《PyTorch深度学习实践》第十二讲循环神经网络基础

一、RNN简介

1、RNN网络最大的特点就是可以处理序列特征，就是我们的一组动态特征。比如，我们可以通过将前三天每天的特征（是否下雨，是否有太阳等）输入到网络，从而来预测第四天的天气。
我们可以看RNN的网络结构如下：

二、RNN cell用法

import torch

batch_size = 1 # 批处理大小
seq_len = 3 # 序列长度
input_size = 4 # 输入维度
hidden_size = 2 # 隐藏层维度

cell = torch.nn.RNNCell(input_size=input_size, hidden_size=hidden_size)

# (seq, batch, features)
dataset = torch.randn(seq_len, batch_size, input_size)
print(dataset)
hidden = torch.zeros(batch_size, hidden_size)
print(hidden)

for idx, input in enumerate(dataset):
    print( '=' * 20, idx, '=' * 20)
    print( 'Input size: ', input.shape)
    hidden = cell(input, hidden)
    print( 'outputs size: ', hidden.shape)
    print(hidden)

三、RNN用法

import torch

batch_size = 1 # 批处理大小
seq_len = 3 # 序列长度
input_size = 4 # 输入维度
hidden_size = 2 # 隐藏层维度
num_layers = 4  # 隐藏层数量

cell = torch.nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers)

# (seqLen, batchSize, inputSize)
inputs = torch.randn(seq_len, batch_size, input_size)
hidden = torch.zeros(num_layers, batch_size, hidden_size)
out, hidden = cell(inputs, hidden)

print( 'Output size:', out.shape)
print( 'Output:', out)
print( 'Hidden size: ', hidden.shape)
print( 'Hidden: ', hidden)

四、Embedding

把input变为稠密的数据

代码：

import torch

# parameters
num_class = 4
input_size = 4
hidden_size = 8
embedding_size = 10
num_layers = 2
batch_size = 1
seq_len = 5

# 准备数据集
idx2char = ['e', 'h', 'l', 'o']
x_data = [[1, 0, 2, 2, 3]]  # (batch, seq_len)
y_data = [3, 1, 2, 3, 2]    # (batch * seq_len)

inputs = torch.LongTensor(x_data)   # Input should be LongTensor: (batchSize, seqLen)
labels = torch.LongTensor(y_data)   # Target should be LongTensor: (batchSize * seqLen)

# 构建模型
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.emb = torch.nn.Embedding(input_size, embedding_size)
        self.rnn = torch.nn.RNN(input_size=embedding_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
        self.fc = torch.nn.Linear(hidden_size, num_class)

    def forward(self, x):
        hidden = torch.zeros(num_layers, x.size(0), hidden_size)
        x = self.emb(x)  # (batch, seqLen, embeddingSize)
        x, _ = self.rnn(x, hidden)  # 输出(𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆, 𝒔𝒆𝒒𝑳𝒆𝒏, hidden_size)
        x = self.fc(x)  # 输出(𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆, 𝒔𝒆𝒒𝑳𝒆𝒏, 𝒏𝒖𝒎𝑪𝒍𝒂𝒔𝒔)
        return x.view(-1, num_class)  # reshape to use Cross Entropy: (𝒃𝒂𝒕𝒄𝒉𝑺𝒊𝒛𝒆×𝒔𝒆𝒒𝑳𝒆𝒏, 𝒏𝒖𝒎𝑪𝒍𝒂𝒔𝒔)

net = Model()

# 损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.05)

# 训练模型
for epoch in range(15):
    optimizer.zero_grad()
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

    _, idx = outputs.max(dim=1)
    idx = idx.data.numpy()
    print('Predicted: ', ''.join([idx2char[x] for x in idx]), end='')
    print(', Epoch [%d/15] loss = %.3f' % (epoch + 1, loss.item()))

运行结果：