vgg

1 files changed, 887 insertions, 0 deletions

diff --git a/dl/tutorials/04_vgg_on_cifar10.ipynb b/dl/tutorials/04_vgg_on_cifar10.ipynb
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@@ -0,0 +1,887 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "36600735",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:07:27.625977Z",
+     "start_time": "2023-02-21T16:07:27.623719Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch import nn\n",
+    "import torchvision\n",
+    "from torchvision import models\n",
+    "from torchvision import datasets, transforms\n",
+    "from datetime import datetime\n",
+    "from utils import get_mean_and_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "daeda546",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:01:51.590225Z",
+     "start_time": "2023-02-21T16:01:51.562576Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.10.2\n",
+      "0.11.3\n",
+      "True\n",
+      "NVIDIA A10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(torch.__version__)\n",
+    "print(torchvision.__version__)\n",
+    "print(torch.cuda.is_available())\n",
+    "print(torch.cuda.get_device_name())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df51273a",
+   "metadata": {},
+   "source": [
+    "## vgg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15ffacfc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:02:34.019695Z",
+     "start_time": "2023-02-21T16:02:33.088531Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vgg = models.vgg16(pretrained=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7291eaa0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:02:38.446266Z",
+     "start_time": "2023-02-21T16:02:38.440109Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VGG(\n",
+       "  (features): Sequential(\n",
+       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (3): ReLU(inplace=True)\n",
+       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (6): ReLU(inplace=True)\n",
+       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (8): ReLU(inplace=True)\n",
+       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (11): ReLU(inplace=True)\n",
+       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (13): ReLU(inplace=True)\n",
+       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (15): ReLU(inplace=True)\n",
+       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (18): ReLU(inplace=True)\n",
+       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (20): ReLU(inplace=True)\n",
+       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (22): ReLU(inplace=True)\n",
+       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (25): ReLU(inplace=True)\n",
+       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (27): ReLU(inplace=True)\n",
+       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (29): ReLU(inplace=True)\n",
+       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
+       "  (classifier): Sequential(\n",
+       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Dropout(p=0.5, inplace=False)\n",
+       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
+       "    (4): ReLU(inplace=True)\n",
+       "    (5): Dropout(p=0.5, inplace=False)\n",
+       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vgg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5353f3f1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:03:11.962435Z",
+     "start_time": "2023-02-21T16:03:11.960524Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b2120b07",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:03:30.107291Z",
+     "start_time": "2023-02-21T16:03:29.845682Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1         [-1, 64, 224, 224]           1,792\n",
+      "              ReLU-2         [-1, 64, 224, 224]               0\n",
+      "            Conv2d-3         [-1, 64, 224, 224]          36,928\n",
+      "              ReLU-4         [-1, 64, 224, 224]               0\n",
+      "         MaxPool2d-5         [-1, 64, 112, 112]               0\n",
+      "            Conv2d-6        [-1, 128, 112, 112]          73,856\n",
+      "              ReLU-7        [-1, 128, 112, 112]               0\n",
+      "            Conv2d-8        [-1, 128, 112, 112]         147,584\n",
+      "              ReLU-9        [-1, 128, 112, 112]               0\n",
+      "        MaxPool2d-10          [-1, 128, 56, 56]               0\n",
+      "           Conv2d-11          [-1, 256, 56, 56]         295,168\n",
+      "             ReLU-12          [-1, 256, 56, 56]               0\n",
+      "           Conv2d-13          [-1, 256, 56, 56]         590,080\n",
+      "             ReLU-14          [-1, 256, 56, 56]               0\n",
+      "           Conv2d-15          [-1, 256, 56, 56]         590,080\n",
+      "             ReLU-16          [-1, 256, 56, 56]               0\n",
+      "        MaxPool2d-17          [-1, 256, 28, 28]               0\n",
+      "           Conv2d-18          [-1, 512, 28, 28]       1,180,160\n",
+      "             ReLU-19          [-1, 512, 28, 28]               0\n",
+      "           Conv2d-20          [-1, 512, 28, 28]       2,359,808\n",
+      "             ReLU-21          [-1, 512, 28, 28]               0\n",
+      "           Conv2d-22          [-1, 512, 28, 28]       2,359,808\n",
+      "             ReLU-23          [-1, 512, 28, 28]               0\n",
+      "        MaxPool2d-24          [-1, 512, 14, 14]               0\n",
+      "           Conv2d-25          [-1, 512, 14, 14]       2,359,808\n",
