6-2,训练模型的3种方法.ipynb

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   "source": [
    "# 6-2,训练模型的3种方法\n",
    "\n",
    "Pytorch通常需要用户编写自定义训练循环，训练循环的代码风格因人而异。\n",
    "\n",
    "有3类典型的训练循环代码风格：脚本形式训练循环，函数形式训练循环，类形式训练循环。\n",
    "\n",
    "下面以minist数据集的多分类模型的训练为例，演示这3种训练模型的风格。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24b101ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.__version__ =  2.4.0\n",
      "torchkeras.__version__ =  4.0.0\n"
     ]
    }
   ],
   "source": [
    "import torch \n",
    "import torchkeras\n",
    "print(\"torch.__version__ = \", torch.__version__)\n",
    "print(\"torchkeras.__version__ = \", torchkeras.__version__) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b143f46a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "52642b45",
   "metadata": {},
   "source": [
    "### 〇，准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04d2d65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "from torch import nn \n",
    "\n",
    "import torchvision \n",
    "from torchvision import transforms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fa1c7d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "ds_train = torchvision.datasets.MNIST(root=\"./data/mnist/\",train=True,download=True,transform=transform)\n",
    "ds_val = torchvision.datasets.MNIST(root=\"./data/mnist/\",train=False,download=True,transform=transform)\n",
    "\n",
    "dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=4)\n",
    "dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=128, shuffle=False, num_workers=4)\n",
    "\n",
    "print(len(ds_train))\n",
    "print(len(ds_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7130d4e",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
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     },
     "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 = torch.squeeze(img)\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": "code",
   "execution_count": null,
   "id": "8a3238e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "99c0512e",
   "metadata": {},
   "source": [
    "### 一，脚本风格"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d305aa4",
   "metadata": {},
   "source": [
    "脚本风格的训练循环非常常见。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dad7971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (dropout): Dropout2d(p=0.1, inplace=False)\n",
      "  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (linear1): Linear(in_features=64, out_features=32, bias=True)\n",
      "  (relu): ReLU()\n",
      "  (linear2): Linear(in_features=32, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "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",
    "\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f99414",
   "metadata": {},
   "source": [
    "```\n",
    "Sequential(\n",
    "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
    "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n",
    "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
    "  (dropout): Dropout2d(p=0.1, inplace=False)\n",
    "  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n",
    "  (flatten): Flatten()\n",
    "  (linear1): Linear(in_features=64, out_features=32, bias=True)\n",
    "  (relu): ReLU()\n",
    "  (linear2): Linear(in_features=32, out_features=10, bias=True)\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "417a87bd-8e15-4872-a5c2-f6d41f2247ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime \n",
    "from tqdm import tqdm \n",
    "\n",
    "import torch\n",
    "from torch import nn \n",
    "from copy import deepcopy\n",
    "from torchmetrics import Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc9fb1ae-2e9e-45ed-a12b-007068e2fd33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:14:14\n",
      "Epoch 1 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:13<00:00, 34.72it/s, train_acc=0.903, train_loss=0.296]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 38.04it/s, val_acc=0.975, val_loss=0.081]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:14:29\n",
      "Epoch 2 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "<<<<<< reach best val_acc : 0.9753999710083008 >>>>>>\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:13<00:00, 34.79it/s, train_acc=0.968, train_loss=0.104]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 37.08it/s, val_acc=0.976, val_loss=0.0783]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:14:45\n",
      "Epoch 3 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "<<<<<< reach best val_acc : 0.9758999943733215 >>>>>>\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:13<00:00, 33.96it/s, train_acc=0.971, train_loss=0.0972]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 35.95it/s, val_acc=0.981, val_loss=0.0627]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:15:01\n",
      "Epoch 4 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "<<<<<< reach best val_acc : 0.9807999730110168 >>>>>>\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 33.13it/s, train_acc=0.976, train_loss=0.0811]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 36.22it/s, val_acc=0.978, val_loss=0.0821]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:15:18\n",
