365 acc= 0.942505 cost_time= 0.312081s \n",
      "Epoch:   4036 loss= 0.191747 acc= 0.961704 cost_time= 0.312850s \n",
      "Epoch:   4037 loss= 0.481914 acc= 0.890860 cost_time= 0.305059s \n",
      "Epoch:   4038 loss= 0.240201 acc= 0.951862 cost_time= 0.312363s \n",
      "Epoch:   4039 loss= 0.243857 acc= 0.956144 cost_time= 0.332600s \n",
      "Epoch:   4040 loss= 0.302671 acc= 0.936536 cost_time= 0.311309s \n",
      "Epoch:   4041 loss= 1.014276 acc= 0.835557 cost_time= 0.320081s \n",
      "Epoch:   4042 loss= 0.283141 acc= 0.946272 cost_time= 0.308541s \n",
      "Epoch:   4043 loss= 0.794579 acc= 0.862081 cost_time= 0.312376s \n",
      "Epoch:   4044 loss= 1.038293 acc= 0.879907 cost_time= 0.306927s \n",
      "Epoch:   4045 loss= 0.264925 acc= 0.936532 cost_time= 0.312260s \n",
      "Epoch:   4046 loss= 0.309416 acc= 0.929800 cost_time= 0.308703s \n",
      "Epoch:   4047 loss= 0.265542 acc= 0.939598 cost_time= 0.308769s \n",
      "Epoch:   4048 loss= 0.899749 acc= 0.923682 cost_time= 0.316811s \n",
      "Epoch:   4049 loss= 0.298823 acc= 0.944486 cost_time= 0.311509s \n",
      "Epoch:   4050 loss= 0.625986 acc= 0.911230 cost_time= 0.313670s \n",
      "Epoch:   4051 loss= 0.234826 acc= 0.948187 cost_time= 0.314054s \n",
      "Epoch:   4052 loss= 0.394242 acc= 0.932796 cost_time= 0.310964s \n",
      "Epoch:   4053 loss= 0.192427 acc= 0.955447 cost_time= 0.309877s \n",
      "Epoch:   4054 loss= 0.246590 acc= 0.943230 cost_time= 0.310722s \n",
      "Epoch:   4055 loss= 0.259168 acc= 0.947572 cost_time= 0.310671s \n",
      "Epoch:   4056 loss= 0.216388 acc= 0.948942 cost_time= 0.329351s \n",
      "Epoch:   4057 loss= 0.421335 acc= 0.898607 cost_time= 0.304671s \n",
      "Epoch:   4058 loss= 0.309045 acc= 0.938683 cost_time= 0.332888s \n",
      "Epoch:   4059 loss= 0.258849 acc= 0.946481 cost_time= 0.310057s \n",
      "Epoch:   4060 loss= 0.214733 acc= 0.952278 cost_time= 0.311229s \n",
      "Epoch:   4061 loss= 0.626383 acc= 0.888841 cost_time= 0.307343s \n",
      "Epoch:   4062 loss= 0.293901 acc= 0.943879 cost_time= 0.321652s \n",
      "Epoch:   4063 loss= 0.228380 acc= 0.949241 cost_time= 0.310505s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4064 loss= 0.318642 acc= 0.923349 cost_time= 0.310332s \n",
      "Epoch:   4065 loss= 0.227604 acc= 0.951209 cost_time= 0.311746s \n",
      "Epoch:   4066 loss= 1.087332 acc= 0.881169 cost_time= 0.310578s \n",
      "Epoch:   4067 loss= 0.396404 acc= 0.926246 cost_time= 0.311627s \n",
      "Epoch:   4068 loss= 0.272593 acc= 0.957202 cost_time= 0.312373s \n",
      "Epoch:   4069 loss= 0.339598 acc= 0.937265 cost_time= 0.310733s \n",
      "Epoch:   4070 loss= 0.240380 acc= 0.946196 cost_time= 0.310970s \n",
      "Epoch:   4071 loss= 0.318639 acc= 0.934871 cost_time= 0.312767s \n",
      "Epoch:   4072 loss= 0.279136 acc= 0.939230 cost_time= 0.307664s \n",
      "Epoch:   4073 loss= 0.387310 acc= 0.923169 cost_time= 0.304967s \n",
      "Epoch:   4074 loss= 0.229391 acc= 0.950432 cost_time= 0.331323s \n",
      "Epoch:   4075 loss= 0.580092 acc= 0.920780 cost_time= 0.309141s \n",
      "Epoch:   4076 loss= 0.452853 acc= 0.882826 cost_time= 0.304449s \n",
      "Epoch:   4077 loss= 0.281602 acc= 0.941073 cost_time= 0.311778s \n",
      "Epoch:   4078 loss= 1.143410 acc= 0.840740 cost_time= 0.312172s \n",
      "Epoch:   4079 loss= 0.288926 acc= 0.937481 cost_time= 0.329690s \n",
      "Epoch:   4080 loss= 0.269897 acc= 0.957366 cost_time= 0.310326s \n",
      "Epoch:   4081 loss= 0.239727 acc= 0.947629 cost_time= 0.311503s \n",
      "Epoch:   4082 loss= 0.368989 acc= 0.931207 cost_time= 0.307443s \n",
      "Epoch:   4083 loss= 0.235067 acc= 0.959923 cost_time= 0.321103s \n",
      "Epoch:   4084 loss= 0.198565 acc= 0.958231 cost_time= 0.315016s \n",
      "Epoch:   4085 loss= 0.293408 acc= 0.941299 cost_time= 0.316262s \n",
      "Epoch:   4086 loss= 0.251901 acc= 0.939477 cost_time= 0.309671s \n",
      "Epoch:   4087 loss= 0.263936 acc= 0.941981 cost_time= 0.309222s \n",
      "Epoch:   4088 loss= 0.270525 acc= 0.941415 cost_time= 0.329966s \n",
      "Epoch:   4089 loss= 0.338857 acc= 0.933873 cost_time= 0.309469s \n",
      "Epoch:   4090 loss= 0.263183 acc= 0.938765 cost_time= 0.307243s \n",
      "Epoch:   4091 loss= 0.534620 acc= 0.871197 cost_time= 0.308158s \n",
      "Epoch:   4092 loss= 0.779328 acc= 0.881902 cost_time= 0.307906s \n",
      "Epoch:   4093 loss= 0.236381 acc= 0.954174 cost_time= 0.309340s \n",
      "Epoch:   4094 loss= 0.311302 acc= 0.937751 cost_time= 0.306637s \n",
      "Epoch:   4095 loss= 0.222156 acc= 0.951754 cost_time= 0.315366s \n",
      "Epoch:   4096 loss= 0.657992 acc= 0.868575 cost_time= 0.308428s \n",
      "Epoch:   4097 loss= 0.316022 acc= 0.935256 cost_time= 0.305606s \n",
      "Epoch:   4098 loss= 0.276406 acc= 0.944078 cost_time= 0.331206s \n",
      "Epoch:   4099 loss= 0.648517 acc= 0.833914 cost_time= 0.302495s \n",
      "Epoch:   4100 loss= 0.272166 acc= 0.933898 cost_time= 0.311401s \n",
      "Epoch:   4101 loss= 0.361384 acc= 0.911008 cost_time= 0.308252s \n",
      "Epoch:   4102 loss= 0.259801 acc= 0.943010 cost_time= 0.333903s \n",
      "Epoch:   4103 loss= 0.204369 acc= 0.956077 cost_time= 0.310035s \n",
      "Epoch:   4104 loss= 0.391661 acc= 0.917482 cost_time= 0.317312s \n",
      "Epoch:   4105 loss= 0.308595 acc= 0.940882 cost_time= 0.303563s \n",
      "Epoch:   4106 loss= 0.371127 acc= 0.917756 cost_time= 0.307520s \n",
      "Epoch:   4107 loss= 0.211666 acc= 0.951503 cost_time= 0.315058s \n",
      "Epoch:   4108 loss= 0.532389 acc= 0.895121 cost_time= 0.307249s \n",
      "Epoch:   4109 loss= 0.332010 acc= 0.933286 cost_time= 0.307075s \n",
      "Epoch:   4110 loss= 0.252095 acc= 0.955265 cost_time= 0.331820s \n",
      "Epoch:   4111 loss= 0.281720 acc= 0.941083 cost_time= 0.306772s \n",
      "Epoch:   4112 loss= 1.021036 acc= 0.858729 cost_time= 0.303265s \n",
      "Epoch:   4113 loss= 0.232988 acc= 0.954077 cost_time= 0.314097s \n",
      "Epoch:   4114 loss= 0.207597 acc= 0.950178 cost_time= 0.334476s \n",
      "Epoch:   4115 loss= 0.288943 acc= 0.933342 cost_time= 0.311664s \n",
      "Epoch:   4116 loss= 0.338303 acc= 0.938918 cost_time= 0.331557s \n",
      "Epoch:   4117 loss= 0.265269 acc= 0.947991 cost_time= 0.310737s \n",
      "Epoch:   4118 loss= 0.849779 acc= 0.903182 cost_time= 0.320093s \n",
      "Epoch:   4119 loss= 0.555562 acc= 0.880669 cost_time= 0.307905s \n",
      "Epoch:   4120 loss= 0.318280 acc= 0.934145 cost_time= 0.310427s \n",
      "Epoch:   4121 loss= 0.293142 acc= 0.946004 cost_time= 0.308338s \n",
      "Epoch:   4122 loss= 0.414285 acc= 0.910736 cost_time= 0.312369s \n",
      "Epoch:   4123 loss= 0.244220 acc= 0.950840 cost_time= 0.328993s \n",
      "Epoch:   4124 loss= 0.552696 acc= 0.917122 cost_time= 0.310297s \n",
      "Epoch:   4125 loss= 0.365210 acc= 0.919535 cost_time= 0.309365s \n",
      "Epoch:   4126 loss= 1.559263 acc= 0.850974 cost_time= 0.312483s \n",
      "Epoch:   4127 loss= 0.313539 acc= 0.928836 cost_time= 0.307298s \n",
      "Epoch:   4128 loss= 0.296939 acc= 0.947265 cost_time= 0.309899s \n",
      "Epoch:   4129 loss= 0.270931 acc= 0.939457 cost_time= 0.309436s \n",
      "Epoch:   4130 loss= 0.259845 acc= 0.943564 cost_time= 0.328457s \n",
      "Epoch:   4131 loss= 0.218256 acc= 0.957236 cost_time= 0.305868s \n",
      "Epoch:   4132 loss= 0.433498 acc= 0.926482 cost_time= 0.317409s \n",
      "Epoch:   4133 loss= 0.202593 acc= 0.960516 cost_time= 0.313139s \n",
      "Epoch:   4134 loss= 0.553489 acc= 0.920616 cost_time= 0.308483s \n",
      "Epoch:   4135 loss= 0.500857 acc= 0.926783 cost_time= 0.311052s \n",
      "Epoch:   4136 loss= 0.873404 acc= 0.892182 cost_time= 0.310983s \n",
      "Epoch:   4137 loss= 0.393631 acc= 0.932542 cost_time= 0.307954s \n",
      "Epoch:   4138 loss= 0.280960 acc= 0.944234 cost_time= 0.328552s \n",
      "Epoch:   4139 loss= 0.263855 acc= 0.947042 cost_time= 0.314461s \n",
      "Epoch:   4140 loss= 0.206114 acc= 0.959067 cost_time= 0.306628s \n",
      "Epoch:   4141 loss= 0.279143 acc= 0.936568 cost_time= 0.312143s \n",
      "Epoch:   4142 loss= 0.405625 acc= 0.916566 cost_time= 0.311782s \n",
      "Epoch:   4143 loss= 0.982954 acc= 0.906864 cost_time= 0.316813s \n",
      "Epoch:   4144 loss= 0.361079 acc= 0.917957 cost_time= 0.327410s \n",
      "Epoch:   4145 loss= 0.305263 acc= 0.941407 cost_time= 0.307898s \n",
      "Epoch:   4146 loss= 0.243176 acc= 0.957652 cost_time= 0.319788s \n",
      "Epoch:   4147 loss= 0.224322 acc= 0.951073 cost_time= 0.309078s \n",
      "Epoch:   4148 loss= 0.235976 acc= 0.949469 cost_time= 0.311742s \n",
      "Epoch:   4149 loss= 0.276477 acc= 0.944909 cost_time= 0.316537s \n",
      "Epoch:   4150 loss= 0.258046 acc= 0.946855 cost_time= 0.311752s \n",
      "Epoch:   4151 loss= 0.220928 acc= 0.948808 cost_time= 0.332728s \n",
      "Epoch:   4152 loss= 0.238270 acc= 0.946474 cost_time= 0.306782s \n",
      "Epoch:   4153 loss= 0.200035 acc= 0.951493 cost_time= 0.312703s \n",
      "Epoch:   4154 loss= 1.324271 acc= 0.907460 cost_time= 0.313367s \n",
      "Epoch:   4155 loss= 0.287514 acc= 0.936933 cost_time= 0.306450s \n",
      "Epoch:   4156 loss= 0.225821 acc= 0.940939 cost_time= 0.312243s \n",
      "Epoch:   4157 loss= 0.402467 acc= 0.904183 cost_time= 0.309149s \n",
      "Epoch:   4158 loss= 0.329819 acc= 0.929285 cost_time= 0.329646s \n",
      "Epoch:   4159 loss= 0.226684 acc= 0.950762 cost_time= 0.311025s \n",
      "Epoch:   4160 loss= 0.910225 acc= 0.903452 cost_time= 0.314395s \n",
      "Epoch:   4161 loss= 0.482260 acc= 0.892293 cost_time= 0.304473s \n",
      "Epoch:   4162 loss= 1.343186 acc= 0.830406 cost_time= 0.306806s \n",
      "Epoch:   4163 loss= 0.373580 acc= 0.929658 cost_time= 0.309295s \n",
