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145 lines
5.8 KiB
Markdown
145 lines
5.8 KiB
Markdown
# 4.2 – RNN 循环神经网络 (分类 Classification)
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循环神经网络让神经网络有了记忆, 对于序列话的数据,循环神经网络能达到更好的效果. 如果你对循环神经网络还没有特别了解, 请观看几分钟的短动画, RNN 动画简介（如下） 和 LSTM（如下） 动画简介 能让你生动理解 RNN. 接着我们就一步一步做一个分析手写数字的 RNN 吧.
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## RNN 简介
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## LSTM 简介
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## MNIST手写数据
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```py
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import torch
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from torch import nn
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from torch.autograd import Variable
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import torchvision.datasets as dsets
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import torchvision.transforms as transforms
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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 = 64
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TIME_STEP = 28 # rnn 时间步数 / 图片高度
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INPUT_SIZE = 28 # rnn 每步输入值 / 图片每行像素
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LR = 0.01 # learning rate
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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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#### RNN模型
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和以前一样, 我们用一个 class 来建立 RNN 模型. 这个 RNN 整体流程是
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1. (input0, state0) -> LSTM -> (output0, state1) ;
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2. (input1, state1) -> LSTM -> (output1, state2) ;
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3. …
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4. (inputN, stateN)-> LSTM -> (outputN, stateN 1) ;
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5. outputN -> Linear -> prediction . 通过LSTM分析每一时刻的值, 并且将这一时刻和前面时刻的理解合并在一起, 生成当前时刻对前面数据的理解或记忆. 传递这种理解给下一时刻分析.
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```py
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class RNN(nn.Module):
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def __init__(self):
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super(RNN, self).__init__()
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self.rnn = nn.LSTM( # LSTM 效果要比 nn.RNN() 好多了
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input_size=28, # 图片每行的数据像素点
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hidden_size=64, # rnn hidden unit
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num_layers=1, # 有几层 RNN layers
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batch_first=True, # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
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)
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self.out = nn.Linear(64, 10) # 输出层
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def forward(self, x):
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# x shape (batch, time_step, input_size)
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# r_out shape (batch, time_step, output_size)
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# h_n shape (n_layers, batch, hidden_size) LSTM 有两个 hidden states, h_n 是分线, h_c 是主线
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# h_c shape (n_layers, batch, hidden_size)
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r_out, (h_n, h_c) = self.rnn(x, None) # None 表示 hidden state 会用全0的 state
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# 选取最后一个时间点的 r_out 输出
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# 这里 r_out[:, -1, :] 的值也是 h_n 的值
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out = self.out(r_out[:, -1, :])
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return out
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rnn = RNN()
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print(rnn)
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"""
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RNN (
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(rnn): LSTM(28, 64, batch_first=True)
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(out): Linear (64 -> 10)
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)
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"""
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```
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#### 训练
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我们将图片数据看成一个时间上的连续数据, 每一行的像素点都是这个时刻的输入, 读完整张图片就是从上而下的读完了每行的像素点. 然后我们就可以拿出 RNN 在最后一步的分析值判断图片是哪一类了. 下面的代码省略了计算 accuracy 的部分, 你可以在我的 github 中看到全部代码.
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```py
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optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all 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): # gives batch data
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b_x = Variable(x.view(-1, 28, 28)) # reshape x to (batch, time_step, input_size)
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b_y = Variable(y) # batch y
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output = rnn(b_x) # rnn 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: 0.0945 | test accuracy: 0.94
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Epoch: 0 | train loss: 0.0984 | test accuracy: 0.94
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Epoch: 0 | train loss: 0.0332 | test accuracy: 0.95
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Epoch: 0 | train loss: 0.1868 | test accuracy: 0.96
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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 = rnn(test_x[:10].view(-1, 28, 28))
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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], \\'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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所以这也就是在我 [github 代码](https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/402_RNN_classifier.py) 中的每一步的意义啦.
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文章来源：[莫烦](https://morvanzhou.github.io/) |