+      "             ReLU-26          [-1, 512, 14, 14]               0\n",
+      "           Conv2d-27          [-1, 512, 14, 14]       2,359,808\n",
+      "             ReLU-28          [-1, 512, 14, 14]               0\n",
+      "           Conv2d-29          [-1, 512, 14, 14]       2,359,808\n",
+      "             ReLU-30          [-1, 512, 14, 14]               0\n",
+      "        MaxPool2d-31            [-1, 512, 7, 7]               0\n",
+      "AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0\n",
+      "           Linear-33                 [-1, 4096]     102,764,544\n",
+      "             ReLU-34                 [-1, 4096]               0\n",
+      "          Dropout-35                 [-1, 4096]               0\n",
+      "           Linear-36                 [-1, 4096]      16,781,312\n",
+      "             ReLU-37                 [-1, 4096]               0\n",
+      "          Dropout-38                 [-1, 4096]               0\n",
+      "           Linear-39                 [-1, 1000]       4,097,000\n",
+      "================================================================\n",
+      "Total params: 138,357,544\n",
+      "Trainable params: 138,357,544\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.57\n",
+      "Forward/backward pass size (MB): 218.78\n",
+      "Params size (MB): 527.79\n",
+      "Estimated Total Size (MB): 747.15\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary(vgg, input_size=(3, 224, 224), device='cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "88f4b5b0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:05:36.359811Z",
+     "start_time": "2023-02-21T16:05:36.355617Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VGG(\n",
+       "  (features): Sequential(\n",
+       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (3): ReLU(inplace=True)\n",
+       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (6): ReLU(inplace=True)\n",
+       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (8): ReLU(inplace=True)\n",
+       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (11): ReLU(inplace=True)\n",
+       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (13): ReLU(inplace=True)\n",
+       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (15): ReLU(inplace=True)\n",
+       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (18): ReLU(inplace=True)\n",
+       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (20): ReLU(inplace=True)\n",
+       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (22): ReLU(inplace=True)\n",
+       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (25): ReLU(inplace=True)\n",
+       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (27): ReLU(inplace=True)\n",
+       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+       "    (29): ReLU(inplace=True)\n",
+       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
+       "  (classifier): Sequential(\n",
+       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
+       "    (1): ReLU(inplace=True)\n",
+       "    (2): Dropout(p=0.5, inplace=False)\n",
+       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
+       "    (4): ReLU(inplace=True)\n",
+       "    (5): Dropout(p=0.5, inplace=False)\n",
+       "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "in_features = vgg.classifier[6].in_features\n",
+    "vgg.classifier[6] = nn.Linear(in_features, 10)\n",
+    "vgg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "60fb0bd4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:05:57.728656Z",
+     "start_time": "2023-02-21T16:05:57.577214Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1         [-1, 64, 224, 224]           1,792\n",
+      "              ReLU-2         [-1, 64, 224, 224]               0\n",
+      "            Conv2d-3         [-1, 64, 224, 224]          36,928\n",
+      "              ReLU-4         [-1, 64, 224, 224]               0\n",
+      "         MaxPool2d-5         [-1, 64, 112, 112]               0\n",
+      "            Conv2d-6        [-1, 128, 112, 112]          73,856\n",
+      "              ReLU-7        [-1, 128, 112, 112]               0\n",
+      "            Conv2d-8        [-1, 128, 112, 112]         147,584\n",
+      "              ReLU-9        [-1, 128, 112, 112]               0\n",
+      "        MaxPool2d-10          [-1, 128, 56, 56]               0\n",
+      "           Conv2d-11          [-1, 256, 56, 56]         295,168\n",
+      "             ReLU-12          [-1, 256, 56, 56]               0\n",
+      "           Conv2d-13          [-1, 256, 56, 56]         590,080\n",
+      "             ReLU-14          [-1, 256, 56, 56]               0\n",
+      "           Conv2d-15          [-1, 256, 56, 56]         590,080\n",
+      "             ReLU-16          [-1, 256, 56, 56]               0\n",
+      "        MaxPool2d-17          [-1, 256, 28, 28]               0\n",
+      "           Conv2d-18          [-1, 512, 28, 28]       1,180,160\n",
+      "             ReLU-19          [-1, 512, 28, 28]               0\n",
+      "           Conv2d-20          [-1, 512, 28, 28]       2,359,808\n",
+      "             ReLU-21          [-1, 512, 28, 28]               0\n",
+      "           Conv2d-22          [-1, 512, 28, 28]       2,359,808\n",
+      "             ReLU-23          [-1, 512, 28, 28]               0\n",
+      "        MaxPool2d-24          [-1, 512, 14, 14]               0\n",
+      "           Conv2d-25          [-1, 512, 14, 14]       2,359,808\n",
+      "             ReLU-26          [-1, 512, 14, 14]               0\n",