      "Epoch:   4164 loss= 0.481518 acc= 0.909616 cost_time= 0.316463s \n",
      "Epoch:   4165 loss= 0.824865 acc= 0.871770 cost_time= 0.325752s \n",
      "Epoch:   4166 loss= 0.246104 acc= 0.951327 cost_time= 0.308532s \n",
      "Epoch:   4167 loss= 0.278819 acc= 0.952058 cost_time= 0.327348s \n",
      "Epoch:   4168 loss= 0.202905 acc= 0.959073 cost_time= 0.312878s \n",
      "Epoch:   4169 loss= 0.293714 acc= 0.940568 cost_time= 0.312006s \n",
      "Epoch:   4170 loss= 0.760111 acc= 0.901385 cost_time= 0.309562s \n",
      "Epoch:   4171 loss= 0.277328 acc= 0.943049 cost_time= 0.310801s \n",
      "Epoch:   4172 loss= 0.421086 acc= 0.929994 cost_time= 0.307990s \n",
      "Epoch:   4173 loss= 0.275924 acc= 0.942211 cost_time= 0.308014s \n",
      "Epoch:   4174 loss= 0.252393 acc= 0.944269 cost_time= 0.312704s \n",
      "Epoch:   4175 loss= 0.292514 acc= 0.939030 cost_time= 0.310747s \n",
      "Epoch:   4176 loss= 0.223703 acc= 0.952664 cost_time= 0.308475s \n",
      "Epoch:   4177 loss= 0.694535 acc= 0.883965 cost_time= 0.328539s \n",
      "Epoch:   4178 loss= 0.265277 acc= 0.948915 cost_time= 0.310440s \n",
      "Epoch:   4179 loss= 1.045285 acc= 0.858547 cost_time= 0.304483s \n",
      "Epoch:   4180 loss= 0.282215 acc= 0.934018 cost_time= 0.310560s \n",
      "Epoch:   4181 loss= 0.357736 acc= 0.930023 cost_time= 0.315711s \n",
      "Epoch:   4182 loss= 0.251847 acc= 0.943758 cost_time= 0.314239s \n",
      "Epoch:   4183 loss= 0.699352 acc= 0.893356 cost_time= 0.306964s \n",
      "Epoch:   4184 loss= 0.268095 acc= 0.920603 cost_time= 0.308602s \n",
      "Epoch:   4185 loss= 0.719273 acc= 0.909233 cost_time= 0.308155s \n",
      "Epoch:   4186 loss= 0.744421 acc= 0.891914 cost_time= 0.307729s \n",
      "Epoch:   4187 loss= 0.417897 acc= 0.920635 cost_time= 0.310920s \n",
      "Epoch:   4188 loss= 0.261112 acc= 0.941900 cost_time= 0.314222s \n",
      "Epoch:   4189 loss= 0.671380 acc= 0.846775 cost_time= 0.307600s \n",
      "Epoch:   4190 loss= 0.210479 acc= 0.944201 cost_time= 0.313660s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4191 loss= 0.217845 acc= 0.950773 cost_time= 0.308849s \n",
      "Epoch:   4192 loss= 0.184602 acc= 0.959685 cost_time= 0.309754s \n",
      "Epoch:   4193 loss= 0.249812 acc= 0.946915 cost_time= 0.329539s \n",
      "Epoch:   4194 loss= 0.392190 acc= 0.906490 cost_time= 0.311377s \n",
      "Epoch:   4195 loss= 0.500035 acc= 0.934548 cost_time= 0.308907s \n",
      "Epoch:   4196 loss= 0.494344 acc= 0.924467 cost_time= 0.310379s \n",
      "Epoch:   4197 loss= 0.252154 acc= 0.937555 cost_time= 0.309588s \n",
      "Epoch:   4198 loss= 1.060591 acc= 0.891844 cost_time= 0.314804s \n",
      "Epoch:   4199 loss= 0.383178 acc= 0.921077 cost_time= 0.332165s \n",
      "Epoch:   4200 loss= 0.222391 acc= 0.953140 cost_time= 0.330592s \n",
      "Epoch:   4201 loss= 0.201506 acc= 0.950654 cost_time= 0.310494s \n",
      "Epoch:   4202 loss= 0.609419 acc= 0.883882 cost_time= 0.301498s \n",
      "Epoch:   4203 loss= 0.653608 acc= 0.904234 cost_time= 0.317211s \n",
      "Epoch:   4204 loss= 0.212845 acc= 0.952662 cost_time= 0.331441s \n",
      "Epoch:   4205 loss= 0.254319 acc= 0.945071 cost_time= 0.332117s \n",
      "Epoch:   4206 loss= 0.611674 acc= 0.904752 cost_time= 0.304700s \n",
      "Epoch:   4207 loss= 0.358741 acc= 0.929109 cost_time= 0.328548s \n",
      "Epoch:   4208 loss= 0.217976 acc= 0.952006 cost_time= 0.315913s \n",
      "Epoch:   4209 loss= 0.340910 acc= 0.938330 cost_time= 0.311373s \n",
      "Epoch:   4210 loss= 0.864834 acc= 0.871155 cost_time= 0.310024s \n",
      "Epoch:   4211 loss= 0.249486 acc= 0.942993 cost_time= 0.316952s \n",
      "Epoch:   4212 loss= 0.315475 acc= 0.939513 cost_time= 0.305603s \n",
      "Epoch:   4213 loss= 1.189226 acc= 0.898442 cost_time= 0.310910s \n",
      "Epoch:   4214 loss= 0.261919 acc= 0.938629 cost_time= 0.309397s \n",
      "Epoch:   4215 loss= 0.384709 acc= 0.933779 cost_time= 0.307276s \n",
      "Epoch:   4216 loss= 0.308294 acc= 0.922680 cost_time= 0.328388s \n",
      "Epoch:   4217 loss= 0.734158 acc= 0.856355 cost_time= 0.306280s \n",
      "Epoch:   4218 loss= 0.351676 acc= 0.915710 cost_time= 0.305759s \n",
      "Epoch:   4219 loss= 0.446963 acc= 0.917375 cost_time= 0.304708s \n",
      "Epoch:   4220 loss= 0.294250 acc= 0.945181 cost_time= 0.309893s \n",
      "Epoch:   4221 loss= 0.712226 acc= 0.920925 cost_time= 0.333814s \n",
      "Epoch:   4222 loss= 0.300173 acc= 0.939051 cost_time= 0.310457s \n",
      "Epoch:   4223 loss= 0.214257 acc= 0.950621 cost_time= 0.311332s \n",
      "Epoch:   4224 loss= 0.264743 acc= 0.942854 cost_time= 0.311773s \n",
      "Epoch:   4225 loss= 0.368458 acc= 0.926343 cost_time= 0.306010s \n",
      "Epoch:   4226 loss= 1.374781 acc= 0.840411 cost_time= 0.309379s \n",
      "Epoch:   4227 loss= 0.252657 acc= 0.942088 cost_time= 0.310962s \n",
      "Epoch:   4228 loss= 0.248729 acc= 0.949878 cost_time= 0.335148s \n",
      "Epoch:   4229 loss= 0.461776 acc= 0.922725 cost_time= 0.305552s \n",
      "Epoch:   4230 loss= 0.284435 acc= 0.945825 cost_time= 0.307101s \n",
      "Epoch:   4231 loss= 0.445726 acc= 0.927723 cost_time= 0.313534s \n",
      "Epoch:   4232 loss= 0.273227 acc= 0.949736 cost_time= 0.314937s \n",
      "Epoch:   4233 loss= 0.249404 acc= 0.941186 cost_time= 0.308001s \n",
      "Epoch:   4234 loss= 0.772582 acc= 0.815760 cost_time= 0.306302s \n",
      "Epoch:   4235 loss= 0.352586 acc= 0.924200 cost_time= 0.307909s \n",
      "Epoch:   4236 loss= 0.437274 acc= 0.906527 cost_time= 0.309852s \n",
      "Epoch:   4237 loss= 0.404868 acc= 0.923361 cost_time= 0.308494s \n",
      "Epoch:   4238 loss= 0.278911 acc= 0.949887 cost_time= 0.334506s \n",
      "Epoch:   4239 loss= 0.390719 acc= 0.931108 cost_time= 0.313682s \n",
      "Epoch:   4240 loss= 0.294383 acc= 0.955599 cost_time= 0.308148s \n",
      "Epoch:   4241 loss= 0.670237 acc= 0.868719 cost_time= 0.303877s \n",
      "Epoch:   4242 loss= 0.228101 acc= 0.943696 cost_time= 0.314793s \n",
      "Epoch:   4243 loss= 0.308564 acc= 0.931563 cost_time= 0.312500s \n",
      "Epoch:   4244 loss= 0.280447 acc= 0.936232 cost_time= 0.312461s \n",
      "Epoch:   4245 loss= 0.314275 acc= 0.946617 cost_time= 0.306329s \n",
      "Epoch:   4246 loss= 0.329681 acc= 0.922517 cost_time= 0.307480s \n",
      "Epoch:   4247 loss= 0.213942 acc= 0.952845 cost_time= 0.334166s \n",
      "Epoch:   4248 loss= 0.404199 acc= 0.940802 cost_time= 0.319944s \n",
      "Epoch:   4249 loss= 0.529797 acc= 0.880127 cost_time= 0.311657s \n",
      "Epoch:   4250 loss= 0.243074 acc= 0.945817 cost_time= 0.310517s \n",
      "Epoch:   4251 loss= 0.188727 acc= 0.955059 cost_time= 0.332352s \n",
      "Epoch:   4252 loss= 0.311939 acc= 0.945524 cost_time= 0.308051s \n",
      "Epoch:   4253 loss= 1.308379 acc= 0.881392 cost_time= 0.313075s \n",
      "Epoch:   4254 loss= 0.528514 acc= 0.906398 cost_time= 0.308554s \n",
      "Epoch:   4255 loss= 0.247957 acc= 0.940493 cost_time= 0.339440s \n",
      "Epoch:   4256 loss= 0.398203 acc= 0.915517 cost_time= 0.326156s \n",
      "Epoch:   4257 loss= 0.434811 acc= 0.921803 cost_time= 0.308098s \n",
      "Epoch:   4258 loss= 0.257536 acc= 0.954699 cost_time= 0.331416s \n",
      "Epoch:   4259 loss= 0.569184 acc= 0.886765 cost_time= 0.308641s \n",
      "Epoch:   4260 loss= 0.264104 acc= 0.949042 cost_time= 0.307400s \n",
      "Epoch:   4261 loss= 0.287371 acc= 0.938024 cost_time= 0.309208s \n",
      "Epoch:   4262 loss= 0.252167 acc= 0.950008 cost_time= 0.310373s \n",
      "Epoch:   4263 loss= 0.337248 acc= 0.918618 cost_time= 0.330832s \n",
      "Epoch:   4264 loss= 0.391937 acc= 0.919249 cost_time= 0.303721s \n",
      "Epoch:   4265 loss= 0.294247 acc= 0.936268 cost_time= 0.310533s \n",
      "Epoch:   4266 loss= 0.501256 acc= 0.873488 cost_time= 0.309322s \n",
      "Epoch:   4267 loss= 0.549835 acc= 0.886620 cost_time= 0.311104s \n",
      "Epoch:   4268 loss= 0.233612 acc= 0.936861 cost_time= 0.313217s \n",
      "Epoch:   4269 loss= 0.448601 acc= 0.884016 cost_time= 0.309061s \n",
      "Epoch:   4270 loss= 0.419510 acc= 0.910038 cost_time= 0.317177s \n",
      "Epoch:   4271 loss= 0.337303 acc= 0.926934 cost_time= 0.307831s \n",
      "Epoch:   4272 loss= 0.292135 acc= 0.930285 cost_time= 0.309682s \n",
      "Epoch:   4273 loss= 0.428127 acc= 0.934674 cost_time= 0.316604s \n",
      "Epoch:   4274 loss= 0.226576 acc= 0.949664 cost_time= 0.315459s \n",
      "Epoch:   4275 loss= 0.364360 acc= 0.928193 cost_time= 0.312809s \n",
      "Epoch:   4276 loss= 0.521518 acc= 0.888792 cost_time= 0.306520s \n",
      "Epoch:   4277 loss= 0.247918 acc= 0.945286 cost_time= 0.321896s \n",
      "Epoch:   4278 loss= 0.330812 acc= 0.946423 cost_time= 0.328502s \n",
      "Epoch:   4279 loss= 0.304776 acc= 0.941268 cost_time= 0.310330s \n",
      "Epoch:   4280 loss= 0.272208 acc= 0.954858 cost_time= 0.312075s \n",
      "Epoch:   4281 loss= 0.976329 acc= 0.869923 cost_time= 0.311218s \n",
      "Epoch:   4282 loss= 0.291950 acc= 0.942843 cost_time= 0.312098s \n",
      "Epoch:   4283 loss= 0.223801 acc= 0.943681 cost_time= 0.308616s \n",
      "Epoch:   4284 loss= 0.309997 acc= 0.920909 cost_time= 0.308476s \n",
      "Epoch:   4285 loss= 0.330167 acc= 0.930058 cost_time= 0.316152s \n",
      "Epoch:   4286 loss= 0.197386 acc= 0.952037 cost_time= 0.313640s \n",
      "Epoch:   4287 loss= 0.468348 acc= 0.926762 cost_time= 0.317923s \n",
      "Epoch:   4288 loss= 0.249333 acc= 0.958816 cost_time= 0.313052s \n",
      "Epoch:   4289 loss= 0.292952 acc= 0.932609 cost_time= 0.309542s \n",
      "Epoch:   4290 loss= 0.240311 acc= 0.946594 cost_time= 0.313284s \n",
      "Epoch:   4291 loss= 0.636314 acc= 0.906888 cost_time= 0.319366s \n",
