动手学深度学习（Pytorch版）代码实践 -循环神经网络-55循环神经网络的从零开始实现和简洁实现

55循环神经网络的实现

1.从零开始实现

import math
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
import matplotlib.pyplot as plt
import liliPytorch as lp

# 读取H.G.Wells的时光机器数据集
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)

# 查看数据集
# for X, Y in train_iter:
#     print('X:', X.shape)
#     print('Y:', Y.shape)
# print(vocab.token_freqs)
# print(vocab.idx_to_token)
# print(vocab.token_to_idx)

# 独热编码
# 将每个索引映射为相互不同的单位向量： 假设词表中不同词元的数目为N（即len(vocab)）， 词元索引的范围为0
# 到N-1。 如果词元的索引是整数i， 那么我们将创建一个长度为N的全0向量， 并将第i处的元素设置为1。 
# 此向量是原始词元的一个独热向量。
# print(F.one_hot(torch.tensor([0,3,6]), len(vocab)))
"""
tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0],
        [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        
         0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        
         0, 0, 0, 0]])
"""

# 每次采样的小批量数据形状是二维张量： （批量大小，时间步数）。 
# one_hot函数将这样一个小批量数据转换成三维张量， 张量的最后一个维度等于词表大小（len(vocab)）。
# 我们经常转换输入的维度，以便获得形状为 （时间步数，批量大小，词表大小）的输出。 
# 这将使我们能够更方便地通过最外层的维度， 一步一步地更新小批量数据的隐状态。

# X = torch.arange(10).reshape((2, 5))
# print(X)
# tensor([[0, 1, 2, 3, 4],
#         [5, 6, 7, 8, 9]])
# print(X.T)
# tensor([[0, 5],
#         [1, 6],
#         [2, 7],
#         [3, 8],
#         [4, 9]])
# print(F.one_hot(X.T, 28).shape) # torch.Size([5, 2, 28])
# print(F.one_hot(X.T, 28))
"""
tensor([[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0]],

        [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0]],

        [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0]],

        [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0]],

        [[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
          0, 0, 0, 0, 0]]])
"""

# 初始化模型参数
def get_params(vocab_size, num_hiddens, device):
    # 设置输入和输出的数量为词汇表的大小
    num_inputs = num_outputs = vocab_size

    # 定义一个函数，用于以正态分布初始化权重
    def normal(shape):
        return torch.randn(size=shape, device=device) * 0.01

    # 初始化隐藏层参数
    W_xh = normal((num_inputs, num_hiddens))  # 输入到隐藏层的权重
    W_hh = normal((num_hiddens, num_hiddens))  # 隐藏层到隐藏层的权重（循环权重）
    b_h = torch.zeros(num_hiddens, device=device)  # 隐藏层的偏置

    # 初始化输出层参数
    W_hq = normal((num_hiddens, num_outputs))  # 隐藏层到输出层的权重
    b_q = torch.zeros(num_outputs, device=device)  # 输出层的偏置

    # 将所有参数收集到一个列表中
    params = [W_xh, W_hh, b_h, W_hq, b_q]

    # 设置每个参数的requires_grad属性为True，以便在反向传播期间计算梯度
    for param in params:
        param.requires_grad_(True)

    return params  # 返回参数列表

# 循环神经网络模型
# 初始化时返回隐状态
def init_rnn_state(batch_size, num_hiddens, device):
    # batch_size：批量的大小，即每次输入到RNN的序列数量。
    # num_hiddens：隐藏层单元的数量，即隐藏状态的维度。
    return (torch.zeros((batch_size, num_hiddens), device=device), ) # 返回一个包含一个张量的元组


def rnn(inputs, state, params):
    # inputs的形状：(时间步数量，批量大小，词表大小)
    # state：初始隐藏状态，通常是一个元组，包含隐藏层的状态。
    # params：RNN的参数，包含权重和偏置。
    W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state # 当前的隐藏状态。
    outputs = []
    # X的形状：(批量大小，词表大小)
    for X in inputs:
        H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h)
        Y = torch.mm(H, W_hq) + b_q
        outputs.append(Y)
    return torch.cat(outputs, dim=0), (H,)

