【动手学习pytorch笔记】23.RNN简易实现

RNN简易实现

import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)

一样的加载 train_iter，vocab

num_hiddens = 256
rnn_layer = nn.RNN(len(vocab), num_hiddens)

使用nn提供的RNN，只需要传入两个参数（len(vocab), num_hiddens）

state = torch.zeros((1, batch_size, num_hiddens))
state.shape

隐藏状态还是要自己初始化，上一节说过，我们将state存在一个turple里（铺垫之后的LSTM），这里多了一个参数 1，

torch.Size([1, 32, 256])

测试一下

X = torch.rand(size=(num_steps, batch_size, len(vocab)))
Y, state_new = rnn_layer(X, state)
Y.shape, state_new.shape

输出

(torch.Size([35, 32, 256]), torch.Size([1, 32, 256]))

这里注意，torch的RNN和我们上一届自己写的有两个区别：

• 这里的Y并不是最终输出的Y，而是最后Linear层之前的Y，最后一个维度是256，不是28，需要我们自己再写一个线性层
• 这个Y并没有进行拼接成 [35 * 32，256]

#@save
class RNNModel(nn.Module):
    """循环神经网络模型"""
    def __init__(self, rnn_layer, vocab_size, **kwargs):
        super(RNNModel, self).__init__(**kwargs)
        self.rnn = rnn_layer
        self.vocab_size = vocab_size
        self.num_hiddens = self.rnn.hidden_size
        # 如果RNN是双向的（之后将介绍），num_directions应该是2，否则应该是1
        if not self.rnn.bidirectional:
            self.num_directions = 1
            self.linear = nn.Linear(self.num_hiddens, self.vocab_size)
        else:
            self.num_directions = 2
            self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size)

    def forward(self, inputs, state):
        X = F.one_hot(inputs.T.long(), self.vocab_size)
        X = X.to(torch.float32)
        Y, state = self.rnn(X, state)
        # 全连接层首先将Y的形状改为(时间步数*批量大小,隐藏单元数)
        # 它的输出形状是(时间步数*批量大小,词表大小)。
        output = self.linear(Y.reshape((-1, Y.shape[-1])))
        return output, state

    def begin_state(self, device, batch_size=1):
        if not isinstance(self.rnn, nn.LSTM):
            # nn.GRU以张量作为隐状态
            return  torch.zeros((self.num_directions * self.rnn.num_layers,
                                 batch_size, self.num_hiddens),
                                device=device)
        else:
            # nn.LSTM以元组作为隐状态
            return (torch.zeros((
                self.num_directions * self.rnn.num_layers,
                batch_size, self.num_hiddens), device=device),
                    torch.zeros((
                        self.num_directions * self.rnn.num_layers,
                        batch_size, self.num_hiddens), device=device))

self.linear = nn.Linear(self.num_hiddens, self.vocab_size) 就像我们刚才说的，这里需要自己定义输出的线性层

预测是一样的，看看

device = d2l.try_gpu()
net = RNNModel(rnn_layer, vocab_size=len(vocab))
net = net.to(device)
d2l.predict_ch8('time traveller', 10, net, vocab, device)

输出

'time travellermmjmrmmj'

训练

num_epochs, lr = 500, 1
d2l.train_ch8(net, train_iter, vocab, lr, num_epochs, device)

perplexity 1.3, 176156.1 tokens/sec on cuda:0
time travellerit s against reason said filby but you willnever c
travelleryou can show al and down can of sit ee ascond dfar