      "Epoch 5 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 33.21it/s, train_acc=0.975, train_loss=0.085]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 35.47it/s, val_acc=0.972, val_loss=0.0949]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:15:34\n",
      "Epoch 6 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 32.39it/s, train_acc=0.974, train_loss=0.0901]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 35.15it/s, val_acc=0.98, val_loss=0.0607]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:15:51\n",
      "Epoch 7 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 32.24it/s, train_acc=0.973, train_loss=0.0894]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 34.23it/s, val_acc=0.979, val_loss=0.0665]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:16:08\n",
      "Epoch 8 / 20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 31.41it/s, train_acc=0.973, train_loss=0.0915]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.71it/s, val_acc=0.975, val_loss=0.084]\n",
      "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n"
     ]
    }
   ],
   "source": [
    "def printlog(info):\n",
    "    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
    "    print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n",
    "    print(str(info)+\"\\n\")\n",
    "    \n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01)   \n",
    "metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)}\n",
    "\n",
    "epochs = 20 \n",
    "ckpt_path='checkpoint'\n",
    "\n",
    "#early_stopping相关设置\n",
    "monitor=\"val_acc\"\n",
    "patience=5\n",
    "mode=\"max\"\n",
    "\n",
    "history = {}\n",
    "\n",
    "for epoch in range(1, epochs+1):\n",
    "    printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n",
    "\n",
    "    # 1，train -------------------------------------------------  \n",
    "    net.train()\n",
    "    \n",
    "    total_loss,step = 0,0\n",
    "    \n",
    "    loop = tqdm(enumerate(dl_train), total =len(dl_train))\n",
    "    train_metrics_dict = deepcopy(metrics_dict) \n",
    "    \n",
    "    for i, batch in loop: \n",
    "        \n",
    "        features,labels = batch\n",
    "        #forward\n",
    "        preds = net(features)\n",
    "        loss = loss_fn(preds,labels)\n",
    "        \n",
    "        #backward\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "            \n",
    "        #metrics\n",
    "        step_metrics = {\"train_\"+name:metric_fn(preds, labels).item() \n",
    "                        for name,metric_fn in train_metrics_dict.items()}\n",
    "        \n",
    "        step_log = dict({\"train_loss\":loss.item()},**step_metrics)\n",
    "\n",
    "        total_loss += loss.item()\n",
    "        \n",
    "        step+=1\n",
    "        if i!=len(dl_train)-1:\n",
    "            loop.set_postfix(**step_log)\n",
    "        else:\n",
    "            epoch_loss = total_loss/step\n",
    "            epoch_metrics = {\"train_\"+name:metric_fn.compute().item() \n",
    "                             for name,metric_fn in train_metrics_dict.items()}\n",
    "            epoch_log = dict({\"train_loss\":epoch_loss},**epoch_metrics)\n",
    "            loop.set_postfix(**epoch_log)\n",
    "\n",
    "            for name,metric_fn in train_metrics_dict.items():\n",
    "                metric_fn.reset()\n",
    "                \n",
    "    for name, metric in epoch_log.items():\n",
    "        history[name] = history.get(name, []) + [metric]\n",
    "        \n",
    "\n",
    "    # 2，validate -------------------------------------------------\n",
    "    net.eval()\n",
    "    \n",
    "    total_loss,step = 0,0\n",
    "    loop = tqdm(enumerate(dl_val), total =len(dl_val))\n",
    "    \n",
    "    val_metrics_dict = deepcopy(metrics_dict) \n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for i, batch in loop: \n",
    "\n",
    "            features,labels = batch\n",
    "            \n",
    "            #forward\n",
    "            preds = net(features)\n",
    "            loss = loss_fn(preds,labels)\n",
    "\n",
    "            #metrics\n",
    "            step_metrics = {\"val_\"+name:metric_fn(preds, labels).item() \n",
    "                            for name,metric_fn in val_metrics_dict.items()}\n",
    "\n",
    "            step_log = dict({\"val_loss\":loss.item()},**step_metrics)\n",
    "\n",
    "            total_loss += loss.item()\n",
    "            step+=1\n",
    "            if i!=len(dl_val)-1:\n",
    "                loop.set_postfix(**step_log)\n",
    "            else:\n",
    "                epoch_loss = (total_loss/step)\n",
    "                epoch_metrics = {\"val_\"+name:metric_fn.compute().item() \n",
    "                                 for name,metric_fn in val_metrics_dict.items()}\n",
    "                epoch_log = dict({\"val_loss\":epoch_loss},**epoch_metrics)\n",
    "                loop.set_postfix(**epoch_log)\n",
    "\n",
    "                for name,metric_fn in val_metrics_dict.items():\n",
    "                    metric_fn.reset()\n",
    "                    \n",
    "    epoch_log[\"epoch\"] = epoch           \n",
