RNN循环神经网络python实现
import collections
import math
import re
import random
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
def read_txt():
# 读取文本数据
with open('./A Study in ', 'r', encoding='utf-8') as f:
# 读取每一行
lines = f.readlines()
# 将不是英文字符的转换为空格，全部变为小写字符返回
return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]
def tokenize(lines, token='word'):
# 将文本以空格进行分割成词
if token == 'word':
return [line.split() for line in lines]
# 将文本分割成字符
elif token == 'char':
return [list(line) for line in lines]
else:
print('错误：未知令牌类型：' + token)
def count_corpus(tokens):
# 如果tokens长度为0或tokens[0]是list
if len(tokens) == 0 or isinstance(tokens[0], list):
# 将[[,,,],[,,,]]多层结构变为一层结构[,,,,]
tokens = [token for line in tokens for token in line]
# 统计可迭代对象中元素出现的次数，并返回一个字典（key-value）key 表示元素，value 表示各元素 key 出现的次数
return collections.Counter(tokens)
# idx_to_token 是一个list 由token作为元素构成['<unk>', ' ', 'e', 't', 'a', 'o', 'h', 'n', 'i', 's', 'r', 'd', 'l', 'u', 'f', 'w', 'g', 'm', 'y', 'c', 'p', 'b', 'k', 'v', 'j', 'x', 'z', 'q']
# token_freqs 是一个list 由token和该token出现的次数构成的元组作为元素构成[(' ', 94824), ('e', 54804), ('t', 38742), ('a', 33172), ('o', 30656), ('h', 29047), ('n', 28667), ('i', 28093), ('s', 27922), ('r', 26121), ('d', 20394), ('l', 17755), ('u', 12267), ('f', 11033), ('w', 10033), ('g', 9837), ('m', 9258), ('y', 9251), ('c', 8872), ('p', 6998), ('b', 6620), ('k', 4817), ('v', 3574), ('j', 500), ('x', 372), ('z', 308), ('q', 285)]
# token_to_idx 是一个dict 由token作为key token在idx_to_token的索引作为value构成{' ': 1, '<unk>': 0, 'a': 4, 'b': 21, 'c': 19, 'd': 11, 'e': 2, 'f': 14, 'g': 16, 'h': 6, 'i': 8, 'j': 24, 'k': 22, 'l': 12, 'm': 17, 'n': 7, 'o': 5, 'p': 20, 'q': 27, 'r': 10, 's': 9, 't': 3, 'u': 13, 'v': 23, 'w': 15, 'x': 25, 'y': 18, 'z': 26}
class Vocab:
def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):
# 处理特殊情况
if tokens is None:
tokens = []
# 处理特殊情况
if reserved_tokens is None:
reserved_tokens = []
# counter为一个字典（key-value）key 表示元素，value 表示各元素 key 出现的次数
counter = count_corpus(tokens)
# 排序
# iterable：待排序的序列()
# key：排序规则lambda x: x[1]从小到大
# reverse：指定排序的方式，默认值False，即升序排列，这是True也就是降序
self.token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True)
# 初始化
self.unk, uniq_tokens = 0, ['<unk>'] + reserved_tokens
# 初始化 token_freqs中 key不在 uniq_tokens中 且 value大于min_freq 返回token放入uniq_tokens
uniq_tokens += [
token for token, freq in self.token_freqs
if freq >= min_freq and token not in uniq_tokens]
# 初始化
self.idx_to_token, self.token_to_idx = [], dict()
# 赋值
for token in uniq_tokens:
self.idx_to_token.append(token)
self.token_to_idx[token] = len(self.idx_to_token) - 1
def __len__(self):
return len(self.idx_to_token)
def __getitem__(self, tokens):
if not isinstance(tokens, (list, tuple)):
return self.token_to_idx.get(tokens, self.unk)
return [self.__getitem__(token) for token in tokens]
def to_tokens(self, indices):
if not isinstance(indices, (list, tuple)):
return self.idx_to_token[indices]
return [self.idx_to_token[index] for index in indices]
def load_corpus_time_machine(max_tokens=-1):
# 将文本处理成行
lines = read_txt()
# print(lines)
# 将行tokens化
tokens = tokenize(lines, 'char')
# print(tokens)
# 构建字典表
vocab = Vocab(tokens)
# vocab的格式为{list:524222}[5, 7, 2, 5, 7, 2, 8, 3, ......, 1, 18, 5, 13]
#print(vocab)
corpus = [vocab[token] for line in tokens for token in line]
if max_tokens > 0:
corpus = corpus[:max_tokens]
return corpus, vocab
# 随机地生成一个小批量数据的特征和标签以供读取。 在随机采样中，每个样本都是在原始的长序列上任意捕获的子序列
def seq_data_iter_random(corpus, batch_size, num_steps):
"""使用随机抽样生成一个小批量子序列。"""
