434 lines
13 KiB
Plaintext
434 lines
13 KiB
Plaintext
{
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"cells": [
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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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"outputs": [],
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"source": [
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"%matplotlib inline"
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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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"\n",
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"Neural Networks\n",
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"===============\n",
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"\n",
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"使用torch.nn包来构建神经网络。\n",
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"\n",
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"上一讲已经讲过了``autograd``，``nn``包依赖``autograd``包来定义模型并求导。\n",
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"一个``nn.Module``包含各个层和一个``forward(input)``方法，该方法返回``output``。\n",
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"\n",
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"\n",
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"\n",
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"例如：\n",
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"\n",
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"\n",
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"\n",
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"它是一个简单的前馈神经网络，它接受一个输入，然后一层接着一层地传递，最后输出计算的结果。\n",
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"\n",
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"神经网络的典型训练过程如下：\n",
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"\n",
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"1. 定义包含一些可学习的参数(或者叫权重)神经网络模型； \n",
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"2. 在数据集上迭代； \n",
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"3. 通过神经网络处理输入； \n",
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"4. 计算损失(输出结果和正确值的差值大小)；\n",
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"5. 将梯度反向传播回网络的参数； \n",
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"6. 更新网络的参数，主要使用如下简单的更新原则： \n",
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"``weight = weight - learning_rate * gradient``\n",
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"\n",
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" \n",
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"\n",
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"定义网络\n",
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"------------------\n",
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"\n",
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"开始定义一个网络：\n",
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"\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": 1,
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"metadata": {},
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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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"Net(\n",
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" (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
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" (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
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" (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
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" (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
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" (fc3): Linear(in_features=84, out_features=10, bias=True)\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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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"\n",
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"\n",
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"class Net(nn.Module):\n",
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"\n",
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" def __init__(self):\n",
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" super(Net, self).__init__()\n",
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" # 1 input image channel, 6 output channels, 5x5 square convolution\n",
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" # kernel\n",
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" self.conv1 = nn.Conv2d(1, 6, 5)\n",
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" self.conv2 = nn.Conv2d(6, 16, 5)\n",
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" # an affine operation: y = Wx + b\n",
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" self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
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" self.fc2 = nn.Linear(120, 84)\n",
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" self.fc3 = nn.Linear(84, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" # Max pooling over a (2, 2) window\n",
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" x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
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" # If the size is a square you can only specify a single number\n",
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" x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
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" x = x.view(-1, self.num_flat_features(x))\n",
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" x = F.relu(self.fc1(x))\n",
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" x = F.relu(self.fc2(x))\n",
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" x = self.fc3(x)\n",
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" return x\n",
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"\n",
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" def num_flat_features(self, x):\n",
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" size = x.size()[1:] # all dimensions except the batch dimension\n",
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" num_features = 1\n",
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" for s in size:\n",
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" num_features *= s\n",
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" return num_features\n",
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"\n",
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"\n",
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"net = Net()\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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"在模型中必须要定义 ``forward`` 函数，``backward``\n",
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"函数（用来计算梯度）会被``autograd``自动创建。\n",
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"可以在 ``forward`` 函数中使用任何针对 Tensor 的操作。\n",
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"\n",
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" ``net.parameters()``返回可被学习的参数（权重）列表和值\n",
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"\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": 2,
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"metadata": {},
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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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"10\n",
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"torch.Size([6, 1, 5, 5])\n"
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]
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}
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],
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"source": [
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"params = list(net.parameters())\n",
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"print(len(params))\n",
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"print(params[0].size()) # conv1's .weight"
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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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"测试随机输入32×32。\n",
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"注：这个网络（LeNet）期望的输入大小是32×32，如果使用MNIST数据集来训练这个网络，请把图片大小重新调整到32×32。\n",
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"\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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"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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"tensor([[ 0.1120, 0.0713, 0.1014, -0.0696, -0.1210, 0.0084, -0.0206, 0.1366,\n",
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" -0.0455, -0.0036]], grad_fn=<AddmmBackward>)\n"
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]
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}
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],
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"source": [
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"input = torch.randn(1, 1, 32, 32)\n",
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"out = net(input)\n",
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"print(out)"
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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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"将所有参数的梯度缓存清零，然后进行随机梯度的的反向传播：\n",
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"\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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"net.zero_grad()\n",
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"out.backward(torch.randn(1, 10))"
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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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"<div class=\"alert alert-info\"><h4>Note</h4><p>``torch.nn`` 只支持小批量输入。整个 ``torch.nn``\n",
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"包都只支持小批量样本，而不支持单个样本。\n",
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"\n",
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" 例如，``nn.Conv2d`` 接受一个4维的张量，\n",
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" ``每一维分别是sSamples * nChannels * Height * Width（样本数*通道数*高*宽）``。\n",
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"\n",
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" 如果你有单个样本，只需使用 ``input.unsqueeze(0)`` 来添加其它的维数</p></div>\n",
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"\n",
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"在继续之前，我们回顾一下到目前为止用到的类。\n",
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"\n",
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"**回顾:**\n",
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" - ``torch.Tensor``：一个用过自动调用 ``backward()``实现支持自动梯度计算的 *多维数组* ，\n",
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" 并且保存关于这个向量的*梯度* w.r.t.\n",
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" - ``nn.Module``：神经网络模块。封装参数、移动到GPU上运行、导出、加载等。\n",
