cnn on mnist

1 files changed, 590 insertions, 0 deletions

diff --git a/dl/tutorials/02_cnn_on_mnist.ipynb b/dl/tutorials/02_cnn_on_mnist.ipynb
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@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "36600735",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:42:26.334897Z",
+     "start_time": "2023-02-12T07:42:25.748244Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch import nn\n",
+    "from torchvision import datasets, transforms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7333d83",
+   "metadata": {},
+   "source": [
+    "## parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8d31ddee",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:01:39.702046Z",
+     "start_time": "2023-02-12T08:01:39.696278Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# dataset\n",
+    "input_shape = 28\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": 15,
+   "id": "72dcd7cf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:59:43.271511Z",
+     "start_time": "2023-02-12T07:59:43.264504Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "856f2db1",
+   "metadata": {},
+   "source": [
+    "## dataset 与 dataloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "421d5a1d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:42:58.378258Z",
+     "start_time": "2023-02-12T07:42:58.318280Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_dataset = datasets.MNIST(root='../data/', \n",
+    "                               download=True, \n",
+    "                               train=True, \n",
+    "                               transform=transforms.ToTensor())\n",
+    "test_dataset = datasets.MNIST(root='../data/', \n",
+    "                               download=True, \n",
+    "                               train=False, \n",
+    "                               transform=transforms.ToTensor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c656663b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:43:16.544394Z",
+     "start_time": "2023-02-12T07:43:16.538929Z"
+    }
+   },
+   "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": 5,
+   "id": "f77ea73d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:43:18.810041Z",
+     "start_time": "2023-02-12T07:43:18.790215Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "images, labels = next(iter(train_dataloader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d03b9505",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:44:03.036073Z",
+     "start_time": "2023-02-12T07:44:03.029497Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([64, 1, 28, 28])"
+      ]
+     },
+     "execution_count": 7,
+     "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": "markdown",
+   "id": "6a3182ef",
+   "metadata": {},
+   "source": [
+    "- cnn: channel 不断增加，shape 不断减少的过程\n",
+    "    - 最好是 *2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "521ddf96",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:05:01.738399Z",
+     "start_time": "2023-02-12T08:05:01.731637Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class CNN(nn.Module):\n",
+    "    def __init__(self, input_shape, in_channels, num_classes):\n",
+    "        super(CNN, self).__init__()\n",
+    "        # conv2d: (b, 1, 28, 28) => (b, 16, 28, 28)\n",
+    "        # maxpool2d: (b, 16, 28, 28) => (b, 16, 14, 14)\n",
+    "        self.cnn1 = nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=16, \n",
+    "                                            kernel_size=5, padding=2, stride=1), \n",
+    "                                  nn.BatchNorm2d(16), \n",
+    "                                  nn.ReLU(), \n",
+    "                                  nn.MaxPool2d(kernel_size=2, stride=2))\n",
+    "        \n",
+    "        # conv2d: (b, 16, 14, 14) => (b, 32, 14, 14)\n",
+    "        # maxpool2d: (b, 32, 14, 14) => (b, 32, 7, 7)\n",
+    "        self.cnn2 = nn.Sequential(nn.Conv2d(in_channels=16, out_channels=32, \n",
+    "                                            kernel_size=5, padding=2, stride=1), \n",
+    "                                  nn.BatchNorm2d(32), \n",
+    "                                  nn.ReLU(), \n",
+    "                                  nn.MaxPool2d(kernel_size=2, stride=2))\n",
+    "        # (b, 32, 7, 7) => (b, 32*7*7)\n",
+    "        # (b, 32*7*7) => (b, 10)\n",
+    "        self.fc = nn.Linear(32*(input_shape//4)*(input_shape//4), num_classes)\n",
+    "\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        # (b, 1, 28, 28) => (b, 16, 14, 14)\n",
+    "        out = self.cnn1(x)\n",
+    "        # (b, 16, 14, 14) => (b, 32, 7, 7)\n",
+    "        out = self.cnn2(out)\n",
+    "        # (b, 32, 7, 7) => (b, 32*7*7)\n",
+    "        out = out.reshape(out.size(0), -1)\n",
+    "        out = self.fc(out)\n",
+    "        return out\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ab15298",
+   "metadata": {},
+   "source": [
+    "### torchsummary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4e0b41cd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:57:32.036968Z",
+     "start_time": "2023-02-12T07:57:30.861860Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\r\n",
+      "Requirement already satisfied: torchsummary in /home/chzhang/anaconda3/envs/ldm/lib/python3.8/site-packages (1.5.1)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install torchsummary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "78aa128c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:57:38.852830Z",
