04，kerasmodel_tensorboard_demo.ipynb · BLZ/torchkeras_1

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04，kerasmodel_tensorboard_demo.ipynb 111.96 KB

{
 "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": 6,
   "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": null,
   "id": "580741c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad17716f",
   "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): 0.000069\n",
      "Forward/backward pass size (MB): 0.263016\n",
      "Params size (MB): 0.205971\n",
      "Estimated Total Size (MB): 0.469055\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "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",
    "    )\n",
    "\n",
    "from torchkeras import summary\n",
    "summary(model,input_data=features);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fce5463e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e98c79f5",
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   "source": [
    "### 3, train the model"
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  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54bcc553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n"
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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
       "\n",
       "    /* optional */\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #000000;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='15' class='' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [02:40]\n",
       "      <br>\n",
       "      ████████████████████100.00% [79/79] [val_loss=0.04731,val_acc=0.98620]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
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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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dd04566-9d85-49f5-ae7f-eb921af635a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad03b2a6-1831-4259-a6cd-f1c9e384b88e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 9,
   "id": "9d48905d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Known TensorBoard instances:\n",
      "  - port 6006: logdir ./runs (started 1:05:47 ago; pid 96887)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 96887), started 1:05:47 ago. (Use '!kill 96887' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-eca99ecf6fc3d70\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-eca99ecf6fc3d70\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

一键复制编辑原始数据按行查看历史

提交于 2023-07-15 16:09 . update 3.9.1

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