+      "           Conv2d-27          [-1, 512, 14, 14]       2,359,808\n",
+      "             ReLU-28          [-1, 512, 14, 14]               0\n",
+      "           Conv2d-29          [-1, 512, 14, 14]       2,359,808\n",
+      "             ReLU-30          [-1, 512, 14, 14]               0\n",
+      "        MaxPool2d-31            [-1, 512, 7, 7]               0\n",
+      "AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0\n",
+      "           Linear-33                 [-1, 4096]     102,764,544\n",
+      "             ReLU-34                 [-1, 4096]               0\n",
+      "          Dropout-35                 [-1, 4096]               0\n",
+      "           Linear-36                 [-1, 4096]      16,781,312\n",
+      "             ReLU-37                 [-1, 4096]               0\n",
+      "          Dropout-38                 [-1, 4096]               0\n",
+      "           Linear-39                   [-1, 10]          40,970\n",
+      "================================================================\n",
+      "Total params: 134,301,514\n",
+      "Trainable params: 134,301,514\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.57\n",
+      "Forward/backward pass size (MB): 218.77\n",
+      "Params size (MB): 512.32\n",
+      "Estimated Total Size (MB): 731.67\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary(vgg, input_size=(3, 224, 224), device='cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7333d83",
+   "metadata": {},
+   "source": [
+    "## parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8d31ddee",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:06:28.035219Z",
+     "start_time": "2023-02-21T16:06:28.032859Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# dataset\n",
+    "# input_shape = 32\n",
+    "num_classes = 10\n",
+    "\n",
+    "# hyper \n",
+    "batch_size = 64\n",
+    "num_epochs = 5\n",
+    "learning_rate = 1e-3\n",
+    "\n",
+    "# gpu\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "72dcd7cf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:06:30.620769Z",
+     "start_time": "2023-02-21T16:06:30.618149Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "856f2db1",
+   "metadata": {},
+   "source": [
+    "## dataset 与 dataloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "421d5a1d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:07:08.617211Z",
+     "start_time": "2023-02-21T16:07:07.313136Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_dataset = datasets.CIFAR10(root='../data/', \n",
+    "                               download=True, \n",
+    "                               train=True, \n",
+    "                               transform=transforms.ToTensor())\n",
+    "test_dataset = datasets.CIFAR10(root='../data/', \n",
+    "                               download=True, \n",
+    "                               train=False, \n",
+    "                               transform=transforms.ToTensor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1da83915",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:08:15.650602Z",
+     "start_time": "2023-02-21T16:07:37.819122Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==> Computing mean and std..\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50000/50000 [00:37<00:00, 1321.94it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([0.4914, 0.4822, 0.4465]), tensor([0.2023, 0.1994, 0.2010]))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_mean_and_std(train_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f2379f10",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:09:30.085725Z",
+     "start_time": "2023-02-21T16:09:30.083236Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transform = transforms.Compose([\n",
+    "    transforms.Resize(size=(224, 224)),\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "00a568cd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:09:51.421012Z",
+     "start_time": "2023-02-21T16:09:50.112455Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_dataset = datasets.CIFAR10(root='../data/', \n",
+    "                               download=True, \n",
+    "                               train=True, \n",
+    "                               transform=transform)\n",
+    "test_dataset = datasets.CIFAR10(root='../data/', \n",
+    "                               download=True, \n",
+    "                               train=False, \n",
+    "                               transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c656663b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:09:53.259344Z",
+     "start_time": "2023-02-21T16:09:53.256872Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
+    "                                               shuffle=True, \n",
+    "                                               batch_size=batch_size)\n",
+    "test_dataloader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
+    "                                               shuffle=False, \n",
+    "                                               batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f77ea73d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:09:55.520559Z",
+     "start_time": "2023-02-21T16:09:55.462862Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "images, labels = next(iter(train_dataloader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d03b9505",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:09:56.669215Z",
+     "start_time": "2023-02-21T16:09:56.666615Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([64, 3, 224, 224])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# batch_size, channels, h, w\n",
