RNN代码解释pytorch

你的名字 2022-04-08 13:51 399阅读 0赞

## 简述 ##

还是跟之前的CNN一样，都是学于莫烦Python的。

## 解释 ##

*  关于数据导入部分的代码含义，其实跟之前的CNN几乎完全一致。
 *  而且还需要部分的源代码–MNIST（**在之前的地方有超链接**）
 *  这些都可以在下面的CNN的链接中看到
 *  [卷积神经网络CNN入门【pytorch学习】][CNN_pytorch]

### 模型含义 ###

这里使用RNN，这是跟之前的CNN唯一的不同的地方，其他的都是完全一致的。

class RNN(nn.Module):
        def __init__(self):
            super(RNN, self).__init__()
            self.rnn = nn.LSTM(
                input_size=28,
                hidden_size=64,
                num_layers=1,
                batch_first=True
            )
            self.out = nn.Linear(64, 10)  # fully connected layer, output 10 classes
    
        def forward(self, x):
            r_out, (h_n, h_c) = self.rnn(x, None)  # None 表示 hidden state 会用全0的 state
            # r_out = [BATCH_SIZE, input_size, hidden_size]
            # r_out[:, -1, :] = [BATCH_SIZE, hidden_size] '-1'，表示选取最后一个时间点的 r_out 输出
            out = self.out(r_out[:, -1, :])
            # out = [BATCH_SIZE, 10]
            return out
    
    
    rnn = RNN()

#### LSTM参数解释 ####

*  输入参数，其实是表示有多少序列。这里的最小单位，考虑的其实不是整个图片的完整全部序列。而是每一行为最小单位的。
 *  所以说经过LSTM之后，输出的结果就是`r_out = [BATCH_SIZE, input_size, hidden_size]`。 第一个input\_size其实是恰好这个图片大小是（input\_size, input\_size）的

#### out中输入的有-1 ####

*  会发现这里有一个数字-1，其实就是表示要选最后的一列作为最后的结果。其实就是说只看最后的一行。

## 完整代码 ##

import os
    
    import torch
    import torch.nn as nn
    import torch.utils.data as Data
    import torchvision
    
    # Hyper Parameters
    EPOCH = 1  # train the training data n times, to save time, we just train 1 epoch
    BATCH_SIZE = 50
    LR = 0.001  # learning rate
    DOWNLOAD_MNIST = False
    
    # Mnist digits dataset
    if not (os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
        # not mnist dir or mnist is empyt dir
        DOWNLOAD_MNIST = True
    
    train_data = torchvision.datasets.MNIST(
        root='./mnist/',
        train=True,  # this is training data
        transform=torchvision.transforms.ToTensor(),  # Converts a PIL.Image or numpy.ndarray to
        # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
        download=DOWNLOAD_MNIST,
    )
    
    # Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
    train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
    
    # pick 2000 samples to speed up testing
    test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
    test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[
             :2000] / 255.  # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
    test_y = test_data.test_labels[:2000]
    
    
    class RNN(nn.Module):
        def __init__(self):
            super(RNN, self).__init__()
            self.rnn = nn.LSTM(
                input_size=28,
                hidden_size=64,
                num_layers=1,
                batch_first=True
            )
            self.out = nn.Linear(64, 10)  # fully connected layer, output 10 classes
    
        def forward(self, x):
            r_out, (h_n, h_c) = self.rnn(x, None)  # None 表示 hidden state 会用全0的 state
            # r_out = [BATCH_SIZE, input_size, hidden_size]
            # r_out[:, -1, :] = [BATCH_SIZE, hidden_size] '-1'，表示选取最后一个时间点的 r_out 输出
            out = self.out(r_out[:, -1, :])
            # out = [BATCH_SIZE, 10]
            return out
    
    
    rnn = RNN()
    
    optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)  # optimize all cnn parameters
    loss_func = nn.CrossEntropyLoss()  # the target label is not one-hotted
    
    for epoch in range(EPOCH):
        for step, (x, b_y) in enumerate(train_loader):  # gives batch data
            b_x = x.view(-1, 28, 28)  # reshape x to (batch, time_step, input_size)
            output = rnn(b_x)  # rnn output
            loss = loss_func(output, b_y)  # cross entropy loss
            optimizer.zero_grad()  # clear gradients for this training step
            loss.backward()  # backpropagation, compute gradients
            optimizer.step()
    
    test_output = rnn(test_x[:10].view(-1, 28, 28))
    pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
    
    print(pred_y, 'prediction number')
    print(test_y[:10], 'real number')

*  **结果：**

[7 2 1 0 4 1 4 9 6 9] prediction number
    tensor([7, 2, 1, 0, 4, 1, 4, 9, 5, 9]) real number

[CNN_pytorch]: https://blog.csdn.net/a19990412/article/details/83905170