      "Epoch:   4292 loss= 0.300892 acc= 0.935853 cost_time= 0.311381s \n",
      "Epoch:   4293 loss= 0.274812 acc= 0.940420 cost_time= 0.312227s \n",
      "Epoch:   4294 loss= 0.406830 acc= 0.906949 cost_time= 0.303101s \n",
      "Epoch:   4295 loss= 0.420722 acc= 0.899408 cost_time= 0.308206s \n",
      "Epoch:   4296 loss= 0.692084 acc= 0.901234 cost_time= 0.336225s \n",
      "Epoch:   4297 loss= 0.471822 acc= 0.923478 cost_time= 0.308740s \n",
      "Epoch:   4298 loss= 0.313616 acc= 0.948450 cost_time= 0.316159s \n",
      "Epoch:   4299 loss= 0.270339 acc= 0.950121 cost_time= 0.309406s \n",
      "Epoch:   4300 loss= 0.627709 acc= 0.918177 cost_time= 0.332596s \n",
      "Epoch:   4301 loss= 0.215892 acc= 0.960793 cost_time= 0.311657s \n",
      "Epoch:   4302 loss= 0.483906 acc= 0.899570 cost_time= 0.313202s \n",
      "Epoch:   4303 loss= 0.238160 acc= 0.951645 cost_time= 0.311809s \n",
      "Epoch:   4304 loss= 1.418971 acc= 0.875781 cost_time= 0.312540s \n",
      "Epoch:   4305 loss= 0.499754 acc= 0.887386 cost_time= 0.307526s \n",
      "Epoch:   4306 loss= 0.293343 acc= 0.947352 cost_time= 0.313257s \n",
      "Epoch:   4307 loss= 0.284908 acc= 0.940803 cost_time= 0.331439s \n",
      "Epoch:   4308 loss= 0.337895 acc= 0.928829 cost_time= 0.307012s \n",
      "Epoch:   4309 loss= 0.460571 acc= 0.915922 cost_time= 0.313934s \n",
      "Epoch:   4310 loss= 0.264637 acc= 0.937370 cost_time= 0.308494s \n",
      "Epoch:   4311 loss= 0.222638 acc= 0.954120 cost_time= 0.311498s \n",
      "Epoch:   4312 loss= 0.520979 acc= 0.932492 cost_time= 0.312169s \n",
      "Epoch:   4313 loss= 0.237695 acc= 0.936834 cost_time= 0.309927s \n",
      "Epoch:   4314 loss= 0.239071 acc= 0.948474 cost_time= 0.311412s \n",
      "Epoch:   4315 loss= 0.281136 acc= 0.940218 cost_time= 0.313251s \n",
      "Epoch:   4316 loss= 0.280555 acc= 0.944581 cost_time= 0.320085s \n",
      "Epoch:   4317 loss= 0.341448 acc= 0.934192 cost_time= 0.305246s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4318 loss= 0.367254 acc= 0.927962 cost_time= 0.307680s \n",
      "Epoch:   4319 loss= 0.270837 acc= 0.942483 cost_time= 0.310279s \n",
      "Epoch:   4320 loss= 0.258691 acc= 0.947599 cost_time= 0.312986s \n",
      "Epoch:   4321 loss= 0.993251 acc= 0.878781 cost_time= 0.306200s \n",
      "Epoch:   4322 loss= 0.358390 acc= 0.923285 cost_time= 0.305500s \n",
      "Epoch:   4323 loss= 0.355960 acc= 0.919773 cost_time= 0.313797s \n",
      "Epoch:   4324 loss= 0.223178 acc= 0.947302 cost_time= 0.331956s \n",
      "Epoch:   4325 loss= 0.217734 acc= 0.944156 cost_time= 0.312249s \n",
      "Epoch:   4326 loss= 0.255090 acc= 0.951347 cost_time= 0.311416s \n",
      "Epoch:   4327 loss= 1.148313 acc= 0.813356 cost_time= 0.308964s \n",
      "Epoch:   4328 loss= 0.264965 acc= 0.954737 cost_time= 0.332435s \n",
      "Epoch:   4329 loss= 0.247048 acc= 0.948939 cost_time= 0.309442s \n",
      "Epoch:   4330 loss= 0.281943 acc= 0.940935 cost_time= 0.316590s \n",
      "Epoch:   4331 loss= 0.370988 acc= 0.925122 cost_time= 0.307048s \n",
      "Epoch:   4332 loss= 0.269497 acc= 0.951206 cost_time= 0.315521s \n",
      "Epoch:   4333 loss= 0.203965 acc= 0.958689 cost_time= 0.314845s \n",
      "Epoch:   4334 loss= 0.267324 acc= 0.949667 cost_time= 0.311028s \n",
      "Epoch:   4335 loss= 0.241792 acc= 0.953559 cost_time= 0.307499s \n",
      "Epoch:   4336 loss= 0.407373 acc= 0.926373 cost_time= 0.310454s \n",
      "Epoch:   4337 loss= 1.196500 acc= 0.848204 cost_time= 0.317693s \n",
      "Epoch:   4338 loss= 0.259775 acc= 0.947085 cost_time= 0.308725s \n",
      "Epoch:   4339 loss= 0.453608 acc= 0.928993 cost_time= 0.311473s \n",
      "Epoch:   4340 loss= 0.565863 acc= 0.884994 cost_time= 0.307909s \n",
      "Epoch:   4341 loss= 0.212956 acc= 0.966165 cost_time= 0.314909s \n",
      "Epoch:   4342 loss= 0.399292 acc= 0.899808 cost_time= 0.331204s \n",
      "Epoch:   4343 loss= 0.286671 acc= 0.946122 cost_time= 0.318855s \n",
      "Epoch:   4344 loss= 0.229823 acc= 0.953994 cost_time= 0.309301s \n",
      "Epoch:   4345 loss= 0.234503 acc= 0.950722 cost_time= 0.309359s \n",
      "Epoch:   4346 loss= 0.227646 acc= 0.958039 cost_time= 0.313551s \n",
      "Epoch:   4347 loss= 0.591290 acc= 0.906201 cost_time= 0.330952s \n",
      "Epoch:   4348 loss= 0.230499 acc= 0.953455 cost_time= 0.312455s \n",
      "Epoch:   4349 loss= 0.277607 acc= 0.937841 cost_time= 0.310216s \n",
      "Epoch:   4350 loss= 0.529368 acc= 0.916402 cost_time= 0.313174s \n",
      "Epoch:   4351 loss= 0.593911 acc= 0.923459 cost_time= 0.309403s \n",
      "Epoch:   4352 loss= 0.517489 acc= 0.876149 cost_time= 0.304616s \n",
      "Epoch:   4353 loss= 0.279389 acc= 0.937101 cost_time= 0.321978s \n",
      "Epoch:   4354 loss= 0.929986 acc= 0.901844 cost_time= 0.329124s \n",
      "Epoch:   4355 loss= 0.759053 acc= 0.855746 cost_time= 0.310981s \n",
      "Epoch:   4356 loss= 0.215400 acc= 0.952108 cost_time= 0.310957s \n",
      "Epoch:   4357 loss= 0.406644 acc= 0.908410 cost_time= 0.304014s \n",
      "Epoch:   4358 loss= 0.228410 acc= 0.951444 cost_time= 0.318403s \n",
      "Epoch:   4359 loss= 0.760256 acc= 0.914871 cost_time= 0.311645s \n",
      "Epoch:   4360 loss= 0.245190 acc= 0.954438 cost_time= 0.309939s \n",
      "Epoch:   4361 loss= 0.319632 acc= 0.933955 cost_time= 0.316211s \n",
      "Epoch:   4362 loss= 0.192151 acc= 0.958128 cost_time= 0.313462s \n",
      "Epoch:   4363 loss= 0.242231 acc= 0.943956 cost_time= 0.331211s \n",
      "Epoch:   4364 loss= 0.253382 acc= 0.949846 cost_time= 0.314263s \n",
      "Epoch:   4365 loss= 0.243144 acc= 0.947439 cost_time= 0.309760s \n",
      "Epoch:   4366 loss= 0.174709 acc= 0.959396 cost_time= 0.318711s \n",
      "Epoch:   4367 loss= 0.244035 acc= 0.950181 cost_time= 0.317830s \n",
      "Epoch:   4368 loss= 0.323456 acc= 0.942840 cost_time= 0.313166s \n",
      "Epoch:   4369 loss= 0.719857 acc= 0.890818 cost_time= 0.314627s \n",
      "Epoch:   4370 loss= 0.366788 acc= 0.909547 cost_time= 0.329148s \n",
      "Epoch:   4371 loss= 0.303039 acc= 0.935670 cost_time= 0.309533s \n",
      "Epoch:   4372 loss= 0.251396 acc= 0.948340 cost_time= 0.311332s \n",
      "Epoch:   4373 loss= 0.241480 acc= 0.943841 cost_time= 0.319573s \n",
      "Epoch:   4374 loss= 0.520650 acc= 0.912603 cost_time= 0.306119s \n",
      "Epoch:   4375 loss= 0.326453 acc= 0.922213 cost_time= 0.310413s \n",
      "Epoch:   4376 loss= 0.543490 acc= 0.878058 cost_time= 0.304239s \n",
      "Epoch:   4377 loss= 0.289271 acc= 0.944599 cost_time= 0.334239s \n",
      "Epoch:   4378 loss= 0.506252 acc= 0.901450 cost_time= 0.306406s \n",
      "Epoch:   4379 loss= 0.238131 acc= 0.951000 cost_time= 0.320481s \n",
      "Epoch:   4380 loss= 0.529116 acc= 0.931448 cost_time= 0.309036s \n",
      "Epoch:   4381 loss= 0.243060 acc= 0.949089 cost_time= 0.307024s \n",
      "Epoch:   4382 loss= 0.268634 acc= 0.941868 cost_time= 0.310635s \n",
      "Epoch:   4383 loss= 0.864678 acc= 0.814259 cost_time= 0.302261s \n",
      "Epoch:   4384 loss= 0.262247 acc= 0.944042 cost_time= 0.332361s \n",
      "Epoch:   4385 loss= 0.830911 acc= 0.873042 cost_time= 0.307761s \n",
      "Epoch:   4386 loss= 0.245036 acc= 0.949395 cost_time= 0.310938s \n",
      "Epoch:   4387 loss= 0.285639 acc= 0.950495 cost_time= 0.308511s \n",
      "Epoch:   4388 loss= 0.221945 acc= 0.951214 cost_time= 0.312289s \n",
      "Epoch:   4389 loss= 0.261833 acc= 0.942539 cost_time= 0.330847s \n",
      "Epoch:   4390 loss= 0.462031 acc= 0.924499 cost_time= 0.315362s \n",
      "Epoch:   4391 loss= 0.299917 acc= 0.933615 cost_time= 0.312293s \n",
      "Epoch:   4392 loss= 0.241854 acc= 0.951233 cost_time= 0.310220s \n",
      "Epoch:   4393 loss= 0.391810 acc= 0.888483 cost_time= 0.314323s \n",
      "Epoch:   4394 loss= 0.377149 acc= 0.919392 cost_time= 0.303867s \n",
      "Epoch:   4395 loss= 0.266869 acc= 0.951851 cost_time= 0.308757s \n",
      "Epoch:   4396 loss= 0.380875 acc= 0.911771 cost_time= 0.315959s \n",
      "Epoch:   4397 loss= 0.239401 acc= 0.947077 cost_time= 0.310579s \n",
      "Epoch:   4398 loss= 0.216143 acc= 0.968672 cost_time= 0.312589s \n",
      "Epoch:   4399 loss= 0.474758 acc= 0.892271 cost_time= 0.308542s \n",
      "Epoch:   4400 loss= 0.511952 acc= 0.877752 cost_time= 0.301493s \n",
      "Epoch:   4401 loss= 0.593244 acc= 0.906332 cost_time= 0.313751s \n",
      "Epoch:   4402 loss= 0.313400 acc= 0.930425 cost_time= 0.307037s \n",
      "Epoch:   4403 loss= 0.234798 acc= 0.945280 cost_time= 0.313335s \n",
      "Epoch:   4404 loss= 0.283469 acc= 0.941543 cost_time= 0.314506s \n",
      "Epoch:   4405 loss= 0.185177 acc= 0.954534 cost_time= 0.337066s \n",
      "Epoch:   4406 loss= 0.254696 acc= 0.953882 cost_time= 0.309469s \n",
      "Epoch:   4407 loss= 0.385024 acc= 0.909729 cost_time= 0.311172s \n",
      "Epoch:   4408 loss= 0.359677 acc= 0.923708 cost_time= 0.314015s \n",
      "Epoch:   4409 loss= 0.490415 acc= 0.904610 cost_time= 0.311444s \n",
      "Epoch:   4410 loss= 0.510224 acc= 0.891516 cost_time= 0.306823s \n",
      "Epoch:   4411 loss= 0.266239 acc= 0.942860 cost_time= 0.308166s \n",
      "Epoch:   4412 loss= 0.237770 acc= 0.963851 cost_time= 0.331966s \n",
      "Epoch:   4413 loss= 0.513367 acc= 0.881691 cost_time= 0.305582s \n",
      "Epoch:   4414 loss= 0.347158 acc= 0.912033 cost_time= 0.314987s \n",
      "Epoch:   4415 loss= 0.260127 acc= 0.951616 cost_time= 0.313782s \n",
      "Epoch:   4416 loss= 0.208976 acc= 0.951328 cost_time= 0.311404s \n",
      "Epoch:   4417 loss= 0.323049 acc= 0.908767 cost_time= 0.308349s \n",
      "Epoch:   4418 loss= 0.270212 acc= 0.945009 cost_time= 0.309897s \n",
      "Epoch:   4419 loss= 0.462048 acc= 0.926171 cost_time= 0.330819s \n",