+    "images.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b8c4b4",
+   "metadata": {},
+   "source": [
+    "## model arch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d69e7f3e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:10:48.937581Z",
+     "start_time": "2023-02-21T16:10:44.764451Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vgg = models.vgg16(pretrained=True)\n",
+    "in_features = vgg.classifier[6].in_features\n",
+    "vgg.classifier[6] = nn.Linear(in_features, 10)\n",
+    "vgg = vgg.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "872d454c",
+   "metadata": {},
+   "source": [
+    "## model train/fine-tune"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3e0babe0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:13:24.682178Z",
+     "start_time": "2023-02-21T16:13:24.679390Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "criterion = nn.CrossEntropyLoss()\n",
+    "# optimzier = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
+    "optimizer = torch.optim.SGD(vgg.parameters(), lr = learning_rate, momentum=0.9,weight_decay=5e-4)\n",
+    "total_batch = len(train_dataloader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "636c01fb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:20:46.902487Z",
+     "start_time": "2023-02-21T16:13:25.728051Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-02-22 00:13:56.818252, 1/5, 100/782: 0.4080, acc: 0.859375\n",
+      "2023-02-22 00:14:28.367012, 1/5, 200/782: 0.4214, acc: 0.90625\n",
+      "2023-02-22 00:15:00.193995, 1/5, 300/782: 0.5746, acc: 0.828125\n",
+      "2023-02-22 00:15:32.204011, 1/5, 400/782: 0.3419, acc: 0.875\n",
+      "2023-02-22 00:16:04.007379, 1/5, 500/782: 0.3597, acc: 0.859375\n",
+      "2023-02-22 00:16:35.717492, 1/5, 600/782: 0.4721, acc: 0.796875\n",
+      "2023-02-22 00:17:07.485007, 1/5, 700/782: 0.1224, acc: 0.953125\n",
+      "2023-02-22 00:18:05.079364, 2/5, 100/782: 0.1760, acc: 0.921875\n",
+      "2023-02-22 00:18:36.859823, 2/5, 200/782: 0.1521, acc: 0.921875\n",
+      "2023-02-22 00:19:08.638372, 2/5, 300/782: 0.2511, acc: 0.90625\n",
+      "2023-02-22 00:19:40.422421, 2/5, 400/782: 0.1399, acc: 0.9375\n",
+      "2023-02-22 00:20:12.218806, 2/5, 500/782: 0.1915, acc: 0.90625\n",
+      "2023-02-22 00:20:44.003433, 2/5, 600/782: 0.1382, acc: 0.9375\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-9a44dc355816>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;31m# 标准的处理，用 validate data；这个过程是监督训练过程\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mn_corrects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_corrects\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(num_epochs):\n",
+    "    for batch_idx, (images, labels) in enumerate(train_dataloader):\n",
+    "        images = images.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "        \n",
+    "        # forward\n",
+    "        out = vgg(images)\n",
+    "        loss = criterion(out, labels)\n",
+    "        \n",
+    "        # 标准的处理，用 validate data；这个过程是监督训练过程，用于 early stop\n",
+    "        n_corrects = (out.argmax(axis=1) == labels).sum().item()\n",
+    "        acc = n_corrects/labels.size(0)\n",
+    "        \n",
+    "        # backward\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()   # 更细 模型参数\n",
+    "        \n",
+    "        if (batch_idx+1) % 100 == 0:\n",
+    "            print(f'{datetime.now()}, {epoch+1}/{num_epochs}, {batch_idx+1}/{total_batch}: {loss.item():.4f}, acc: {acc}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e7078dd",
+   "metadata": {},
+   "source": [
+    "## model evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ef80d16f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:21:06.582989Z",
+     "start_time": "2023-02-21T16:20:55.012423Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-27-a604fa16a1e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcorrect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_dataloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torchvision/datasets/cifar.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_transform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/torch/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1095\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1096\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1097\u001b[0;31m         \u001b[0mforward_call\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1098\u001b[0m         \u001b[0;31m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m         \u001b[0;31m# this function, and just call forward.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "for images, labels in test_dataloader:\n",
+    "    images = images.to(device)\n",
+    "    labels = labels.to(device)\n",
+    "    out = vgg(images)\n",
+    "    preds = torch.argmax(out, dim=1)\n",
+    "    \n",
+    "    total += images.size(0)\n",
+    "    correct += (preds == labels).sum().item()\n",
+    "print(f'{correct}/{total}={correct/total}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96272f61",
+   "metadata": {},
+   "source": [
+    "## model save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e68f24f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-21T16:21:06.587517Z",
+     "start_time": "2023-02-21T16:21:03.590Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "torch.save(vgg.state_dict(), 'cnn_cifar.ckpt')"
+   ]
+  }
+ ],
+ "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": "3.6.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}