04，kerasmodel_tensorboard_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8068f820",
   "metadata": {},
   "source": [
    "# torchkeras.KerasModel TensorBoard Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84d3f83",
   "metadata": {},
   "source": [
    "You can install torchkeras using pip:\n",
    "`pip install torchkeras`\n",
    "\n",
    "Here is a mnist cnn example using torchkeras.KerasModel  with visualization of tensorboad.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e915b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1920bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import torchkeras #Attention this line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "358f728f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e73ca619",
   "metadata": {},
   "source": [
    "### 1, prepare data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25ab4939",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision \n",
    "from torchvision import transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "ds_train = torchvision.datasets.MNIST(root=\"mnist/\",train=True,download=True,transform=transform)\n",
    "ds_val = torchvision.datasets.MNIST(root=\"mnist/\",train=False,download=True,transform=transform)\n",
    "dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=2)\n",
    "dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=128, shuffle=False, num_workers=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85a45abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 1, 28, 28])\n",
      "torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "for features,labels in dl_train:\n",
    "    break\n",
    "print(features.shape)\n",
    "print(labels.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c484c0b4",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "#查看部分样本\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "plt.figure(figsize=(8,8)) \n",
    "for i in range(9):\n",
    "    img,label = ds_train[i]\n",
    "    img = img.permute(1,2,0)\n",
    "    ax=plt.subplot(3,3,i+1)\n",
    "    ax.imshow(img.numpy())\n",
    "    ax.set_title(\"label = %d\"%label)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([]) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485da125",
   "metadata": {},
   "source": [
    "### 2, create the  model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6174ae8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_net():\n",
    "    net = nn.Sequential()\n",
    "    net.add_module(\"conv1\",nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3))\n",
    "    net.add_module(\"pool1\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"conv2\",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))\n",
    "    net.add_module(\"pool2\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"dropout\",nn.Dropout2d(p = 0.1))\n",
    "    net.add_module(\"adaptive_pool\",nn.AdaptiveMaxPool2d((1,1)))\n",
    "    net.add_module(\"flatten\",nn.Flatten())\n",
    "    net.add_module(\"linear1\",nn.Linear(64,32))\n",
    "    net.add_module(\"relu\",nn.ReLU())\n",
    "    net.add_module(\"linear2\",nn.Linear(32,10))\n",
    "    return net\n",
    "\n",
    "net = create_net()\n",
    "\n",
    "class Accuracy(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.correct = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "        self.total = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "\n",
    "    def forward(self, preds: torch.Tensor, targets: torch.Tensor):\n",
    "        preds = preds.argmax(dim=-1)\n",
    "        m = (preds == targets).sum()\n",
    "        n = targets.shape[0] \n",
    "        self.correct += m \n",
    "        self.total += n\n",
    "        \n",
    "        return m/n\n",
    "\n",
    "    def compute(self):\n",
    "        return self.correct.float() / self.total \n",
    "    \n",
    "    def reset(self):\n",
    "        self.correct -= self.correct\n",
    "        self.total -= self.total\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad17716f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "model = torchkeras.KerasModel(net,\n",
    "      loss_fn = nn.CrossEntropyLoss(),\n",
    "      optimizer= torch.optim.Adam(net.parameters(),lr=0.001),\n",
    "      metrics_dict = {\"acc\":Accuracy()})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "404dec39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Layer (type)                            Output Shape              Param #\n",
      "==========================================================================\n",
      "Conv2d-1                            [-1, 32, 26, 26]                  320\n",
      "MaxPool2d-2                         [-1, 32, 13, 13]                    0\n",
      "Conv2d-3                              [-1, 64, 9, 9]               51,264\n",
      "MaxPool2d-4                           [-1, 64, 4, 4]                    0\n",
      "Dropout2d-5                           [-1, 64, 4, 4]                    0\n",
      "AdaptiveMaxPool2d-6                   [-1, 64, 1, 1]                    0\n",
      "Flatten-7                                   [-1, 64]                    0\n",
      "Linear-8                                    [-1, 32]                2,080\n",
      "ReLU-9                                      [-1, 32]                    0\n",
      "Linear-10                                   [-1, 10]                  330\n",
      "==========================================================================\n",
      "Total params: 53,994\n",
      "Trainable params: 53,994\n",
      "Non-trainable params: 0\n",
      "--------------------------------------------------------------------------\n",
      "Input size (MB): 0.000084\n",
      "Forward/backward pass size (MB): 0.263016\n",
      "Params size (MB): 0.205971\n",
      "Estimated Total Size (MB): 0.469070\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchkeras import summary\n",
    "summary(model,input_data=features);\n"
   ]
  },
  {
   "cell_type": "code",
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    "### 3, train the model"
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     "text": [
      "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n"
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      "text/plain": [
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     },
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     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* background: */\n",
       "    progress::-webkit-progress-bar {background-color: #CDCDCD; width: 100%;}\n",
       "    progress {background-color: #CDCDCD;}\n",
       "\n",
       "    /* value: */\n",
       "    progress::-webkit-progress-value {background-color: #00BFFF  !important;}\n",
       "    progress::-moz-progress-bar {background-color: #00BFFF  !important;}\n",
       "    progress {color: #00BFFF ;}\n",
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       "\n",
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       "      <progress value='15' class='' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100% [15/15] [02:27]\n",
       "      <br>\n",
       "      ████████████████████100.00% [79/79] [val_loss=0.0517, val_acc=0.9850]\n",
       "    </div>\n",
       "    "
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   "source": [
    "# you can also try: from torchkeras.kerascallbacks import WandbCallback\n",
    "from torchkeras.kerascallbacks import TensorBoardCallback\n",
    "dfhistory=model.fit(train_data=dl_train, \n",
    "                    val_data=dl_val, \n",
    "                    epochs=15, \n",
    "                    patience=5, \n",
    "                    monitor=\"val_acc\",mode=\"max\",\n",
    "                    ckpt_path='checkpoint.pt',\n",
    "                    callbacks = [TensorBoardCallback(save_dir='runs',\n",
    "                                model_name='mnist_cnn',log_weight=True,log_weight_freq=5)]\n",
    "                   )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ddc3317",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visual the  prediction\n",
    "device = None\n",
    "for p in model.parameters():\n",
    "    device = p.device\n",
    "    break\n",
    "\n",
    "plt.figure(figsize=(8,8)) \n",
    "for i in range(9):\n",
    "    img,label = ds_train[i]\n",
    "    tensor = img.to(device)\n",
    "    y_pred = torch.argmax(net(tensor[None,...])) \n",
    "    img = img.permute(1,2,0)\n",
    "    ax=plt.subplot(3,3,i+1)\n",
    "    ax.imshow(img.numpy())\n",
    "    ax.set_title(\"y_pred = %d\"%y_pred)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([]) \n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bfe3e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a6c184b2",
   "metadata": {},
   "source": [
    "### 4, Monitor from TensorBoard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30867c63",
   "metadata": {},
   "source": [
    "The TensorBoardCallback  saves logs at the directory  'runs'.\n",
    "\n",
    "We can monitor and analysis training process using TensorBoard now.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e130675",
   "metadata": {},
   "source": [
    "!tensorboard --logdir=\"./runs\" --bind_all --port=6006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d48905d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorboard import notebook\n",
    "notebook.list() \n",
    "notebook.start(\"--logdir ./runs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e532b964",
   "metadata": {},
   "source": [
    "![](./data/tb_acc.png)\n",
    "\n",
    "![](./data/conv_weight.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58552e97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3de487e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a241359",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "926009e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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