      "Epoch:   4420 loss= 0.442632 acc= 0.925597 cost_time= 0.304212s \n",
      "Epoch:   4421 loss= 0.272425 acc= 0.945841 cost_time= 0.305188s \n",
      "Epoch:   4422 loss= 0.421422 acc= 0.915364 cost_time= 0.305310s \n",
      "Epoch:   4423 loss= 0.227005 acc= 0.956636 cost_time= 0.312392s \n",
      "Epoch:   4424 loss= 0.248570 acc= 0.942566 cost_time= 0.307759s \n",
      "Epoch:   4425 loss= 0.852836 acc= 0.845928 cost_time= 0.308806s \n",
      "Epoch:   4426 loss= 0.274819 acc= 0.947736 cost_time= 0.329011s \n",
      "Epoch:   4427 loss= 0.234494 acc= 0.963681 cost_time= 0.311886s \n",
      "Epoch:   4428 loss= 0.395920 acc= 0.905052 cost_time= 0.316095s \n",
      "Epoch:   4429 loss= 0.266613 acc= 0.930506 cost_time= 0.309169s \n",
      "Epoch:   4430 loss= 0.215276 acc= 0.966237 cost_time= 0.315280s \n",
      "Epoch:   4431 loss= 0.223652 acc= 0.953585 cost_time= 0.307024s \n",
      "Epoch:   4432 loss= 0.479834 acc= 0.890159 cost_time= 0.308972s \n",
      "Epoch:   4433 loss= 0.262082 acc= 0.937655 cost_time= 0.311111s \n",
      "Epoch:   4434 loss= 0.302991 acc= 0.936915 cost_time= 0.334795s \n",
      "Epoch:   4435 loss= 0.183904 acc= 0.949872 cost_time= 0.317968s \n",
      "Epoch:   4436 loss= 0.223637 acc= 0.947212 cost_time= 0.309678s \n",
      "Epoch:   4437 loss= 0.258544 acc= 0.935501 cost_time= 0.316755s \n",
      "Epoch:   4438 loss= 0.278209 acc= 0.950066 cost_time= 0.331270s \n",
      "Epoch:   4439 loss= 0.268077 acc= 0.943739 cost_time= 0.306760s \n",
      "Epoch:   4440 loss= 0.245657 acc= 0.938588 cost_time= 0.330941s \n",
      "Epoch:   4441 loss= 0.314543 acc= 0.937989 cost_time= 0.313998s \n",
      "Epoch:   4442 loss= 0.236823 acc= 0.942497 cost_time= 0.311908s \n",
      "Epoch:   4443 loss= 0.339171 acc= 0.923630 cost_time= 0.313781s \n",
      "Epoch:   4444 loss= 0.193877 acc= 0.963809 cost_time= 0.314567s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4445 loss= 0.268360 acc= 0.944608 cost_time= 0.311032s \n",
      "Epoch:   4446 loss= 0.599080 acc= 0.885535 cost_time= 0.313486s \n",
      "Epoch:   4447 loss= 0.330267 acc= 0.920452 cost_time= 0.327736s \n",
      "Epoch:   4448 loss= 0.265976 acc= 0.948248 cost_time= 0.314781s \n",
      "Epoch:   4449 loss= 0.240565 acc= 0.940036 cost_time= 0.316292s \n",
      "Epoch:   4450 loss= 0.282184 acc= 0.940001 cost_time= 0.309518s \n",
      "Epoch:   4451 loss= 0.756302 acc= 0.906913 cost_time= 0.308024s \n",
      "Epoch:   4452 loss= 0.387680 acc= 0.904185 cost_time= 0.308892s \n",
      "Epoch:   4453 loss= 0.589573 acc= 0.914458 cost_time= 0.312960s \n",
      "Epoch:   4454 loss= 0.273883 acc= 0.936195 cost_time= 0.327568s \n",
      "Epoch:   4455 loss= 0.215463 acc= 0.960258 cost_time= 0.309116s \n",
      "Epoch:   4456 loss= 0.216310 acc= 0.946181 cost_time= 0.320757s \n",
      "Epoch:   4457 loss= 0.375002 acc= 0.934546 cost_time= 0.310771s \n",
      "Epoch:   4458 loss= 0.242035 acc= 0.956100 cost_time= 0.312487s \n",
      "Epoch:   4459 loss= 0.265808 acc= 0.940408 cost_time= 0.310426s \n",
      "Epoch:   4460 loss= 0.476303 acc= 0.878239 cost_time= 0.305837s \n",
      "Epoch:   4461 loss= 0.302335 acc= 0.938114 cost_time= 0.319345s \n",
      "Epoch:   4462 loss= 0.549213 acc= 0.929494 cost_time= 0.313766s \n",
      "Epoch:   4463 loss= 0.375615 acc= 0.933402 cost_time= 0.320752s \n",
      "Epoch:   4464 loss= 0.253254 acc= 0.957886 cost_time= 0.315812s \n",
      "Epoch:   4465 loss= 0.394852 acc= 0.917525 cost_time= 0.306355s \n",
      "Epoch:   4466 loss= 1.004102 acc= 0.845380 cost_time= 0.310864s \n",
      "Epoch:   4467 loss= 0.266079 acc= 0.941726 cost_time= 0.308283s \n",
      "Epoch:   4468 loss= 0.603979 acc= 0.881436 cost_time= 0.322959s \n",
      "Epoch:   4469 loss= 1.303294 acc= 0.864185 cost_time= 0.304593s \n",
      "Epoch:   4470 loss= 0.229600 acc= 0.957392 cost_time= 0.327602s \n",
      "Epoch:   4471 loss= 0.613251 acc= 0.920772 cost_time= 0.307681s \n",
      "Epoch:   4472 loss= 0.327311 acc= 0.933899 cost_time= 0.309311s \n",
      "Epoch:   4473 loss= 0.277715 acc= 0.941279 cost_time= 0.318702s \n",
      "Epoch:   4474 loss= 0.281634 acc= 0.940504 cost_time= 0.310358s \n",
      "Epoch:   4475 loss= 0.193235 acc= 0.950622 cost_time= 0.313311s \n",
      "Epoch:   4476 loss= 0.211506 acc= 0.957124 cost_time= 0.312400s \n",
      "Epoch:   4477 loss= 0.230280 acc= 0.953849 cost_time= 0.308403s \n",
      "Epoch:   4478 loss= 0.731988 acc= 0.908688 cost_time= 0.307720s \n",
      "Epoch:   4479 loss= 0.335475 acc= 0.930765 cost_time= 0.308524s \n",
      "Epoch:   4480 loss= 0.283042 acc= 0.941436 cost_time= 0.317840s \n",
      "Epoch:   4481 loss= 0.222510 acc= 0.950994 cost_time= 0.315239s \n",
      "Epoch:   4482 loss= 0.211134 acc= 0.953688 cost_time= 0.312485s \n",
      "Epoch:   4483 loss= 0.380008 acc= 0.914951 cost_time= 0.307447s \n",
      "Epoch:   4484 loss= 0.323592 acc= 0.940899 cost_time= 0.315720s \n",
      "Epoch:   4485 loss= 0.296340 acc= 0.942953 cost_time= 0.309603s \n",
      "Epoch:   4486 loss= 0.262928 acc= 0.943525 cost_time= 0.311074s \n",
      "Epoch:   4487 loss= 0.217422 acc= 0.953384 cost_time= 0.314316s \n",
      "Epoch:   4488 loss= 0.287427 acc= 0.938508 cost_time= 0.306549s \n",
      "Epoch:   4489 loss= 0.255561 acc= 0.945687 cost_time= 0.308503s \n",
      "Epoch:   4490 loss= 0.746374 acc= 0.896315 cost_time= 0.334633s \n",
      "Epoch:   4491 loss= 0.224302 acc= 0.957735 cost_time= 0.319025s \n",
      "Epoch:   4492 loss= 0.570558 acc= 0.882067 cost_time= 0.301588s \n",
      "Epoch:   4493 loss= 0.211840 acc= 0.950705 cost_time= 0.308422s \n",
      "Epoch:   4494 loss= 0.352617 acc= 0.924266 cost_time= 0.307357s \n",
      "Epoch:   4495 loss= 0.374130 acc= 0.931910 cost_time= 0.312613s \n",
      "Epoch:   4496 loss= 0.267657 acc= 0.940510 cost_time= 0.335032s \n",
      "Epoch:   4497 loss= 0.415938 acc= 0.902999 cost_time= 0.305155s \n",
      "Epoch:   4498 loss= 0.574153 acc= 0.924293 cost_time= 0.322176s \n",
      "Epoch:   4499 loss= 0.302572 acc= 0.934230 cost_time= 0.305535s \n",
      "Epoch:   4500 loss= 0.425482 acc= 0.933538 cost_time= 0.307819s \n",
      "Epoch:   4501 loss= 0.339818 acc= 0.931548 cost_time= 0.310951s \n",
      "Epoch:   4502 loss= 0.344662 acc= 0.944953 cost_time= 0.320901s \n",
      "Epoch:   4503 loss= 0.263398 acc= 0.941252 cost_time= 0.307120s \n",
      "Epoch:   4504 loss= 0.317797 acc= 0.932774 cost_time= 0.306561s \n",
      "Epoch:   4505 loss= 1.208730 acc= 0.851497 cost_time= 0.315543s \n",
      "Epoch:   4506 loss= 0.423476 acc= 0.913549 cost_time= 0.310194s \n",
      "Epoch:   4507 loss= 0.609767 acc= 0.866928 cost_time= 0.308183s \n",
      "Epoch:   4508 loss= 0.566763 acc= 0.921455 cost_time= 0.314053s \n",
      "Epoch:   4509 loss= 0.412912 acc= 0.934346 cost_time= 0.303011s \n",
      "Epoch:   4510 loss= 0.277061 acc= 0.933514 cost_time= 0.329345s \n",
      "Epoch:   4511 loss= 0.389023 acc= 0.928359 cost_time= 0.314790s \n",
      "Epoch:   4512 loss= 0.244427 acc= 0.946871 cost_time= 0.320526s \n",
      "Epoch:   4513 loss= 0.395459 acc= 0.922586 cost_time= 0.311432s \n",
      "Epoch:   4514 loss= 0.371560 acc= 0.922082 cost_time= 0.307296s \n",
      "Epoch:   4515 loss= 0.295234 acc= 0.927699 cost_time= 0.310287s \n",
      "Epoch:   4516 loss= 0.243086 acc= 0.954526 cost_time= 0.314456s \n",
      "Epoch:   4517 loss= 0.284547 acc= 0.930001 cost_time= 0.328304s \n",
      "Epoch:   4518 loss= 0.195797 acc= 0.958069 cost_time= 0.313327s \n",
      "Epoch:   4519 loss= 0.435418 acc= 0.898864 cost_time= 0.307464s \n",
      "Epoch:   4520 loss= 0.239349 acc= 0.952070 cost_time= 0.314801s \n",
      "Epoch:   4521 loss= 0.364206 acc= 0.923563 cost_time= 0.306566s \n",
      "Epoch:   4522 loss= 0.741857 acc= 0.898933 cost_time= 0.310171s \n",
      "Epoch:   4523 loss= 0.632641 acc= 0.886816 cost_time= 0.314049s \n",
      "Epoch:   4524 loss= 0.761850 acc= 0.907242 cost_time= 0.331348s \n",
      "Epoch:   4525 loss= 1.009155 acc= 0.844175 cost_time= 0.307999s \n",
      "Epoch:   4526 loss= 0.201828 acc= 0.954088 cost_time= 0.315136s \n",
      "Epoch:   4527 loss= 0.277064 acc= 0.941776 cost_time= 0.310364s \n",
      "Epoch:   4528 loss= 0.237706 acc= 0.946400 cost_time= 0.308644s \n",
      "Epoch:   4529 loss= 0.500452 acc= 0.918103 cost_time= 0.330101s \n",
      "Epoch:   4530 loss= 0.246778 acc= 0.944786 cost_time= 0.311696s \n",
      "Epoch:   4531 loss= 0.263838 acc= 0.948719 cost_time= 0.312708s \n",
      "Epoch:   4532 loss= 0.381434 acc= 0.920899 cost_time= 0.311421s \n",
      "Epoch:   4533 loss= 0.339269 acc= 0.926259 cost_time= 0.312775s \n",
      "Epoch:   4534 loss= 0.688515 acc= 0.902296 cost_time= 0.315782s \n",
      "Epoch:   4535 loss= 0.217850 acc= 0.952837 cost_time= 0.316424s \n",
      "Epoch:   4536 loss= 0.723265 acc= 0.862682 cost_time= 0.324239s \n",
      "Epoch:   4537 loss= 0.262400 acc= 0.939651 cost_time= 0.310540s \n",
      "Epoch:   4538 loss= 0.197991 acc= 0.953521 cost_time= 0.310696s \n",
      "Epoch:   4539 loss= 0.927209 acc= 0.906320 cost_time= 0.312001s \n",
      "Epoch:   4540 loss= 0.475236 acc= 0.885178 cost_time= 0.308454s \n",
      "Epoch:   4541 loss= 0.301543 acc= 0.928547 cost_time= 0.307204s \n",
      "Epoch:   4542 loss= 0.333001 acc= 0.945923 cost_time= 0.309160s \n",
      "Epoch:   4543 loss= 0.347856 acc= 0.913835 cost_time= 0.313618s \n",
      "Epoch:   4544 loss= 0.200152 acc= 0.955218 cost_time= 0.315770s \n",
      "Epoch:   4545 loss= 0.670412 acc= 0.920537 cost_time= 0.311684s \n",
      "Epoch:   4546 loss= 0.228336 acc= 0.942888 cost_time= 0.312125s \n",
      "Epoch:   4547 loss= 0.396113 acc= 0.895638 cost_time= 0.308413s \n",
