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159 lines
6.4 KiB
Markdown
159 lines
6.4 KiB
Markdown
# 4.1 – CNN 卷积神经网络
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卷积神经网络目前被广泛地用在图片识别上, 已经有层出不穷的应用, 如果你对卷积神经网络还没有特别了解, 我制作的 卷积神经网络 动画简介 （如下） 能让你花几分钟就了解什么是卷积神经网络. 接着我们就一步一步做一个分析手写数字的 CNN 吧.
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下面是一个 CNN 最后一层的学习过程, 我们先可视化看看:
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## MNIST手写数据
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```py
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.utils.data as Data
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import torchvision # 数据库模块
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import matplotlib.pyplot as plt
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torch.manual_seed(1) # reproducible
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# Hyper Parameters
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EPOCH = 1 # 训练整批数据多少次, 为了节约时间, 我们只训练一次
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BATCH_SIZE = 50
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LR = 0.001 # 学习率
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DOWNLOAD_MNIST = True # 如果你已经下载好了mnist数据就写上 Fasle
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# Mnist 手写数字
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train_data = torchvision.datasets.MNIST(
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root=\\'./mnist/\\', # 保存或者提取位置
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train=True, # this is training data
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transform=torchvision.transforms.ToTensor(), # 转换 PIL.Image or numpy.ndarray 成
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# torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
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download=DOWNLOAD_MNIST, # 没下载就下载, 下载了就不用再下了
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)
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```
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黑色的地方的值都是0, 白色的地方值大于0.
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同样, 我们除了训练数据, 还给一些测试数据, 测试看看它有没有训练好.
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```py
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test_data = torchvision.datasets.MNIST(root=\\'./mnist/\\', train=False)
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# 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
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train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
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# 为了节约时间, 我们测试时只测试前2000个
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test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor)[:2000]/255\. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
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test_y = test_data.test_labels[:2000]
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```
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## CNN模型
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和以前一样, 我们用一个 class 来建立 CNN 模型. 这个 CNN 整体流程是 卷积( Conv2d ) -> 激励函数( ReLU ) -> 池化, 向下采样 ( MaxPooling ) -> 再来一遍 -> 展平多维的卷积成的特征图 -> 接入全连接层 ( Linear ) -> 输出
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```py
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class CNN(nn.Module):
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def __init__(self):
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super(CNN, self).__init__()
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self.conv1 = nn.Sequential( # input shape (1, 28, 28)
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nn.Conv2d(
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in_channels=1, # input height
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out_channels=16, # n_filters
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kernel_size=5, # filter size
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stride=1, # filter movement/step
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padding=2, # 如果想要 con2d 出来的图片长宽没有变化, padding=(kernel_size-1)/2 当 stride=1
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), # output shape (16, 28, 28)
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nn.ReLU(), # activation
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nn.MaxPool2d(kernel_size=2), # 在 2x2 空间里向下采样, output shape (16, 14, 14)
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)
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self.conv2 = nn.Sequential( # input shape (1, 28, 28)
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nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
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nn.ReLU(), # activation
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nn.MaxPool2d(2), # output shape (32, 7, 7)
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)
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self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
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output = self.out(x)
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return output
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cnn = CNN()
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print(cnn) # net architecture
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"""
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CNN (
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(conv1): Sequential (
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(0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
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(1): ReLU ()
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(2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
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)
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(conv2): Sequential (
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(0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
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(1): ReLU ()
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(2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))
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)
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(out): Linear (1568 -> 10)
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)
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"""
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```
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## 训练
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下面我们开始训练, 将 y 都用 Variable 包起来, 然后放入 cnn 中计算 output, 最后再计算误差. 下面代码省略了计算精确度 accuracy 的部分, 如果想细看 accuracy 代码的同学, 请去往我的 github 看全部代码.
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```py
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optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
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loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
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# training and testing
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for epoch in range(EPOCH):
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for step, (x, y) in enumerate(train_loader): # 分配 batch data, normalize x when iterate train_loader
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b_x = Variable(x) # batch x
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b_y = Variable(y) # batch y
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output = cnn(b_x) # cnn output
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loss = loss_func(output, b_y) # cross entropy loss
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optimizer.zero_grad() # clear gradients for this training step
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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"""
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...
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Epoch: 0 | train loss: 0.0306 | test accuracy: 0.97
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Epoch: 0 | train loss: 0.0147 | test accuracy: 0.98
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Epoch: 0 | train loss: 0.0427 | test accuracy: 0.98
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Epoch: 0 | train loss: 0.0078 | test accuracy: 0.98
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"""
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```
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最后我们再来取10个数据, 看看预测的值到底对不对:
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```py
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test_output = cnn(test_x[:10])
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pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
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print(pred_y, \\'prediction number\\')
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print(test_y[:10].numpy(), \\'real number\\')
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"""
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[7 2 1 0 4 1 4 9 5 9] prediction number
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[7 2 1 0 4 1 4 9 5 9] real number
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"""
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```
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#### 可视化训练(视频中没有)
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这是做完视频后突然想要补充的内容, 因为可视化可以帮助理解, 所以还是有必要提一下. 可视化的代码主要是用 matplotlib 和 sklearn 来完成的, 因为其中我们用到了 T-SNE 的降维手段, 将高维的 CNN 最后一层输出结果可视化, 也就是 CNN forward 代码中的 x = x.view(x.size(0), -1) 这一个结果.
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可视化的代码不是重点, 我们就直接展示可视化的结果吧.
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所以这也就是在我 [github 代码](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/401_CNN.py) 中的每一步的意义啦.
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文章来源：[莫烦](https://morvanzhou.github.io/) |