# 存储从零开始实现的循环神经网络模型的参数
class RNNModelScratch: #@save
    """从零开始实现的循环神经网络模型"""
    def __init__(self, vocab_size, num_hiddens, device,
                 get_params, init_state, forward_fn):
        self.vocab_size, self.num_hiddens = vocab_size, num_hiddens
        self.params = get_params(vocab_size, num_hiddens, device)
        self.init_state, self.forward_fn = init_state, forward_fn

    def __call__(self, X, state): # 前向传播方法
        X = F.one_hot(X.T, self.vocab_size).type(torch.float32)
        return self.forward_fn(X, state, self.params)

    def begin_state(self, batch_size, device): # 初始化隐藏状态
        return self.init_state(batch_size, self.num_hiddens, device)


# X = torch.arange(10).reshape((2, 5))
num_hiddens = 512
# net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), get_params,
#                       init_rnn_state, rnn)
# state = net.begin_state(X.shape[0], d2l.try_gpu()) # 初始化隐藏状态
# 调用模型实例的 __call__ 方法执行前向传播。
# Y, new_state = net(X.to(d2l.try_gpu()), state)
# Y：模型输出。
# new_state：更新后的隐藏状态。

# print(Y.shape, len(new_state), new_state[0].shape)
# torch.Size([10, 28]) 1 torch.Size([2, 512])
# 输出形状是（时间步数 X 批量大小，词表大小）， 而隐状态形状保持不变，即（批量大小，隐藏单元数)


def predict_ch8(prefix, num_preds, net, vocab, device):  #@save
    """在prefix后面生成新字符
        prefix：生成文本的前缀，即初始输入字符序列。
        num_preds：要预测的字符数。
        net：训练好的循环神经网络模型。
        vocab：词汇表，包含字符到索引和索引到字符的映射。
    """
    state = net.begin_state(batch_size=1, device=device)
    outputs = [vocab[prefix[0]]] # outputs：用于存储生成字符的索引列表。
    get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape((1, 1))
    for y in prefix[1:]:  # 预热期,遍历前缀中的剩余字符（从第二个字符开始）。
        _, state = net(get_input(), state) # 调用 net 进行前向传播，更新隐藏状态 state。
        outputs.append(vocab[y]) # 将当前字符的索引添加到 outputs 中。
        
    for _ in range(num_preds):  # 预测num_preds步
        # 调用 net 进行前向传播，获取预测结果 y 和更新后的隐藏状态 state。
        y, state = net(get_input(), state)
        # 使用 y.argmax(dim=1) 获取预测的字符索引，并将其添加到 outputs 中。
        outputs.append(int(y.argmax(dim=1).reshape(1)))
    return ''.join([vocab.idx_to_token[i] for i in outputs])

# print(predict_ch8('time traveller ', 10, net, vocab, d2l.try_gpu()))
# time traveller cfjwsthaqc

# 梯度裁剪
"""
在训练深层神经网络（特别是循环神经网络）时，梯度爆炸（gradients exploding）问题会导致梯度值变得非常大，
从而导致模型不稳定甚至训练失败。为了防止梯度爆炸，可以对梯度进行裁剪，使得梯度的范数不超过某个预设的阈值。
"""
def grad_clipping(net, theta):  #@save
    """裁剪梯度
        net：神经网络模型。
        theta：梯度裁剪的阈值。
    """
    if isinstance(net, nn.Module):
        params = [p for p in net.parameters() if p.requires_grad]
    else:
        params = net.params
    # 计算梯度范数, L2 范数
    norm = torch.sqrt(sum(torch.sum((p.grad ** 2)) for p in params))
    if norm > theta:
        for param in params:
            param.grad[:] *= theta / norm
            # 将每个参数的梯度按比例缩放，使得新的梯度范数等于 theta。