      "Epoch:   4548 loss= 0.411012 acc= 0.909984 cost_time= 0.309427s \n",
      "Epoch:   4549 loss= 0.956020 acc= 0.857319 cost_time= 0.324577s \n",
      "Epoch:   4550 loss= 0.212969 acc= 0.945004 cost_time= 0.311929s \n",
      "Epoch:   4551 loss= 0.236299 acc= 0.955261 cost_time= 0.316269s \n",
      "Epoch:   4552 loss= 0.203289 acc= 0.948863 cost_time= 0.311586s \n",
      "Epoch:   4553 loss= 0.223506 acc= 0.957123 cost_time= 0.310665s \n",
      "Epoch:   4554 loss= 0.265813 acc= 0.950828 cost_time= 0.308802s \n",
      "Epoch:   4555 loss= 0.222213 acc= 0.960072 cost_time= 0.316668s \n",
      "Epoch:   4556 loss= 0.675015 acc= 0.886122 cost_time= 0.333015s \n",
      "Epoch:   4557 loss= 0.823003 acc= 0.867487 cost_time= 0.309552s \n",
      "Epoch:   4558 loss= 0.205006 acc= 0.955936 cost_time= 0.309631s \n",
      "Epoch:   4559 loss= 0.349572 acc= 0.935473 cost_time= 0.315296s \n",
      "Epoch:   4560 loss= 0.252053 acc= 0.949547 cost_time= 0.312257s \n",
      "Epoch:   4561 loss= 0.230015 acc= 0.957471 cost_time= 0.308114s \n",
      "Epoch:   4562 loss= 0.383325 acc= 0.919386 cost_time= 0.305744s \n",
      "Epoch:   4563 loss= 0.242501 acc= 0.951842 cost_time= 0.311241s \n",
      "Epoch:   4564 loss= 0.258793 acc= 0.935633 cost_time= 0.328677s \n",
      "Epoch:   4565 loss= 0.219713 acc= 0.950495 cost_time= 0.311079s \n",
      "Epoch:   4566 loss= 0.838250 acc= 0.893197 cost_time= 0.330220s \n",
      "Epoch:   4567 loss= 0.389966 acc= 0.935693 cost_time= 0.311368s \n",
      "Epoch:   4568 loss= 0.272284 acc= 0.941444 cost_time= 0.336069s \n",
      "Epoch:   4569 loss= 0.267088 acc= 0.948893 cost_time= 0.311149s \n",
      "Epoch:   4570 loss= 0.679222 acc= 0.886045 cost_time= 0.300531s \n",
      "Epoch:   4571 loss= 0.217002 acc= 0.954712 cost_time= 0.317234s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4572 loss= 0.230772 acc= 0.961311 cost_time= 0.318990s \n",
      "Epoch:   4573 loss= 0.225372 acc= 0.945696 cost_time= 0.327338s \n",
      "Epoch:   4574 loss= 0.345194 acc= 0.926630 cost_time= 0.305834s \n",
      "Epoch:   4575 loss= 0.255536 acc= 0.949546 cost_time= 0.310372s \n",
      "Epoch:   4576 loss= 0.526467 acc= 0.889607 cost_time= 0.305692s \n",
      "Epoch:   4577 loss= 0.221712 acc= 0.953624 cost_time= 0.313412s \n",
      "Epoch:   4578 loss= 0.513098 acc= 0.922503 cost_time= 0.310096s \n",
      "Epoch:   4579 loss= 0.466848 acc= 0.912468 cost_time= 0.309956s \n",
      "Epoch:   4580 loss= 0.297425 acc= 0.933423 cost_time= 0.331430s \n",
      "Epoch:   4581 loss= 0.355973 acc= 0.930060 cost_time= 0.310026s \n",
      "Epoch:   4582 loss= 0.341623 acc= 0.924762 cost_time= 0.328769s \n",
      "Epoch:   4583 loss= 0.982286 acc= 0.866125 cost_time= 0.308503s \n",
      "Epoch:   4584 loss= 0.407151 acc= 0.904490 cost_time= 0.326192s \n",
      "Epoch:   4585 loss= 0.355634 acc= 0.931991 cost_time= 0.303819s \n",
      "Epoch:   4586 loss= 0.256497 acc= 0.940366 cost_time= 0.313358s \n",
      "Epoch:   4587 loss= 0.225632 acc= 0.951054 cost_time= 0.318877s \n",
      "Epoch:   4588 loss= 0.800421 acc= 0.867942 cost_time= 0.304232s \n",
      "Epoch:   4589 loss= 0.697380 acc= 0.827906 cost_time= 0.321523s \n",
      "Epoch:   4590 loss= 0.244138 acc= 0.951889 cost_time= 0.308718s \n",
      "Epoch:   4591 loss= 0.233254 acc= 0.950262 cost_time= 0.315054s \n",
      "Epoch:   4592 loss= 0.341007 acc= 0.924399 cost_time= 0.311689s \n",
      "Epoch:   4593 loss= 0.220912 acc= 0.958372 cost_time= 0.316940s \n",
      "Epoch:   4594 loss= 0.402181 acc= 0.903796 cost_time= 0.308112s \n",
      "Epoch:   4595 loss= 1.078261 acc= 0.862227 cost_time= 0.316988s \n",
      "Epoch:   4596 loss= 0.237751 acc= 0.953387 cost_time= 0.308700s \n",
      "Epoch:   4597 loss= 0.227549 acc= 0.953856 cost_time= 0.314280s \n",
      "Epoch:   4598 loss= 0.264988 acc= 0.947425 cost_time= 0.312107s \n",
      "Epoch:   4599 loss= 0.240614 acc= 0.941205 cost_time= 0.310971s \n",
      "Epoch:   4600 loss= 0.254078 acc= 0.954185 cost_time= 0.314867s \n",
      "Epoch:   4601 loss= 0.380728 acc= 0.913865 cost_time= 0.309676s \n",
      "Epoch:   4602 loss= 0.290562 acc= 0.942518 cost_time= 0.328465s \n",
      "Epoch:   4603 loss= 0.295069 acc= 0.939332 cost_time= 0.332499s \n",
      "Epoch:   4604 loss= 0.320970 acc= 0.931567 cost_time= 0.306597s \n",
      "Epoch:   4605 loss= 0.897193 acc= 0.888919 cost_time= 0.326453s \n",
      "Epoch:   4606 loss= 0.561346 acc= 0.876483 cost_time= 0.313435s \n",
      "Epoch:   4607 loss= 0.264851 acc= 0.926337 cost_time= 0.330490s \n",
      "Epoch:   4608 loss= 0.209149 acc= 0.957028 cost_time= 0.310879s \n",
      "Epoch:   4609 loss= 0.352128 acc= 0.903831 cost_time= 0.310477s \n",
      "Epoch:   4610 loss= 0.256039 acc= 0.944797 cost_time= 0.310595s \n",
      "Epoch:   4611 loss= 0.233846 acc= 0.948786 cost_time= 0.314874s \n",
      "Epoch:   4612 loss= 0.309441 acc= 0.926892 cost_time= 0.321348s \n",
      "Epoch:   4613 loss= 0.339685 acc= 0.917357 cost_time= 0.309125s \n",
      "Epoch:   4614 loss= 0.212423 acc= 0.952335 cost_time= 0.329157s \n",
      "Epoch:   4615 loss= 0.207973 acc= 0.948452 cost_time= 0.321196s \n",
      "Epoch:   4616 loss= 0.222823 acc= 0.957927 cost_time= 0.312142s \n",
      "Epoch:   4617 loss= 0.175721 acc= 0.968348 cost_time= 0.309520s \n",
      "Epoch:   4618 loss= 0.412429 acc= 0.915737 cost_time= 0.311002s \n",
      "Epoch:   4619 loss= 0.208685 acc= 0.959544 cost_time= 0.314255s \n",
      "Epoch:   4620 loss= 0.597740 acc= 0.921903 cost_time= 0.310804s \n",
      "Epoch:   4621 loss= 0.409173 acc= 0.905011 cost_time= 0.323458s \n",
      "Epoch:   4622 loss= 0.218660 acc= 0.954795 cost_time= 0.310170s \n",
      "Epoch:   4623 loss= 0.277372 acc= 0.941210 cost_time= 0.310077s \n",
      "Epoch:   4624 loss= 0.285555 acc= 0.941176 cost_time= 0.315850s \n",
      "Epoch:   4625 loss= 0.886714 acc= 0.878161 cost_time= 0.305983s \n",
      "Epoch:   4626 loss= 0.209005 acc= 0.959924 cost_time= 0.334940s \n",
      "Epoch:   4627 loss= 0.323531 acc= 0.926605 cost_time= 0.308125s \n",
      "Epoch:   4628 loss= 0.231192 acc= 0.944242 cost_time= 0.310526s \n",
      "Epoch:   4629 loss= 0.312140 acc= 0.926774 cost_time= 0.311073s \n",
      "Epoch:   4630 loss= 0.212568 acc= 0.951026 cost_time= 0.313961s \n",
      "Epoch:   4631 loss= 0.208779 acc= 0.950482 cost_time= 0.313144s \n",
      "Epoch:   4632 loss= 0.324948 acc= 0.923069 cost_time= 0.317429s \n",
      "Epoch:   4633 loss= 0.234748 acc= 0.941282 cost_time= 0.309086s \n",
      "Epoch:   4634 loss= 0.250030 acc= 0.941765 cost_time= 0.314990s \n",
      "Epoch:   4635 loss= 0.751184 acc= 0.894319 cost_time= 0.333025s \n",
      "Epoch:   4636 loss= 0.425035 acc= 0.922458 cost_time= 0.316907s \n",
      "Epoch:   4637 loss= 0.245837 acc= 0.957511 cost_time= 0.318402s \n",
      "Epoch:   4638 loss= 0.181318 acc= 0.955299 cost_time= 0.313776s \n",
      "Epoch:   4639 loss= 0.314931 acc= 0.932639 cost_time= 0.310923s \n",
      "Epoch:   4640 loss= 0.272218 acc= 0.944919 cost_time= 0.307404s \n",
      "Epoch:   4641 loss= 0.294587 acc= 0.951146 cost_time= 0.306837s \n",
      "Epoch:   4642 loss= 0.225815 acc= 0.945880 cost_time= 0.311019s \n",
      "Epoch:   4643 loss= 0.547060 acc= 0.921201 cost_time= 0.313098s \n",
      "Epoch:   4644 loss= 0.283704 acc= 0.930078 cost_time= 0.314536s \n",
      "Epoch:   4645 loss= 0.333059 acc= 0.917954 cost_time= 0.311999s \n",
      "Epoch:   4646 loss= 0.642676 acc= 0.892224 cost_time= 0.310899s \n",
      "Epoch:   4647 loss= 0.850244 acc= 0.908807 cost_time= 0.309343s \n",
      "Epoch:   4648 loss= 0.227215 acc= 0.961658 cost_time= 0.314056s \n",
      "Epoch:   4649 loss= 0.483906 acc= 0.901482 cost_time= 0.327280s \n",
      "Epoch:   4650 loss= 0.245061 acc= 0.949268 cost_time= 0.312240s \n",
      "Epoch:   4651 loss= 0.297162 acc= 0.940607 cost_time= 0.319417s \n",
      "Epoch:   4652 loss= 0.195917 acc= 0.947321 cost_time= 0.310406s \n",
      "Epoch:   4653 loss= 0.274516 acc= 0.945237 cost_time= 0.311518s \n",
      "Epoch:   4654 loss= 0.309727 acc= 0.934326 cost_time= 0.310953s \n",
      "Epoch:   4655 loss= 0.177398 acc= 0.958812 cost_time= 0.314773s \n",
      "Epoch:   4656 loss= 0.238394 acc= 0.951135 cost_time= 0.317323s \n",
      "Epoch:   4657 loss= 0.223045 acc= 0.948000 cost_time= 0.309143s \n",
      "Epoch:   4658 loss= 0.225695 acc= 0.947125 cost_time= 0.315343s \n",
      "Epoch:   4659 loss= 0.262748 acc= 0.943155 cost_time= 0.329611s \n",
      "Epoch:   4660 loss= 0.318514 acc= 0.938706 cost_time= 0.309609s \n",
      "Epoch:   4661 loss= 0.199055 acc= 0.959007 cost_time= 0.308135s \n",
      "Epoch:   4662 loss= 0.312947 acc= 0.923790 cost_time= 0.303774s \n",
      "Epoch:   4663 loss= 0.252761 acc= 0.951353 cost_time= 0.327418s \n",
      "Epoch:   4664 loss= 0.406880 acc= 0.919998 cost_time= 0.311457s \n",
      "Epoch:   4665 loss= 0.448980 acc= 0.924622 cost_time= 0.319653s \n",
      "Epoch:   4666 loss= 1.265973 acc= 0.911237 cost_time= 0.313422s \n",
      "Epoch:   4667 loss= 0.504983 acc= 0.898790 cost_time= 0.304395s \n",
      "Epoch:   4668 loss= 0.227089 acc= 0.958306 cost_time= 0.311146s \n",
      "Epoch:   4669 loss= 0.374040 acc= 0.941018 cost_time= 0.316447s \n",
      "Epoch:   4670 loss= 0.577234 acc= 0.922289 cost_time= 0.309965s \n",
      "Epoch:   4671 loss= 0.247132 acc= 0.947666 cost_time= 0.313754s \n",
      "Epoch:   4672 loss= 0.730707 acc= 0.883925 cost_time= 0.308458s \n",
      "Epoch:   4673 loss= 0.207022 acc= 0.951550 cost_time= 0.311044s \n",
      "Epoch:   4674 loss= 0.229533 acc= 0.951856 cost_time= 0.310441s \n",
      "Epoch:   4675 loss= 0.263719 acc= 0.945434 cost_time= 0.329559s \n",
