pytorch深度学习实践-循环神经网络0113

B站 刘二大人：循环神经网络（基础篇）

目录

1、RNN概念

2、numLayers含义

3、RNN使用

4、利用RNN Cell训练hello转换到ohlol

5、Embedding编码方式

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1、RNN概念

        RNN Cell是线性层。

         隐层是RNN Cell里线性层矩阵w的行数。

         使用RNN Cell:

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  # 隐层维度

cell = torch.nn.RNNCell(input_size=input_size, hidden_size=hidden_size)  # 初始化

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

# 这个循环就是处理seq_len长度的数据
for idx, data in enumerate(dataset):
    print('=' * 20, idx, '=' * 20)
    print('Input size:', data.shape, data)

    hidden = cell(data, hidden)

    print('hidden size:', hidden.shape, hidden)
    print(hidden)

2、numLayers含义

3、RNN使用

        input_size和hidden_size: 输入维度和隐层维度

        batch_size: 批处理大小

        seq_len: 序列长度

        num_layers: 隐层数目

        使用RNN：

import torch

batch_size = 1  # batch_size: 批处理大小
seq_len = 3  # seq_len: 序列长度
input_size = 4  # input_size：输入维度
hidden_size = 2  # hidden_size: 隐层维度
num_layers = 1  # num_layers: 隐层数目

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)  # (seq_len, batch_size, hidden_size)
print('Output:', out)
print('Hidden size:', hidden.shape)  # (num_layers, batch_size, hidden_size)
print('Hidden:', hidden)

4、利用RNN Cell训练hello转换到ohlol

 

         代码如下：

import torch
input_size = 4
hidden_size = 4
batch_size = 1

idx2char = ['e', 'h', 'l', 'o']
x_data = [1, 0, 2, 3, 3]    # hello中各个字符的下标
y_data = [3, 1, 2, 3, 2]    # ohlol中各个字符的下标

one_hot_lookup = [[1, 0, 0, 0],
                  [0, 1, 0, 0],
                  [0, 0, 1, 0],
                  [0, 0, 0, 1]]
x_one_hot = [one_hot_lookup[x] for x in x_data]  # (seqLen, inputSize)

inputs = torch.Tensor(x_one_hot).view(-1, batch_size, input_size)
labels = torch.LongTensor(y_data).view(-1, 1)
# torch.Tensor默认是torch.FloatTensor是32位浮点类型数据，torch.LongTensor是64位整型
print(inputs.shape, labels.shape)


class Model(torch.nn.Module):
    def __init__(self, input_size, hidden_size, batch_size):
        super(Model, self).__init__()
        self.batch_size = batch_size
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.rnncell = torch.nn.RNNCell(input_size=self.input_size, hidden_size=self.hidden_size)

    def forward(self, inputs, hidden):
        hidden = self.rnncell(inputs, hidden)   # 输入和隐层转换为下一个隐层
        # shape of inputs:(batchSize, inputSize),shape of hidden:(batchSize, hiddenSize),
        return hidden

    def init_hidden(self):
        return torch.zeros(self.batch_size, self.hidden_size)  # 生成全0的h0

net = Model(input_size, hidden_size, batch_size)


criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.1)


for epoch in range(15):
    loss = 0
    optimizer.zero_grad()
    hidden = net.init_hidden()
    print('Predicted string:', end='')
    for input, label in zip(inputs, labels):
        hidden = net(input, hidden)
        # 注意交叉熵在计算loss的时候维度关系，这里的hidden是([1, 4]), label是 ([1])
        loss += criterion(hidden, label)
        _, idx = hidden.max(dim = 1)
        print(idx2char[idx.item()], end='')

    loss.backward()
    optimizer.step()
    print(', Epoch [%d/15] loss=%.4f' % (epoch+1, loss.item()))

结果：

 

5、Embedding编码方式

       独热编码向量维度过高；
       独热编码向量稀疏，每个向量是一个为1其余为0；
       独热编码是硬编码，编码情况与数据特征无关；
       采用一种低维度的、稠密的、可学习数据的编码方式：Embedding。

         代码：

import torch

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

idx2char_1 = ['e', 'h', 'l', 'o']
idx2char_2 = ['h', 'l', 'o']

x_data = [[1, 0, 2, 2, 3]]
y_data = [3, 1, 2, 2, 3]

# inputs 维度为（batchsize，seqLen）
inputs = torch.LongTensor(x_data)
# labels 维度为（batchsize*seqLen）
labels = torch.LongTensor(y_data)


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)  # 进行embedding处理
        x, _ = self.rnn(x, hidden)
        x = self.fc(x)
        return x.view(-1, num_class)


net = Model()

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.05)

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 string: ', ''.join([idx2char_1[x] for x in idx]), end='')
    print(", Epoch [%d/15] loss = %.3f" % (epoch + 1, loss.item()))

        结果：