    "    for name, metric in epoch_log.items():\n",
    "        history[name] = history.get(name, []) + [metric]\n",
    "\n",
    "    # 3，early-stopping -------------------------------------------------\n",
    "    arr_scores = history[monitor]\n",
    "    best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n",
    "    if best_score_idx==len(arr_scores)-1:\n",
    "        torch.save(net.state_dict(),ckpt_path)\n",
    "        print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n",
    "             arr_scores[best_score_idx]),file=sys.stderr)\n",
    "    if len(arr_scores)-best_score_idx>patience:\n",
    "        print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n",
    "            monitor,patience),file=sys.stderr)\n",
    "        break \n",
    "    net.load_state_dict(torch.load(ckpt_path,weights_only=True))\n",
    "    \n",
    "dfhistory = pd.DataFrame(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2605b75e-15f9-4a4a-a31f-00a6a5d0b228",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c726168a",
   "metadata": {},
   "source": [
    "### 二，函数风格"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48817db4",
   "metadata": {},
   "source": [
    "该风格在脚本形式上做了进一步的函数封装。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a1a41d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (layers): ModuleList(\n",
      "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Dropout2d(p=0.1, inplace=False)\n",
      "    (5): AdaptiveMaxPool2d(output_size=(1, 1))\n",
      "    (6): Flatten(start_dim=1, end_dim=-1)\n",
      "    (7): Linear(in_features=64, out_features=32, bias=True)\n",
      "    (8): ReLU()\n",
      "    (9): Linear(in_features=32, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.layers = nn.ModuleList([\n",
    "            nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),\n",
    "            nn.MaxPool2d(kernel_size = 2,stride = 2),\n",
    "            nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),\n",
    "            nn.MaxPool2d(kernel_size = 2,stride = 2),\n",
    "            nn.Dropout2d(p = 0.1),\n",
    "            nn.AdaptiveMaxPool2d((1,1)),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64,32),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(32,10)]\n",
    "        )\n",
    "    def forward(self,x):\n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "        return x\n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840dec1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c97a606",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime \n",
    "from tqdm import tqdm \n",
    "\n",
    "import torch\n",
    "from torch import nn \n",
    "from copy import deepcopy\n",
    "\n",
    "def printlog(info):\n",
    "    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
    "    print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n",
    "    print(str(info)+\"\\n\")\n",
    "\n",
    "class StepRunner:\n",
    "    def __init__(self, net, loss_fn,\n",
    "                 stage = \"train\", metrics_dict = None, \n",
    "                 optimizer = None\n",
    "                 ):\n",
    "        self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n",
    "        self.optimizer = optimizer\n",
    "            \n",
    "    def step(self, features, labels):\n",
    "        #loss\n",
    "        preds = self.net(features)\n",
    "        loss = self.loss_fn(preds,labels)\n",
    "        \n",
    "        #backward()\n",
    "        if self.optimizer is not None and self.stage==\"train\": \n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.optimizer.zero_grad()\n",
    "            \n",
    "        #metrics\n",
    "        step_metrics = {self.stage+\"_\"+name:metric_fn(preds, labels).item() \n",
    "                        for name,metric_fn in self.metrics_dict.items()}\n",
    "        return loss.item(),step_metrics\n",
    "    \n",
    "    def train_step(self,features,labels):\n",
    "        self.net.train() #训练模式, dropout层发生作用\n",
    "        return self.step(features,labels)\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def eval_step(self,features,labels):\n",
    "        self.net.eval() #预测模式, dropout层不发生作用\n",
    "        return self.step(features,labels)\n",
    "    \n",
    "    def __call__(self,features,labels):\n",
    "        if self.stage==\"train\":\n",
    "            return self.train_step(features,labels) \n",
    "        else:\n",
    "            return self.eval_step(features,labels)\n",
    "        \n",
    "class EpochRunner:\n",
    "    def __init__(self,steprunner):\n",
    "        self.steprunner = steprunner\n",
    "        self.stage = steprunner.stage\n",
    "        \n",
    "    def __call__(self,dataloader):\n",
    "        total_loss,step = 0,0\n",
    "        loop = tqdm(enumerate(dataloader), total =len(dataloader))\n",
    "        for i, batch in loop: \n",
    "            loss, step_metrics = self.steprunner(*batch)\n",
    "            step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n",
    "            total_loss += loss\n",
    "            step+=1\n",
    "            if i!=len(dataloader)-1:\n",