      "Epoch:   4676 loss= 0.279461 acc= 0.943696 cost_time= 0.308225s \n",
      "Epoch:   4677 loss= 0.252050 acc= 0.955849 cost_time= 0.332562s \n",
      "Epoch:   4678 loss= 0.279918 acc= 0.944780 cost_time= 0.316081s \n",
      "Epoch:   4679 loss= 0.408362 acc= 0.912927 cost_time= 0.314774s \n",
      "Epoch:   4680 loss= 0.212932 acc= 0.954230 cost_time= 0.309545s \n",
      "Epoch:   4681 loss= 0.245187 acc= 0.954099 cost_time= 0.308278s \n",
      "Epoch:   4682 loss= 0.696627 acc= 0.901217 cost_time= 0.313707s \n",
      "Epoch:   4683 loss= 0.383604 acc= 0.920937 cost_time= 0.312269s \n",
      "Epoch:   4684 loss= 0.359010 acc= 0.908725 cost_time= 0.330023s \n",
      "Epoch:   4685 loss= 0.225908 acc= 0.946972 cost_time= 0.309744s \n",
      "Epoch:   4686 loss= 0.421260 acc= 0.907122 cost_time= 0.315513s \n",
      "Epoch:   4687 loss= 0.234454 acc= 0.944354 cost_time= 0.312041s \n",
      "Epoch:   4688 loss= 0.319253 acc= 0.934478 cost_time= 0.310848s \n",
      "Epoch:   4689 loss= 0.246370 acc= 0.947699 cost_time= 0.307060s \n",
      "Epoch:   4690 loss= 0.684497 acc= 0.909762 cost_time= 0.312204s \n",
      "Epoch:   4691 loss= 0.410489 acc= 0.912389 cost_time= 0.311009s \n",
      "Epoch:   4692 loss= 0.323614 acc= 0.936592 cost_time= 0.309816s \n",
      "Epoch:   4693 loss= 0.498351 acc= 0.924281 cost_time= 0.316010s \n",
      "Epoch:   4694 loss= 0.212504 acc= 0.959221 cost_time= 0.309734s \n",
      "Epoch:   4695 loss= 0.453346 acc= 0.918183 cost_time= 0.312797s \n",
      "Epoch:   4696 loss= 0.232033 acc= 0.952657 cost_time= 0.312048s \n",
      "Epoch:   4697 loss= 0.395061 acc= 0.923485 cost_time= 0.305380s \n",
      "Epoch:   4698 loss= 0.292056 acc= 0.946071 cost_time= 0.334694s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4699 loss= 0.340351 acc= 0.919973 cost_time= 0.311213s \n",
      "Epoch:   4700 loss= 0.199157 acc= 0.957363 cost_time= 0.323598s \n",
      "Epoch:   4701 loss= 1.023188 acc= 0.871485 cost_time= 0.315477s \n",
      "Epoch:   4702 loss= 0.631392 acc= 0.908460 cost_time= 0.316842s \n",
      "Epoch:   4703 loss= 0.312331 acc= 0.941136 cost_time= 0.307219s \n",
      "Epoch:   4704 loss= 0.407298 acc= 0.919105 cost_time= 0.314559s \n",
      "Epoch:   4705 loss= 0.262976 acc= 0.945611 cost_time= 0.314489s \n",
      "Epoch:   4706 loss= 0.823652 acc= 0.926725 cost_time= 0.318245s \n",
      "Epoch:   4707 loss= 0.206973 acc= 0.952735 cost_time= 0.318262s \n",
      "Epoch:   4708 loss= 0.217423 acc= 0.953172 cost_time= 0.309200s \n",
      "Epoch:   4709 loss= 0.202263 acc= 0.947747 cost_time= 0.316004s \n",
      "Epoch:   4710 loss= 0.341585 acc= 0.922014 cost_time= 0.309556s \n",
      "Epoch:   4711 loss= 0.485738 acc= 0.859868 cost_time= 0.312508s \n",
      "Epoch:   4712 loss= 0.274769 acc= 0.935277 cost_time= 0.328624s \n",
      "Epoch:   4713 loss= 0.254065 acc= 0.957252 cost_time= 0.315881s \n",
      "Epoch:   4714 loss= 0.318315 acc= 0.929645 cost_time= 0.310343s \n",
      "Epoch:   4715 loss= 0.246713 acc= 0.946686 cost_time= 0.313955s \n",
      "Epoch:   4716 loss= 0.681115 acc= 0.848128 cost_time= 0.303469s \n",
      "Epoch:   4717 loss= 0.454927 acc= 0.904850 cost_time= 0.303257s \n",
      "Epoch:   4718 loss= 0.194921 acc= 0.955308 cost_time= 0.314617s \n",
      "Epoch:   4719 loss= 0.254666 acc= 0.938754 cost_time= 0.330120s \n",
      "Epoch:   4720 loss= 0.678604 acc= 0.904660 cost_time= 0.309546s \n",
      "Epoch:   4721 loss= 0.428929 acc= 0.915578 cost_time= 0.310567s \n",
      "Epoch:   4722 loss= 0.222304 acc= 0.945368 cost_time= 0.311106s \n",
      "Epoch:   4723 loss= 0.211019 acc= 0.957306 cost_time= 0.310165s \n",
      "Epoch:   4724 loss= 0.259689 acc= 0.959031 cost_time= 0.329282s \n",
      "Epoch:   4725 loss= 0.679648 acc= 0.884419 cost_time= 0.310420s \n",
      "Epoch:   4726 loss= 0.434990 acc= 0.899230 cost_time= 0.304747s \n",
      "Epoch:   4727 loss= 0.205007 acc= 0.961517 cost_time= 0.310982s \n",
      "Epoch:   4728 loss= 0.343036 acc= 0.925023 cost_time= 0.313081s \n",
      "Epoch:   4729 loss= 0.394406 acc= 0.924732 cost_time= 0.306269s \n",
      "Epoch:   4730 loss= 0.215249 acc= 0.955161 cost_time= 0.312691s \n",
      "Epoch:   4731 loss= 0.420477 acc= 0.919340 cost_time= 0.306776s \n",
      "Epoch:   4732 loss= 0.412176 acc= 0.907505 cost_time= 0.307974s \n",
      "Epoch:   4733 loss= 0.297435 acc= 0.937849 cost_time= 0.328160s \n",
      "Epoch:   4734 loss= 0.303456 acc= 0.940383 cost_time= 0.306563s \n",
      "Epoch:   4735 loss= 0.253295 acc= 0.951862 cost_time= 0.313902s \n",
      "Epoch:   4736 loss= 0.227191 acc= 0.951827 cost_time= 0.311121s \n",
      "Epoch:   4737 loss= 0.233459 acc= 0.937606 cost_time= 0.314238s \n",
      "Epoch:   4738 loss= 0.342176 acc= 0.929699 cost_time= 0.316270s \n",
      "Epoch:   4739 loss= 0.386109 acc= 0.907248 cost_time= 0.307827s \n",
      "Epoch:   4740 loss= 0.224210 acc= 0.953263 cost_time= 0.315767s \n",
      "Epoch:   4741 loss= 0.582077 acc= 0.890489 cost_time= 0.327652s \n",
      "Epoch:   4742 loss= 0.466891 acc= 0.907587 cost_time= 0.316982s \n",
      "Epoch:   4743 loss= 0.208411 acc= 0.959720 cost_time= 0.311794s \n",
      "Epoch:   4744 loss= 0.348590 acc= 0.928091 cost_time= 0.308777s \n",
      "Epoch:   4745 loss= 0.390874 acc= 0.927782 cost_time= 0.309627s \n",
      "Epoch:   4746 loss= 0.274469 acc= 0.942249 cost_time= 0.308333s \n",
      "Epoch:   4747 loss= 0.207673 acc= 0.947907 cost_time= 0.329900s \n",
      "Epoch:   4748 loss= 0.945926 acc= 0.808993 cost_time= 0.301809s \n",
      "Epoch:   4749 loss= 0.495696 acc= 0.886906 cost_time= 0.310974s \n",
      "Epoch:   4750 loss= 0.220369 acc= 0.958384 cost_time= 0.309012s \n",
      "Epoch:   4751 loss= 0.802593 acc= 0.890064 cost_time= 0.305855s \n",
      "Epoch:   4752 loss= 0.237009 acc= 0.954277 cost_time= 0.309446s \n",
      "Epoch:   4753 loss= 0.418776 acc= 0.897253 cost_time= 0.309523s \n",
      "Epoch:   4754 loss= 0.205671 acc= 0.952106 cost_time= 0.312552s \n",
      "Epoch:   4755 loss= 0.421961 acc= 0.885427 cost_time= 0.307186s \n",
      "Epoch:   4756 loss= 0.369909 acc= 0.925621 cost_time= 0.312886s \n",
      "Epoch:   4757 loss= 0.273987 acc= 0.940547 cost_time= 0.311975s \n",
      "Epoch:   4758 loss= 0.340734 acc= 0.934064 cost_time= 0.309513s \n",
      "Epoch:   4759 loss= 0.229403 acc= 0.940941 cost_time= 0.329414s \n",
      "Epoch:   4760 loss= 0.672648 acc= 0.893204 cost_time= 0.312425s \n",
      "Epoch:   4761 loss= 0.879690 acc= 0.849943 cost_time= 0.304905s \n",
      "Epoch:   4762 loss= 0.211616 acc= 0.956907 cost_time= 0.317456s \n",
      "Epoch:   4763 loss= 0.395654 acc= 0.917421 cost_time= 0.308040s \n",
      "Epoch:   4764 loss= 0.268417 acc= 0.932177 cost_time= 0.314936s \n",
      "Epoch:   4765 loss= 0.257914 acc= 0.948129 cost_time= 0.316391s \n",
      "Epoch:   4766 loss= 0.204459 acc= 0.959294 cost_time= 0.332678s \n",
      "Epoch:   4767 loss= 0.221811 acc= 0.945000 cost_time= 0.311343s \n",
      "Epoch:   4768 loss= 0.251784 acc= 0.949380 cost_time= 0.325304s \n",
      "Epoch:   4769 loss= 0.212629 acc= 0.962173 cost_time= 0.309954s \n",
      "Epoch:   4770 loss= 0.236809 acc= 0.955782 cost_time= 0.311836s \n",
      "Epoch:   4771 loss= 0.570427 acc= 0.913415 cost_time= 0.311519s \n",
      "Epoch:   4772 loss= 0.213799 acc= 0.961387 cost_time= 0.311089s \n",
      "Epoch:   4773 loss= 0.347110 acc= 0.914163 cost_time= 0.305340s \n",
      "Epoch:   4774 loss= 0.272428 acc= 0.936440 cost_time= 0.315608s \n",
      "Epoch:   4775 loss= 0.242066 acc= 0.951139 cost_time= 0.333504s \n",
      "Epoch:   4776 loss= 1.014495 acc= 0.898617 cost_time= 0.309372s \n",
      "Epoch:   4777 loss= 0.766023 acc= 0.912454 cost_time= 0.315034s \n",
      "Epoch:   4778 loss= 0.586336 acc= 0.935074 cost_time= 0.309437s \n",
      "Epoch:   4779 loss= 0.235789 acc= 0.959190 cost_time= 0.312782s \n",
      "Epoch:   4780 loss= 0.270682 acc= 0.945451 cost_time= 0.327824s \n",
      "Epoch:   4781 loss= 0.306002 acc= 0.934726 cost_time= 0.304919s \n",
      "Epoch:   4782 loss= 0.267246 acc= 0.947310 cost_time= 0.315030s \n",
      "Epoch:   4783 loss= 0.232735 acc= 0.946889 cost_time= 0.307789s \n",
      "Epoch:   4784 loss= 0.534430 acc= 0.904695 cost_time= 0.315882s \n",
      "Epoch:   4785 loss= 0.278951 acc= 0.951033 cost_time= 0.335209s \n",
      "Epoch:   4786 loss= 0.455483 acc= 0.896072 cost_time= 0.309781s \n",
      "Epoch:   4787 loss= 0.249032 acc= 0.943786 cost_time= 0.306624s \n",
      "Epoch:   4788 loss= 0.296883 acc= 0.933305 cost_time= 0.308859s \n",
      "Epoch:   4789 loss= 0.200338 acc= 0.959690 cost_time= 0.334009s \n",
      "Epoch:   4790 loss= 0.676118 acc= 0.918191 cost_time= 0.310170s \n",
      "Epoch:   4791 loss= 0.252769 acc= 0.936552 cost_time= 0.311257s \n",
      "Epoch:   4792 loss= 0.524283 acc= 0.879561 cost_time= 0.307856s \n",
      "Epoch:   4793 loss= 0.202414 acc= 0.951356 cost_time= 0.320116s \n",
      "Epoch:   4794 loss= 0.260594 acc= 0.949901 cost_time= 0.311882s \n",
      "Epoch:   4795 loss= 0.207408 acc= 0.956234 cost_time= 0.314538s \n",
      "Epoch:   4796 loss= 0.217976 acc= 0.951031 cost_time= 0.334162s \n",
      "Epoch:   4797 loss= 0.215717 acc= 0.965630 cost_time= 0.309509s \n",
      "Epoch:   4798 loss= 0.235866 acc= 0.952166 cost_time= 0.318227s \n",
      "Epoch:   4799 loss= 0.613240 acc= 0.900419 cost_time= 0.312460s \n",
      "Epoch:   4800 loss= 0.209417 acc= 0.953132 cost_time= 0.315404s \n",
      "Epoch:   4801 loss= 0.309637 acc= 0.932067 cost_time= 0.307218s \n",
      "Epoch:   4802 loss= 0.315551 acc= 0.943282 cost_time= 0.306110s \n",
      "Epoch:   4803 loss= 0.773676 acc= 0.891295 cost_time= 0.311174s \n",
      "Epoch:   4804 loss= 0.256047 acc= 0.943489 cost_time= 0.313239s \n",