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" - ``nn.Parameter``：一种变量，当把它赋值给一个``Module``时，被 *自动* 地注册为一个参数。\n",
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" - ``autograd.Function``：实现一个自动求导操作的前向和反向定义，每个变量操作至少创建一个函数节点，每一个``Tensor``的操作都回创建一个接到创建``Tensor``和 *编码其历史* 的函数的``Function``节点。\n",
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"\n",
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"**重点如下：**\n",
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" - 定义一个网络\n",
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" - 处理输入，调用backword\n",
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"\n",
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"**还剩：**\n",
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" - 计算损失\n",
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" - 更新网络权重\n",
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"\n",
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"损失函数\n",
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"-------------\n",
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"一个损失函数接受一对 (output, target) 作为输入，计算一个值来估计网络的输出和目标值相差多少。\n",
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"\n",
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"***译者注：output为网络的输出，target为实际值***\n",
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"\n",
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"nn包中有很多不同的[损失函数](https://pytorch.org/docs/nn.html#loss-functions)。\n",
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"``nn.MSELoss``是一个比较简单的损失函数，它计算输出和目标间的**均方误差**，\n",
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"例如：\n",
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"\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": 8,
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"metadata": {},
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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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"tensor(0.8109, grad_fn=<MseLossBackward>)\n"
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]
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}
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],
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"source": [
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"output = net(input)\n",
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"target = torch.randn(10) # 随机值作为样例\n",
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"target = target.view(1, -1) # 使target和output的shape相同\n",
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"criterion = nn.MSELoss()\n",
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"\n",
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"loss = criterion(output, target)\n",
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"print(loss)"
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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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"现在，如果在反向过程中跟随``loss`` ， 使用它的\n",
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"``.grad_fn`` 属性，将看到如下所示的计算图。\n",
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"\n",
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"::\n",
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"\n",
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" input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d\n",
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" -> view -> linear -> relu -> linear -> relu -> linear\n",
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" -> MSELoss\n",
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" -> loss\n",
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"\n",
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"所以，当我们调用 ``loss.backward()``时,整张计算图都会\n",
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"根据loss进行微分，而且图中所有设置为``requires_grad=True``的张量\n",
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"将会拥有一个随着梯度累积的``.grad`` 张量。\n",
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"\n",
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"为了说明，让我们向后退几步:\n",
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"\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": 9,
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"metadata": {},
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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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"<MseLossBackward object at 0x7f3b49fe2470>\n",
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"<AddmmBackward object at 0x7f3bb05f17f0>\n",
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"<AccumulateGrad object at 0x7f3b4a3c34e0>\n"
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]
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}
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],
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"source": [
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"print(loss.grad_fn) # MSELoss\n",
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"print(loss.grad_fn.next_functions[0][0]) # Linear\n",
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"print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU"
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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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"反向传播\n",
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"--------\n",
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"调用loss.backward()获得反向传播的误差。\n",
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"\n",
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"但是在调用前需要清除已存在的梯度，否则梯度将被累加到已存在的梯度。\n",
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"\n",
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"现在，我们将调用loss.backward()，并查看conv1层的偏差（bias）项在反向传播前后的梯度。\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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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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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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"conv1.bias.grad before backward\n",
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"tensor([0., 0., 0., 0., 0., 0.])\n",
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"conv1.bias.grad after backward\n",
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"tensor([ 0.0051, 0.0042, 0.0026, 0.0152, -0.0040, -0.0036])\n"
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]
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}
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],
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"source": [
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"net.zero_grad() # 清除梯度\n",
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"\n",
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"print('conv1.bias.grad before backward')\n",
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"print(net.conv1.bias.grad)\n",
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"\n",
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"loss.backward()\n",
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"\n",
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"print('conv1.bias.grad after backward')\n",
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"print(net.conv1.bias.grad)"
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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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"如何使用损失函数\n",
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"\n",
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"**稍后阅读：**\n",
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"\n",
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" `nn`包，包含了各种用来构成深度神经网络构建块的模块和损失函数，完整的文档请查看[here](https://pytorch.org/docs/nn)。\n",
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"\n",
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"**剩下的最后一件事:**\n",
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"\n",
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" - 新网络的权重\n",
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"\n",
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"更新权重\n",
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"------------------\n",
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"在实践中最简单的权重更新规则是随机梯度下降（SGD）：\n",
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"\n",
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" ``weight = weight - learning_rate * gradient``\n",
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"\n",
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"我们可以使用简单的Python代码实现这个规则：\n",
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"\n",
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"```python\n",
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"\n",
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"learning_rate = 0.01\n",
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"for f in net.parameters():\n",
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" f.data.sub_(f.grad.data * learning_rate)\n",
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"```\n",
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"但是当使用神经网络是想要使用各种不同的更新规则时，比如SGD、Nesterov-SGD、Adam、RMSPROP等，PyTorch中构建了一个包``torch.optim``实现了所有的这些规则。\n",
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"使用它们非常简单：\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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch.optim as optim\n",
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"\n",
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"# create your optimizer\n",
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"optimizer = optim.SGD(net.parameters(), lr=0.01)\n",
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"\n",
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"# in your training loop:\n",
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"optimizer.zero_grad() # zero the gradient buffers\n",
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"output = net(input)\n",
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"loss = criterion(output, target)\n",
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"loss.backward()\n",
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"optimizer.step() # Does the update"
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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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".. 注意::\n",
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" \n",
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" 观察如何使用``optimizer.zero_grad()``手动将梯度缓冲区设置为零。\n",
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" 这是因为梯度是按Backprop部分中的说明累积的。\n",
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"\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": 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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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
|