wehub-resource-sync
307 lines
8.8 KiB
Plaintext
307 lines
8.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5.5 卷积神经网络(LeNet)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:57:37.383972Z",
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"start_time": "2019-05-29T13:57:34.520559Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.0.0\n",
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"cuda\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import time\n",
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"import torch\n",
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"from torch import nn, optim\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"\n",
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"print(torch.__version__)\n",
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"print(device)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.5.1 LeNet模型 "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:57:37.394997Z",
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"start_time": "2019-05-29T13:57:37.386720Z"
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}
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},
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"outputs": [],
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"source": [
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"class LeNet(nn.Module):\n",
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" def __init__(self):\n",
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" super(LeNet, self).__init__()\n",
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" self.conv = nn.Sequential(\n",
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" nn.Conv2d(1, 6, 5), # in_channels, out_channels, kernel_size\n",
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" nn.Sigmoid(),\n",
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" nn.MaxPool2d(2, 2), # kernel_size, stride\n",
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" nn.Conv2d(6, 16, 5),\n",
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" nn.Sigmoid(),\n",
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" nn.MaxPool2d(2, 2)\n",
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" )\n",
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" self.fc = nn.Sequential(\n",
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" nn.Linear(16*4*4, 120),\n",
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" nn.Sigmoid(),\n",
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" nn.Linear(120, 84),\n",
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" nn.Sigmoid(),\n",
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" nn.Linear(84, 10)\n",
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" )\n",
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"\n",
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" def forward(self, img):\n",
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" feature = self.conv(img)\n",
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" output = self.fc(feature.view(img.shape[0], -1))\n",
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" return output"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:57:37.450484Z",
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"start_time": "2019-05-29T13:57:37.397357Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LeNet(\n",
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" (conv): Sequential(\n",
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" (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
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" (1): Sigmoid()\n",
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" (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
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" (4): Sigmoid()\n",
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" (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (fc): Sequential(\n",
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" (0): Linear(in_features=256, out_features=120, bias=True)\n",
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" (1): Sigmoid()\n",
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" (2): Linear(in_features=120, out_features=84, bias=True)\n",
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" (3): Sigmoid()\n",
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" (4): Linear(in_features=84, out_features=10, bias=True)\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"net = LeNet()\n",
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"print(net)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.5.2 获取数据和训练模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:57:38.432567Z",
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"start_time": "2019-05-29T13:57:37.452521Z"
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}
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},
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"outputs": [],
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"source": [
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"batch_size = 256\n",
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"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:57:38.442887Z",
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"start_time": "2019-05-29T13:57:38.435111Z"
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}
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},
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"outputs": [],
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"source": [
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"# 本函数已保存在d2lzh_pytorch包中方便以后使用。该函数将被逐步改进：它的完整实现将在“图像增广”一节中描述\n",
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"def evaluate_accuracy(data_iter, net, device=None):\n",
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" if device is None and isinstance(net, torch.nn.Module):\n",
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" # 如果没指定device就使用net的device\n",
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" device = list(net.parameters())[0].device\n",
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" acc_sum, n = 0.0, 0\n",
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" with torch.no_grad():\n",
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" for X, y in data_iter:\n",
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" if isinstance(net, torch.nn.Module):\n",
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" net.eval() # 评估模式, 这会关闭dropout\n",
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" acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()\n",
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" net.train() # 改回训练模式\n",
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" else: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU\n",
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" if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数\n",
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" # 将is_training设置成False\n",
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" acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() \n",
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" else:\n",
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" acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() \n",
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" n += y.shape[0]\n",
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" return acc_sum / n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:57:38.453480Z",
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"start_time": "2019-05-29T13:57:38.445655Z"
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}
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},
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"outputs": [],
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"source": [
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"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
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"def train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs):\n",
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" net = net.to(device)\n",
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" print(\"training on \", device)\n",
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" loss = torch.nn.CrossEntropyLoss()\n",
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" batch_count = 0\n",
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" for epoch in range(num_epochs):\n",
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" train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()\n",
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" for X, y in train_iter:\n",
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" X = X.to(device)\n",
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" y = y.to(device)\n",
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" y_hat = net(X)\n",
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" l = loss(y_hat, y)\n",
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" optimizer.zero_grad()\n",
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" l.backward()\n",
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" optimizer.step()\n",
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" train_l_sum += l.cpu().item()\n",
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" train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()\n",
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" n += y.shape[0]\n",
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" batch_count += 1\n",
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" test_acc = evaluate_accuracy(test_iter, net)\n",
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" print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'\n",
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" % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T13:58:00.333237Z",
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"start_time": "2019-05-29T13:57:38.456012Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"training on cuda\n",
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"epoch 1, loss 1.7885, train acc 0.337, test acc 0.584, time 2.4 sec\n",
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"epoch 2, loss 0.4793, train acc 0.614, test acc 0.666, time 2.3 sec\n",
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"epoch 3, loss 0.2637, train acc 0.704, test acc 0.720, time 2.3 sec\n",
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"epoch 4, loss 0.1747, train acc 0.734, test acc 0.740, time 2.2 sec\n",
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"epoch 5, loss 0.1282, train acc 0.751, test acc 0.749, time 2.2 sec\n"
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]
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}
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],
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"source": [
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"lr, num_epochs = 0.001, 5\n",
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"optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
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"train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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