      "Epoch:   4805 loss= 0.263145 acc= 0.946206 cost_time= 0.312605s \n",
      "Epoch:   4806 loss= 0.219267 acc= 0.955227 cost_time= 0.326617s \n",
      "Epoch:   4807 loss= 0.178840 acc= 0.962042 cost_time= 0.311062s \n",
      "Epoch:   4808 loss= 0.251069 acc= 0.953203 cost_time= 0.313416s \n",
      "Epoch:   4809 loss= 0.231946 acc= 0.944876 cost_time= 0.311456s \n",
      "Epoch:   4810 loss= 0.632140 acc= 0.906226 cost_time= 0.328262s \n",
      "Epoch:   4811 loss= 0.247670 acc= 0.953342 cost_time= 0.321846s \n",
      "Epoch:   4812 loss= 0.260704 acc= 0.943028 cost_time= 0.311879s \n",
      "Epoch:   4813 loss= 0.862526 acc= 0.861544 cost_time= 0.306386s \n",
      "Epoch:   4814 loss= 0.234222 acc= 0.956397 cost_time= 0.316332s \n",
      "Epoch:   4815 loss= 0.246838 acc= 0.946936 cost_time= 0.311507s \n",
      "Epoch:   4816 loss= 0.514004 acc= 0.913451 cost_time= 0.315347s \n",
      "Epoch:   4817 loss= 0.307038 acc= 0.931273 cost_time= 0.309041s \n",
      "Epoch:   4818 loss= 0.410345 acc= 0.883896 cost_time= 0.307062s \n",
      "Epoch:   4819 loss= 0.270066 acc= 0.948714 cost_time= 0.332507s \n",
      "Epoch:   4820 loss= 0.418368 acc= 0.912460 cost_time= 0.305331s \n",
      "Epoch:   4821 loss= 0.380180 acc= 0.931637 cost_time= 0.319230s \n",
      "Epoch:   4822 loss= 0.320790 acc= 0.927859 cost_time= 0.310423s \n",
      "Epoch:   4823 loss= 0.283230 acc= 0.944468 cost_time= 0.309767s \n",
      "Epoch:   4824 loss= 0.287284 acc= 0.945009 cost_time= 0.310922s \n",
      "Epoch:   4825 loss= 0.253666 acc= 0.945739 cost_time= 0.314589s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4826 loss= 0.599863 acc= 0.910056 cost_time= 0.313425s \n",
      "Epoch:   4827 loss= 0.242433 acc= 0.947141 cost_time= 0.327643s \n",
      "Epoch:   4828 loss= 0.236936 acc= 0.950175 cost_time= 0.314803s \n",
      "Epoch:   4829 loss= 0.297373 acc= 0.941787 cost_time= 0.308556s \n",
      "Epoch:   4830 loss= 0.217277 acc= 0.960344 cost_time= 0.307517s \n",
      "Epoch:   4831 loss= 0.254901 acc= 0.947143 cost_time= 0.310834s \n",
      "Epoch:   4832 loss= 0.257493 acc= 0.952413 cost_time= 0.309742s \n",
      "Epoch:   4833 loss= 0.245673 acc= 0.952112 cost_time= 0.331964s \n",
      "Epoch:   4834 loss= 0.344531 acc= 0.925319 cost_time= 0.311086s \n",
      "Epoch:   4835 loss= 0.194554 acc= 0.950522 cost_time= 0.311050s \n",
      "Epoch:   4836 loss= 0.213834 acc= 0.955351 cost_time= 0.309625s \n",
      "Epoch:   4837 loss= 0.251902 acc= 0.941031 cost_time= 0.309519s \n",
      "Epoch:   4838 loss= 0.632714 acc= 0.858183 cost_time= 0.326647s \n",
      "Epoch:   4839 loss= 0.217954 acc= 0.948104 cost_time= 0.314267s \n",
      "Epoch:   4840 loss= 0.203281 acc= 0.962609 cost_time= 0.309211s \n",
      "Epoch:   4841 loss= 0.239494 acc= 0.945416 cost_time= 0.308361s \n",
      "Epoch:   4842 loss= 0.256956 acc= 0.937256 cost_time= 0.318698s \n",
      "Epoch:   4843 loss= 0.328645 acc= 0.926724 cost_time= 0.309276s \n",
      "Epoch:   4844 loss= 0.333116 acc= 0.943789 cost_time= 0.312675s \n",
      "Epoch:   4845 loss= 0.444955 acc= 0.903799 cost_time= 0.311350s \n",
      "Epoch:   4846 loss= 0.377413 acc= 0.890197 cost_time= 0.314705s \n",
      "Epoch:   4847 loss= 0.587225 acc= 0.920025 cost_time= 0.315188s \n",
      "Epoch:   4848 loss= 0.240709 acc= 0.956502 cost_time= 0.309348s \n",
      "Epoch:   4849 loss= 0.490776 acc= 0.918743 cost_time= 0.308220s \n",
      "Epoch:   4850 loss= 0.214157 acc= 0.955064 cost_time= 0.314965s \n",
      "Epoch:   4851 loss= 0.249713 acc= 0.947966 cost_time= 0.312874s \n",
      "Epoch:   4852 loss= 0.206323 acc= 0.953813 cost_time= 0.316211s \n",
      "Epoch:   4853 loss= 0.289702 acc= 0.937053 cost_time= 0.329204s \n",
      "Epoch:   4854 loss= 0.287192 acc= 0.936564 cost_time= 0.331259s \n",
      "Epoch:   4855 loss= 0.438215 acc= 0.936152 cost_time= 0.310672s \n",
      "Epoch:   4856 loss= 0.300230 acc= 0.942033 cost_time= 0.311469s \n",
      "Epoch:   4857 loss= 0.231538 acc= 0.937426 cost_time= 0.329655s \n",
      "Epoch:   4858 loss= 0.627408 acc= 0.913775 cost_time= 0.311247s \n",
      "Epoch:   4859 loss= 0.640204 acc= 0.897032 cost_time= 0.310133s \n",
      "Epoch:   4860 loss= 0.198308 acc= 0.951874 cost_time= 0.309318s \n",
      "Epoch:   4861 loss= 0.227289 acc= 0.956064 cost_time= 0.328289s \n",
      "Epoch:   4862 loss= 0.233546 acc= 0.948178 cost_time= 0.311051s \n",
      "Epoch:   4863 loss= 0.241691 acc= 0.955648 cost_time= 0.319826s \n",
      "Epoch:   4864 loss= 0.259792 acc= 0.944563 cost_time= 0.308355s \n",
      "Epoch:   4865 loss= 0.214115 acc= 0.955617 cost_time= 0.309212s \n",
      "Epoch:   4866 loss= 0.262194 acc= 0.951082 cost_time= 0.313216s \n",
      "Epoch:   4867 loss= 0.291111 acc= 0.952383 cost_time= 0.311680s \n",
      "Epoch:   4868 loss= 0.209126 acc= 0.950259 cost_time= 0.317299s \n",
      "Epoch:   4869 loss= 0.438258 acc= 0.898157 cost_time= 0.311230s \n",
      "Epoch:   4870 loss= 0.189649 acc= 0.956198 cost_time= 0.314173s \n",
      "Epoch:   4871 loss= 0.245024 acc= 0.953515 cost_time= 0.309228s \n",
      "Epoch:   4872 loss= 0.390555 acc= 0.917070 cost_time= 0.307192s \n",
      "Epoch:   4873 loss= 1.111866 acc= 0.846767 cost_time= 0.308106s \n",
      "Epoch:   4874 loss= 0.428064 acc= 0.913147 cost_time= 0.306069s \n",
      "Epoch:   4875 loss= 0.658842 acc= 0.891104 cost_time= 0.325854s \n",
      "Epoch:   4876 loss= 0.279236 acc= 0.932289 cost_time= 0.312912s \n",
      "Epoch:   4877 loss= 0.281025 acc= 0.939381 cost_time= 0.310593s \n",
      "Epoch:   4878 loss= 0.220159 acc= 0.954695 cost_time= 0.310803s \n",
      "Epoch:   4879 loss= 0.241157 acc= 0.953994 cost_time= 0.319039s \n",
      "Epoch:   4880 loss= 0.283911 acc= 0.949616 cost_time= 0.313455s \n",
      "Epoch:   4881 loss= 0.296879 acc= 0.931926 cost_time= 0.309422s \n",
      "Epoch:   4882 loss= 0.185196 acc= 0.961091 cost_time= 0.331136s \n",
      "Epoch:   4883 loss= 0.235139 acc= 0.946948 cost_time= 0.309546s \n",
      "Epoch:   4884 loss= 0.223154 acc= 0.962254 cost_time= 0.313822s \n",
      "Epoch:   4885 loss= 0.219389 acc= 0.944165 cost_time= 0.308303s \n",
      "Epoch:   4886 loss= 0.356809 acc= 0.908693 cost_time= 0.307612s \n",
      "Epoch:   4887 loss= 0.285987 acc= 0.935532 cost_time= 0.329489s \n",
      "Epoch:   4888 loss= 0.302556 acc= 0.933835 cost_time= 0.306376s \n",
      "Epoch:   4889 loss= 0.442714 acc= 0.902397 cost_time= 0.332103s \n",
      "Epoch:   4890 loss= 0.223230 acc= 0.952841 cost_time= 0.310017s \n",
      "Epoch:   4891 loss= 0.240336 acc= 0.943647 cost_time= 0.311987s \n",
      "Epoch:   4892 loss= 0.569359 acc= 0.893156 cost_time= 0.302882s \n",
      "Epoch:   4893 loss= 0.202122 acc= 0.955111 cost_time= 0.310387s \n",
      "Epoch:   4894 loss= 0.446097 acc= 0.933125 cost_time= 0.336305s \n",
      "Epoch:   4895 loss= 0.474030 acc= 0.918298 cost_time= 0.313918s \n",
      "Epoch:   4896 loss= 0.211686 acc= 0.956982 cost_time= 0.333386s \n",
      "Epoch:   4897 loss= 0.623514 acc= 0.909912 cost_time= 0.307239s \n",
      "Epoch:   4898 loss= 0.363759 acc= 0.946538 cost_time= 0.309463s \n",
      "Epoch:   4899 loss= 0.275274 acc= 0.941981 cost_time= 0.308317s \n",
      "Epoch:   4900 loss= 0.265099 acc= 0.950803 cost_time= 0.313879s \n",
      "Epoch:   4901 loss= 0.758582 acc= 0.883378 cost_time= 0.309291s \n",
      "Epoch:   4902 loss= 0.196753 acc= 0.956661 cost_time= 0.315573s \n",
      "Epoch:   4903 loss= 0.229852 acc= 0.943598 cost_time= 0.333061s \n",
      "Epoch:   4904 loss= 0.405340 acc= 0.918917 cost_time= 0.305827s \n",
      "Epoch:   4905 loss= 0.300081 acc= 0.950594 cost_time= 0.311237s \n",
      "Epoch:   4906 loss= 0.222257 acc= 0.949162 cost_time= 0.312746s \n",
      "Epoch:   4907 loss= 0.383958 acc= 0.912389 cost_time= 0.309104s \n",
      "Epoch:   4908 loss= 0.240586 acc= 0.955614 cost_time= 0.332750s \n",
      "Epoch:   4909 loss= 0.918781 acc= 0.898469 cost_time= 0.312596s \n",
      "Epoch:   4910 loss= 0.646226 acc= 0.904228 cost_time= 0.309681s \n",
      "Epoch:   4911 loss= 0.177176 acc= 0.953300 cost_time= 0.314739s \n",
      "Epoch:   4912 loss= 0.235951 acc= 0.953897 cost_time= 0.313520s \n",
      "Epoch:   4913 loss= 0.802867 acc= 0.906376 cost_time= 0.314928s \n",
      "Epoch:   4914 loss= 0.204650 acc= 0.956967 cost_time= 0.316230s \n",
      "Epoch:   4915 loss= 0.761261 acc= 0.914772 cost_time= 0.314755s \n",
      "Epoch:   4916 loss= 0.463752 acc= 0.914883 cost_time= 0.319329s \n",
      "Epoch:   4917 loss= 0.219037 acc= 0.948368 cost_time= 0.331275s \n",
      "Epoch:   4918 loss= 0.212601 acc= 0.955766 cost_time= 0.315109s \n",
      "Epoch:   4919 loss= 1.116943 acc= 0.883001 cost_time= 0.311646s \n",
      "Epoch:   4920 loss= 0.358714 acc= 0.913856 cost_time= 0.315196s \n",
      "Epoch:   4921 loss= 0.252072 acc= 0.954692 cost_time= 0.306631s \n",
      "Epoch:   4922 loss= 0.393066 acc= 0.900946 cost_time= 0.305100s \n",
      "Epoch:   4923 loss= 0.471359 acc= 0.919100 cost_time= 0.306254s \n",
      "Epoch:   4924 loss= 0.236229 acc= 0.954867 cost_time= 0.334343s \n",
      "Epoch:   4925 loss= 0.198683 acc= 0.956234 cost_time= 0.313099s \n",
      "Epoch:   4926 loss= 0.466777 acc= 0.898641 cost_time= 0.304256s \n",
      "Epoch:   4927 loss= 0.308165 acc= 0.932665 cost_time= 0.315145s \n",
      "Epoch:   4928 loss= 0.398128 acc= 0.919598 cost_time= 0.308266s \n",
      "Epoch:   4929 loss= 0.573082 acc= 0.882578 cost_time= 0.325057s \n",
      "Epoch:   4930 loss= 0.393306 acc= 0.897813 cost_time= 0.305473s \n",
      "Epoch:   4931 loss= 0.409590 acc= 0.930585 cost_time= 0.307273s \n",
      "Epoch:   4932 loss= 0.232896 acc= 0.941609 cost_time= 0.314579s \n",
