54循环神经网络的从零开始实现
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:
55循环神经网络的简洁实现
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