+     "start_time": "2023-02-12T07:57:38.847705Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "306be169",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:05:05.639359Z",
+     "start_time": "2023-02-12T08:05:05.623987Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = CNN(input_shape=input_shape, num_classes=num_classes, in_channels=1).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a4812e64",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T07:59:54.714382Z",
+     "start_time": "2023-02-12T07:59:54.222808Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1           [64, 16, 28, 28]             416\n",
+      "       BatchNorm2d-2           [64, 16, 28, 28]              32\n",
+      "              ReLU-3           [64, 16, 28, 28]               0\n",
+      "         MaxPool2d-4           [64, 16, 14, 14]               0\n",
+      "            Conv2d-5           [64, 32, 14, 14]          12,832\n",
+      "       BatchNorm2d-6           [64, 32, 14, 14]              64\n",
+      "              ReLU-7           [64, 32, 14, 14]               0\n",
+      "         MaxPool2d-8             [64, 32, 7, 7]               0\n",
+      "            Linear-9                   [64, 10]          15,690\n",
+      "================================================================\n",
+      "Total params: 29,034\n",
+      "Trainable params: 29,034\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.19\n",
+      "Forward/backward pass size (MB): 29.86\n",
+      "Params size (MB): 0.11\n",
+      "Estimated Total Size (MB): 30.17\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary(model, input_size=(1, 28, 28), batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "872d454c",
+   "metadata": {},
+   "source": [
+    "## model train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3e0babe0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:05:09.085935Z",
+     "start_time": "2023-02-12T08:05:09.080188Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimzer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bc2ad9e6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:04:31.938635Z",
+     "start_time": "2023-02-12T08:04:31.933653Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "total_batch = len(train_dataloader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "636c01fb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:05:42.541533Z",
+     "start_time": "2023-02-12T08:05:11.254793Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/5, 100/938: 0.2435\n",
+      "1/5, 200/938: 0.0455\n",
+      "1/5, 300/938: 0.0351\n",
+      "1/5, 400/938: 0.2363\n",
+      "1/5, 500/938: 0.0385\n",
+      "1/5, 600/938: 0.0285\n",
+      "1/5, 700/938: 0.0369\n",
+      "1/5, 800/938: 0.0443\n",
+      "1/5, 900/938: 0.1301\n",
+      "2/5, 100/938: 0.0114\n",
+      "2/5, 200/938: 0.0374\n",
+      "2/5, 300/938: 0.0232\n",
+      "2/5, 400/938: 0.0625\n",
+      "2/5, 500/938: 0.0478\n",
+      "2/5, 600/938: 0.0270\n",
+      "2/5, 700/938: 0.0067\n",
+      "2/5, 800/938: 0.0709\n",
+      "2/5, 900/938: 0.0186\n",
+      "3/5, 100/938: 0.0077\n",
+      "3/5, 200/938: 0.0060\n",
+      "3/5, 300/938: 0.0492\n",
+      "3/5, 400/938: 0.0459\n",
+      "3/5, 500/938: 0.0081\n",
+      "3/5, 600/938: 0.0468\n",
+      "3/5, 700/938: 0.0169\n",
+      "3/5, 800/938: 0.0386\n",
+      "3/5, 900/938: 0.0110\n",
+      "4/5, 100/938: 0.0027\n",
+      "4/5, 200/938: 0.0240\n",
+      "4/5, 300/938: 0.0047\n",
+      "4/5, 400/938: 0.0035\n",
+      "4/5, 500/938: 0.0002\n",
+      "4/5, 600/938: 0.0321\n",
+      "4/5, 700/938: 0.0060\n",
+      "4/5, 800/938: 0.0004\n",
+      "4/5, 900/938: 0.0597\n",
+      "5/5, 100/938: 0.0049\n",
+      "5/5, 200/938: 0.0140\n",
+      "5/5, 300/938: 0.0072\n",
+      "5/5, 400/938: 0.0270\n",
+      "5/5, 500/938: 0.0177\n",
+      "5/5, 600/938: 0.0005\n",
+      "5/5, 700/938: 0.0047\n",
+      "5/5, 800/938: 0.0440\n",
+      "5/5, 900/938: 0.0094\n"
+     ]
+    }
+   ],
+   "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 = model(images)\n",
+    "        loss = criterion(out, labels)\n",
+    "        \n",
+    "        # backward\n",
+    "        optimzer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimzer.step()   # 更细 模型参数\n",
+    "        \n",
+    "        if (batch_idx+1) % 100 == 0:\n",
+    "            print(f'{epoch+1}/{num_epochs}, {batch_idx+1}/{total_batch}: {loss.item():.4f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e7078dd",
+   "metadata": {},
+   "source": [
+    "## model evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9d99e59a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:07:52.577181Z",
+     "start_time": "2023-02-12T08:07:52.228875Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9906/10000=0.9906\n"
+     ]
+    }
+   ],
+   "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 = model(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": 29,
+   "id": "2e68f24f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-02-12T08:08:08.828595Z",
+     "start_time": "2023-02-12T08:08:08.817961Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "torch.save(model.state_dict(), 'cnn_mnist.ckpt')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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