      "Epoch:   4933 loss= 0.496754 acc= 0.920280 cost_time= 0.320817s \n",
      "Epoch:   4934 loss= 0.237778 acc= 0.963917 cost_time= 0.312088s \n",
      "Epoch:   4935 loss= 0.288837 acc= 0.932000 cost_time= 0.311049s \n",
      "Epoch:   4936 loss= 0.230893 acc= 0.960775 cost_time= 0.305550s \n",
      "Epoch:   4937 loss= 0.433118 acc= 0.898622 cost_time= 0.308585s \n",
      "Epoch:   4938 loss= 0.265154 acc= 0.951287 cost_time= 0.332240s \n",
      "Epoch:   4939 loss= 0.239897 acc= 0.951453 cost_time= 0.308051s \n",
      "Epoch:   4940 loss= 0.205466 acc= 0.963384 cost_time= 0.317427s \n",
      "Epoch:   4941 loss= 0.290236 acc= 0.935870 cost_time= 0.305177s \n",
      "Epoch:   4942 loss= 0.339214 acc= 0.919945 cost_time= 0.305906s \n",
      "Epoch:   4943 loss= 0.283925 acc= 0.936702 cost_time= 0.311683s \n",
      "Epoch:   4944 loss= 0.291685 acc= 0.921179 cost_time= 0.309839s \n",
      "Epoch:   4945 loss= 0.224331 acc= 0.954680 cost_time= 0.335366s \n",
      "Epoch:   4946 loss= 0.267559 acc= 0.937894 cost_time= 0.309227s \n",
      "Epoch:   4947 loss= 0.180214 acc= 0.955891 cost_time= 0.309521s \n",
      "Epoch:   4948 loss= 0.434610 acc= 0.914435 cost_time= 0.303755s \n",
      "Epoch:   4949 loss= 0.275868 acc= 0.947020 cost_time= 0.310839s \n",
      "Epoch:   4950 loss= 0.357157 acc= 0.926742 cost_time= 0.306667s \n",
      "Epoch:   4951 loss= 0.273682 acc= 0.951485 cost_time= 0.310121s \n",
      "Epoch:   4952 loss= 0.269130 acc= 0.954663 cost_time= 0.332742s \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   4953 loss= 0.366439 acc= 0.927771 cost_time= 0.306230s \n",
      "Epoch:   4954 loss= 0.202140 acc= 0.965842 cost_time= 0.318680s \n",
      "Epoch:   4955 loss= 0.192555 acc= 0.959576 cost_time= 0.315033s \n",
      "Epoch:   4956 loss= 0.288153 acc= 0.942859 cost_time= 0.312404s \n",
      "Epoch:   4957 loss= 0.429971 acc= 0.898783 cost_time= 0.307314s \n",
      "Epoch:   4958 loss= 0.241556 acc= 0.959210 cost_time= 0.312616s \n",
      "Epoch:   4959 loss= 0.263869 acc= 0.954988 cost_time= 0.310951s \n",
      "Epoch:   4960 loss= 0.634624 acc= 0.869760 cost_time= 0.310197s \n",
      "Epoch:   4961 loss= 0.415296 acc= 0.931946 cost_time= 0.307343s \n",
      "Epoch:   4962 loss= 0.250229 acc= 0.952018 cost_time= 0.312800s \n",
      "Epoch:   4963 loss= 0.205701 acc= 0.959605 cost_time= 0.333759s \n",
      "Epoch:   4964 loss= 0.770736 acc= 0.906593 cost_time= 0.329151s \n",
      "Epoch:   4965 loss= 0.229855 acc= 0.950547 cost_time= 0.316953s \n",
      "Epoch:   4966 loss= 0.223405 acc= 0.956311 cost_time= 0.335097s \n",
      "Epoch:   4967 loss= 0.219603 acc= 0.957655 cost_time= 0.311656s \n",
      "Epoch:   4968 loss= 0.368664 acc= 0.918019 cost_time= 0.307755s \n",
      "Epoch:   4969 loss= 0.188331 acc= 0.959803 cost_time= 0.309876s \n",
      "Epoch:   4970 loss= 0.328290 acc= 0.916894 cost_time= 0.303195s \n",
      "Epoch:   4971 loss= 0.181472 acc= 0.957757 cost_time= 0.316643s \n",
      "Epoch:   4972 loss= 0.290317 acc= 0.942444 cost_time= 0.310338s \n",
      "Epoch:   4973 loss= 0.440494 acc= 0.891332 cost_time= 0.327464s \n",
      "Epoch:   4974 loss= 0.948114 acc= 0.896893 cost_time= 0.314198s \n",
      "Epoch:   4975 loss= 1.028825 acc= 0.899373 cost_time= 0.306858s \n",
      "Epoch:   4976 loss= 0.228563 acc= 0.951402 cost_time= 0.314571s \n",
      "Epoch:   4977 loss= 0.298172 acc= 0.938504 cost_time= 0.310103s \n",
      "Epoch:   4978 loss= 0.681390 acc= 0.914666 cost_time= 0.331547s \n",
      "Epoch:   4979 loss= 0.504961 acc= 0.922388 cost_time= 0.309915s \n",
      "Epoch:   4980 loss= 0.245072 acc= 0.938612 cost_time= 0.309098s \n",
      "Epoch:   4981 loss= 0.214989 acc= 0.959154 cost_time= 0.310519s \n",
      "Epoch:   4982 loss= 0.412507 acc= 0.913499 cost_time= 0.315025s \n",
      "Epoch:   4983 loss= 0.813838 acc= 0.886321 cost_time= 0.303747s \n",
      "Epoch:   4984 loss= 0.559102 acc= 0.927965 cost_time= 0.309419s \n",
      "Epoch:   4985 loss= 0.998665 acc= 0.842140 cost_time= 0.308040s \n",
      "Epoch:   4986 loss= 0.218390 acc= 0.957038 cost_time= 0.313893s \n",
      "Epoch:   4987 loss= 0.407066 acc= 0.899765 cost_time= 0.323365s \n",
      "Epoch:   4988 loss= 0.300893 acc= 0.938334 cost_time= 0.309836s \n",
      "Epoch:   4989 loss= 0.170478 acc= 0.951372 cost_time= 0.311410s \n",
      "Epoch:   4990 loss= 0.622823 acc= 0.898329 cost_time= 0.312788s \n",
      "Epoch:   4991 loss= 0.218386 acc= 0.952723 cost_time= 0.310566s \n",
      "Epoch:   4992 loss= 0.189577 acc= 0.957455 cost_time= 0.313570s \n",
      "Epoch:   4993 loss= 0.222064 acc= 0.952281 cost_time= 0.311436s \n",
      "Epoch:   4994 loss= 0.230956 acc= 0.955604 cost_time= 0.332269s \n",
      "Epoch:   4995 loss= 0.301321 acc= 0.943608 cost_time= 0.311536s \n",
      "Epoch:   4996 loss= 0.634649 acc= 0.919651 cost_time= 0.321410s \n",
      "Epoch:   4997 loss= 0.253120 acc= 0.949115 cost_time= 0.314928s \n",
      "Epoch:   4998 loss= 0.184307 acc= 0.969955 cost_time= 0.316334s \n",
      "Epoch:   4999 loss= 0.208154 acc= 0.953366 cost_time= 0.313074s \n"
     ]
    }
   ],
   "source": [
    "with sess.as_default():\n",
    "    # sess init or restore\n",
    "    if weights_path is None:\n",
    "        log.info('Training from scratch')\n",
    "        init = tf.global_variables_initializer()\n",
    "        sess.run(init)\n",
    "\n",
    "        # read vgg model\n",
    "        pretrained_weights = np.load('./data/vgg16.npy', encoding='latin1').item()\n",
    "        for vv in tf.trainable_variables():\n",
    "            weights_key = vv.name.split('/')[-3]\n",
    "            try:\n",
    "                weights = pretrained_weights[weights_key][0]\n",
    "                _op = tf.assign(vv, weights)\n",
    "                sess.run(_op)\n",
    "            except Exception as e:\n",
    "                continue\n",
    "    else:\n",
    "        log.info('Restore model from last model checkpoint {:s}'.format(weights_path))\n",
    "        # restore weights\n",
    "        saver.restore(sess=sess, save_path=weights_path)\n",
    "\n",
    "    # epoch loop\n",
    "    for epoch in range(CFG.TRAIN.EPOCHS):\n",
    "\n",
    "        # Use training data for optimization\n",
    "        for _ in range(CFG.TRAIN.NOOFBATCHES):\n",
    "            with tf.device('/cpu:0'):\n",
    "                gt_imgs, binary_gt_labels = train_dataset.next_batch(CFG.TRAIN.BATCH_SIZE)\n",
    "                gt_imgs, binary_gt_labels = resize_training_data(gt_imgs, binary_gt_labels)\n",
    "\n",
    "            sess.run(optimizer, feed_dict={input_tensor:gt_imgs, \n",
    "                                           binary_label:binary_gt_labels, \n",
    "                                           phase:'train'})\n",
    "\n",
    "        # Validate after every epoch\n",
    "        t_start = time.time()\n",
    "        with tf.device('/cpu:0'):\n",
    "            gt_imgs, binary_gt_labels = train_dataset.next_batch(CFG.TRAIN.BATCH_SIZE)\n",
    "            gt_imgs, binary_gt_labels = resize_training_data(gt_imgs, binary_gt_labels)\n",
    "\n",
    "        train_loss, train_accuracy, train_img, train_summary = \\\n",
    "            sess.run([loss, accuracy, out_logits_out, merged_summary_op],\n",
    "                     feed_dict={input_tensor: gt_imgs,\n",
    "                                binary_label: binary_gt_labels,\n",
    "                                phase: 'train'})\n",
    "\n",
    "        # time\n",
    "        cost_time = time.time() - t_start\n",
    "        train_cost_time_mean.append(cost_time)\n",
    "        \n",
    "        # summary\n",
    "        summary_writer.add_summary(summary=train_summary, global_step=epoch)\n",
    "\n",
    "        # history\n",
    "        if epoch*CFG.TRAIN.NOOFBATCHES > 10:\n",
    "            history.append([train_loss, train_accuracy])\n",
    "\n",
    "        # progress\n",
    "        if epoch*CFG.TRAIN.NOOFBATCHES % 10 == 0:\n",
    "            print('Epoch: {:6d} loss= {:6f} acc= {:6f} cost_time= {:5f}s '.\n",
    "                  format(epoch, train_loss, train_accuracy, np.mean(train_cost_time_mean)))\n",
    "            train_cost_time_mean.clear()\n",
    "\n",
    "        # output image\n",
    "        if epoch*CFG.TRAIN.NOOFBATCHES % 100 == 0:\n",
    "            binary_seg_image_3ch = np.array([[[0]*3]*__C.TRAIN.IMG_WIDTH]*__C.TRAIN.IMG_HEIGHT, np.float64)\n",
    "            binary_seg_image_3ch[:, :, 0] = 0\n",
    "            binary_seg_image_3ch[:, :, 1] = 0\n",
    "            binary_seg_image_3ch[:, :, 2] = train_img[0][:, :]*255\n",
    "            image = gt_imgs[0] + VGG_MEAN\n",
    "            image_field2 = cv2.addWeighted(image, 1.0, binary_seg_image_3ch, 1.0, 0.0)\n",
    "            path = image_save_path + '/image_{:d}.png'.format(epoch)\n",
    "            cv2.imwrite(path, image_field2)\n",
    "\n",
    "        # store model\n",
    "        if epoch*CFG.TRAIN.NOOFBATCHES % 2000 == 0:\n",
    "            saver.save(sess=sess, save_path=model_save_path, global_step=epoch)\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = np.array(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"tab10\")\n",
    "\n",
    "fig = plt.figure(figsize=(15,15),dpi=100)\n",
    "ax1 = fig.add_subplot(2,1,1, facecolor='w')\n",
    "ax1.plot(np.arange(1, history.shape[0] + 1), history[:, 0], label='loss')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(np.arange(1, history.shape[0] + 1), history[:, 1], label='accuracy', color=cmap(1))\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax2.legend(loc=\"upper right\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}