# 训练
def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):
    """训练网络一个迭代周期（定义见第8章）"""
    state, timer = None, d2l.Timer()
    metric = lp.Accumulator(2)  # 训练损失之和,词元数量
    for X, Y in train_iter:
        if state is None or use_random_iter:
            # 在第一次迭代或使用随机抽样时初始化state
            state = net.begin_state(batch_size=X.shape[0], device=device)
        else:
            if isinstance(net, nn.Module) and not isinstance(state, tuple):
                # state对于nn.GRU是个张量
                state.detach_()
            else:
                # state对于nn.LSTM或对于我们从零开始实现的模型是个张量
                for s in state:
                    s.detach_()
        y = Y.T.reshape(-1)
        X, y = X.to(device), y.to(device)
        y_hat, state = net(X, state)
        l = loss(y_hat, y.long()).mean()
        if isinstance(updater, torch.optim.Optimizer):
            updater.zero_grad()
            l.backward()
            grad_clipping(net, 1)
            updater.step()
        else:
            l.backward()
            grad_clipping(net, 1)
            # 因为已经调用了mean函数
            updater(batch_size=1)
        metric.add(l * y.numel(), y.numel())
    return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()

#@save
def train_ch8(net, train_iter, vocab, lr, num_epochs, device,
              use_random_iter=False):
    """训练模型（定义见第8章）"""
    loss = nn.CrossEntropyLoss()
    animator = lp.Animator(xlabel='epoch', ylabel='perplexity',
                            legend=['train'], xlim=[10, num_epochs])
    # 初始化
    if isinstance(net, nn.Module):
        updater = torch.optim.SGD(net.parameters(), lr)
    else:
        updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)

    predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)
    # 训练和预测
    for epoch in range(num_epochs):
        ppl, speed = train_epoch_ch8(
            net, train_iter, loss, updater, device, use_random_iter)
        if (epoch + 1) % 10 == 0:
            print(predict('time traveller'))
            animator.add(epoch + 1, [ppl])
    print(f'困惑度 {ppl:.1f}, {speed:.1f} 词元/秒 {str(device)}')
    print(predict('time traveller '))
    print(predict('traveller '))

# 顺序抽样方法
num_epochs, lr = 500, 1
# train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu())
# plt.show()
"""
困惑度 1.0, 95138.3 词元/秒 cuda:0
time traveller you can show black is white by argument said filby
traveller you can show black is white by argument said filby
"""

# 随机抽样方法
net = RNNModelScratch(len(vocab), num_hiddens, d2l.try_gpu(), get_params,
                      init_rnn_state, rnn)
train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu(),
          use_random_iter=True)
plt.show()
"""
困惑度 1.3, 109268.9 词元/秒 cuda:0
time traveller held in his hand was a glitteringmetallic framewor
traveller held in his hand was a glitteringmetallic framewor
"""

顺序抽样:

随机抽样:

2.简洁实现

import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
import matplotlib.pyplot as plt

# 加载时光机器数据集并设置批量大小和序列长度
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)

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

# 用零张量初始化隐藏状态
state = torch.zeros((1, batch_size, num_hiddens))
# print(state.shape) # torch.Size([1, 32, 256])

# X = torch.rand(size=(num_steps, batch_size, len(vocab)))
# Y, state_new = rnn_layer(X, state)
# print(Y.shape, state_new.shape, X.shape)
# torch.Size([35, 32, 256]) torch.Size([1, 32, 256]) torch.Size([35, 32, 28])

# 完整的循环神经网络模型定义了一个RNNModel类
#@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，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))

# 训练与预测

device = d2l.try_gpu()
net = RNNModel(rnn_layer, vocab_size=len(vocab))
net = net.to(device)
num_epochs, lr = 500, 1
d2l.train_ch8(net, train_iter, vocab, lr, num_epochs, device)
"""
perplexity 1.3, 236379.1 tokens/sec on cuda:0
time traveller held in his hand was a glitteringmetallic framewo
traveller fith a slan but move anotle bothe thon st stagee 
"""
plt.show()
print(d2l.predict_ch8('time traveller', 10, net, vocab, device))
# time traveller held in h