    "                loop.set_postfix(**step_log)\n",
    "            else:\n",
    "                epoch_loss = total_loss/step\n",
    "                epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n",
    "                                 for name,metric_fn in self.steprunner.metrics_dict.items()}\n",
    "                epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n",
    "                loop.set_postfix(**epoch_log)\n",
    "\n",
    "                for name,metric_fn in self.steprunner.metrics_dict.items():\n",
    "                    metric_fn.reset()\n",
    "        return epoch_log\n",
    "\n",
    "\n",
    "def train_model(net, optimizer, loss_fn, metrics_dict, \n",
    "                train_data, val_data=None, \n",
    "                epochs=10, ckpt_path='checkpoint.pt',\n",
    "                patience=5, monitor=\"val_loss\", mode=\"min\"):\n",
    "    \n",
    "    history = {}\n",
    "\n",
    "    for epoch in range(1, epochs+1):\n",
    "        printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n",
    "\n",
    "        # 1，train -------------------------------------------------  \n",
    "        train_step_runner = StepRunner(net = net,stage=\"train\",\n",
    "                loss_fn = loss_fn,metrics_dict=deepcopy(metrics_dict),\n",
    "                optimizer = optimizer)\n",
    "        train_epoch_runner = EpochRunner(train_step_runner)\n",
    "        train_metrics = train_epoch_runner(train_data)\n",
    "\n",
    "        for name, metric in train_metrics.items():\n",
    "            history[name] = history.get(name, []) + [metric]\n",
    "\n",
    "        # 2，validate -------------------------------------------------\n",
    "        if val_data:\n",
    "            val_step_runner = StepRunner(net = net,stage=\"val\",\n",
    "                loss_fn = loss_fn,metrics_dict=deepcopy(metrics_dict))\n",
    "            val_epoch_runner = EpochRunner(val_step_runner)\n",
    "            with torch.no_grad():\n",
    "                val_metrics = val_epoch_runner(val_data)\n",
    "            val_metrics[\"epoch\"] = epoch\n",
    "            for name, metric in val_metrics.items():\n",
    "                history[name] = history.get(name, []) + [metric]\n",
    "\n",
    "        # 3，early-stopping -------------------------------------------------\n",
    "        arr_scores = history[monitor]\n",
    "        best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n",
    "        if best_score_idx==len(arr_scores)-1:\n",
    "            torch.save(net.state_dict(),ckpt_path)\n",
    "            print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n",
    "                 arr_scores[best_score_idx]),file=sys.stderr)\n",
    "        if len(arr_scores)-best_score_idx>patience:\n",
    "            print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n",
    "                monitor,patience),file=sys.stderr)\n",
    "            break \n",
    "        net.load_state_dict(torch.load(ckpt_path,weights_only=True))\n",
    "\n",
    "    return pd.DataFrame(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "feb456ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:17:30\n",
      "Epoch 1 / 10\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:13<00:00, 33.91it/s, train_acc=0.969, train_loss=0.104]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 36.53it/s, val_acc=0.981, val_loss=0.0639]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:17:46\n",
      "Epoch 2 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "<<<<<< reach best val_acc : 0.9805999994277954 >>>>>>\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 31.90it/s, train_acc=0.975, train_loss=0.086]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.94it/s, val_acc=0.977, val_loss=0.0702]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:18:03\n",
      "Epoch 3 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 31.47it/s, train_acc=0.975, train_loss=0.0867]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.53it/s, val_acc=0.977, val_loss=0.0774]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:18:21\n",
      "Epoch 4 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:14<00:00, 31.35it/s, train_acc=0.973, train_loss=0.0912]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.93it/s, val_acc=0.981, val_loss=0.0733]"
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     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================2024-09-11 13:18:38\n",
      "Epoch 5 / 10\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "<<<<<< reach best val_acc : 0.9807000160217285 >>>>>>\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:15<00:00, 30.90it/s, train_acc=0.978, train_loss=0.0739]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.12it/s, val_acc=0.98, val_loss=0.0683]"
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     "text": [
      "\n",
      "================================================================================2024-09-11 13:18:56\n",
      "Epoch 6 / 10\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:15<00:00, 30.97it/s, train_acc=0.976, train_loss=0.0851]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.75it/s, val_acc=0.977, val_loss=0.0848]"
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     "text": [
      "\n",
      "================================================================================2024-09-11 13:19:13\n",
      "Epoch 7 / 10\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:15<00:00, 30.42it/s, train_acc=0.977, train_loss=0.0812]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.16it/s, val_acc=0.98, val_loss=0.0779]\n",
      "<<<<<< val_acc without improvement in 3 epoch, early stopping >>>>>>\n"
     ]
    }
   ],
   "source": [
    "from torchmetrics import Accuracy\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01)   \n",
    "metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)}\n",
    "\n",
    "dfhistory = train_model(net,\n",
    "    optimizer,\n",
    "    loss_fn,\n",
    "    metrics_dict,\n",
    "    train_data = dl_train,\n",
    "    val_data= dl_val,\n",
    "    epochs=10,\n",
    "    patience=3,\n",
    "    monitor=\"val_acc\", \n",
    "    mode=\"max\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bcd9c07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d085b560",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0b68de55",
   "metadata": {},
   "source": [
    "### 三，类风格"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c2c331",
   "metadata": {},
   "source": [
    "此处使用torchkeras.KerasModel高层次API接口中的fit方法训练模型。\n",
    "\n",
    "使用该形式训练模型非常简洁明了。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eab1d853",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (layers): ModuleList(\n",
      "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Dropout2d(p=0.1, inplace=False)\n",
      "    (5): AdaptiveMaxPool2d(output_size=(1, 1))\n",
      "    (6): Flatten(start_dim=1, end_dim=-1)\n",
      "    (7): Linear(in_features=64, out_features=32, bias=True)\n",
      "    (8): ReLU()\n",
      "    (9): Linear(in_features=32, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from torchkeras import KerasModel \n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layers = nn.ModuleList([\n",
    "            nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),\n",
    "            nn.MaxPool2d(kernel_size = 2,stride = 2),\n",
    "            nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),\n",
    "            nn.MaxPool2d(kernel_size = 2,stride = 2),\n",
    "            nn.Dropout2d(p = 0.1),\n",
    "            nn.AdaptiveMaxPool2d((1,1)),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64,32),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(32,10)]\n",
    "        )\n",
    "    def forward(self,x):\n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "        return x\n",
    "    \n",
    "net = Net() \n",
    "\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cf6ae428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n"
     ]
    },
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       "      <td>0.980067</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.061469</td>\n",
       "      <td>0.9828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.066390</td>\n",
       "      <td>0.982067</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.060878</td>\n",
       "      <td>0.9841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.068873</td>\n",
       "      <td>0.981417</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.066541</td>\n",
       "      <td>0.9830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch  train_loss  train_acc    lr  val_loss  val_acc\n",
       "0      1    0.307074   0.901067  0.01  0.121413   0.9619\n",
       "1      2    0.110969   0.967217  0.01  0.081544   0.9764\n",
       "2      3    0.089819   0.973250  0.01  0.072548   0.9778\n",
       "3      4    0.082018   0.976100  0.01  0.061791   0.9807\n",
       "4      5    0.077831   0.977967  0.01  0.066196   0.9825\n",
       "5      6    0.073699   0.978717  0.01  0.092324   0.9797\n",
       "6      7    0.070759   0.980167  0.01  0.073561   0.9811\n",
       "7      8    0.072548   0.980067  0.01  0.061469   0.9828\n",
       "8      9    0.066390   0.982067  0.01  0.060878   0.9841\n",
       "9     10    0.068873   0.981417  0.01  0.066541   0.9830"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchmetrics import Accuracy\n",
    "\n",
    "keras_model = KerasModel(net,\n",
    "                   loss_fn=nn.CrossEntropyLoss(),\n",
    "                   metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)},\n",
    "                   optimizer = torch.optim.Adam(net.parameters(),lr = 0.01)  )\n",
    "\n",
    "keras_model.fit(\n",
    "    train_data = dl_train,\n",
    "    val_data= dl_val,\n",
    "    epochs=10,\n",
    "    patience=3,\n",
    "    monitor=\"val_acc\", \n",
    "    mode=\"max\",\n",
    "    plot=True,\n",
    "    cpu=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d8c747",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "597ea3df",
   "metadata": {},
   "source": [
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    "\n",
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   ]
  }
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