corpus = corpus[random.randint(0, num_steps - 1):]
num_subseqs = (len(corpus) - 1) // num_steps
initial_indices = list(range(0, num_subseqs * num_steps, num_steps))
random.shuffle(initial_indices)
def data(pos):
return corpus[pos:pos + num_steps]
num_batches = num_subseqs // batch_size
for i in range(0, batch_size * num_batches, batch_size):
initial_indices_per_batch = initial_indices[i:i + batch_size]
X = [data(j) for j in initial_indices_per_batch]
Y = [data(j + 1) for j in initial_indices_per_batch]
yield torch.tensor(X), torch.tensor(Y)
# 保证两个相邻的小批量中的子序列在原始序列上也是相邻的
def seq_data_iter_sequential(corpus, batch_size, num_steps):
"""使用顺序分区生成一个小批量子序列。"""
offset = random.randint(0, num_steps)
num_tokens = ((len(corpus) - offset - 1) // batch_size) * batch_size
Xs = torch.tensor(corpus[offset:offset + num_tokens])
Ys = torch.tensor(corpus[offset + 1:offset + 1 + num_tokens])
Xs, Ys = Xs.reshape(batch_size, -1), Ys.reshape(batch_size, -1)
num_batches = Xs.shape[1] // num_steps
for i in range(0, num_steps * num_batches, num_steps):
X = Xs[:, i:i + num_steps]
Y = Ys[:, i:i + num_steps]
yield X, Y
class SeqDataLoader:
"""加载序列数据的迭代器。"""
def __init__(self, batch_size, num_steps, use_random_iter, max_tokens):
if use_random_iter:
self.data_iter_fn = seq_data_iter_random
else:
self.data_iter_fn = seq_data_iter_sequential
self.corpus, self.vocab = load_corpus_time_machine(max_tokens)
self.batch_size, self.num_steps = batch_size, num_steps
def __iter__(self):
return self.data_iter_fn(self.corpus, self.batch_size, self.num_steps)
def load_data_time_machine(batch_size, num_steps,
use_random_iter=False, max_tokens=10000):
"""返回时光机器数据集的迭代器和词汇表。"""
data_iter = SeqDataLoader(batch_size, num_steps, use_random_iter,
max_tokens)
return data_iter, data_iter.vocab
# 初始化模型参数
def get_params(vocab_size, num_hiddens, device):
# 输入等于输出等于字典大小
num_inputs = num_outputs = vocab_size
# 均值为0方差为1的随机张量*0.01
def normal(shape):
return torch.randn(size=shape, device=device) * 0.01
# 输入到隐藏层边缘的W
W_xh = normal((num_inputs, num_hiddens))
# 隐藏层的W
W_hh = normal((num_hiddens, num_hiddens))
b_h = torch.zeros(num_hiddens, device=device)
# 隐藏层到输出的W
W_hq = normal((num_hiddens, num_outputs))
b_q = torch.zeros(num_outputs, device=device)
params = [W_xh, W_hh, b_h, W_hq, b_q]
for param in params:
param.requires_grad_(True)
return params
# 初始化隐藏状态
def init_rnn_state(batch_size, num_hiddens, device):
# 批量大小，隐藏层大小的全0张量
return (torch.zeros((batch_size, num_hiddens), device=device),)
# 计算输出
def rnn(inputs, state, params):
W_xh, W_hh, b_h, W_hq, b_q = params
H, = state
outputs = []
for X in inputs:
# 激活函数是tanh H为初始化隐藏状态
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)
# H为当前隐藏状态
return torch.cat(outputs, dim=0), (H,)
class RNNModelScratch:
"""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):
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):
return self.init_state(batch_size, self.num_hiddens, device)
# 推理测试
def predict_ch8(prefix, num_preds, net, vocab, device):
"""在`prefix`后面生成新字符。"""
state = net.begin_state(batch_size=1, device=device)
outputs = [vocab[prefix[0]]]
get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape(
(1, 1))
for y in prefix[1:]:
_, state = net(get_input(), state)
outputs.append(vocab[y])
for _ in range(num_preds):
y, state = net(get_input(), state)
outputs.append(int(y.argmax(dim=1).reshape(1)))
return ''.join([vocab.idx_to_token[i] for i in outputs])
# 梯度剪裁
def grad_clipping(net, theta):
"""裁剪梯度。"""
if isinstance(net, nn.Module):
params = [p for p in net.parameters() if p.requires_grad]
else:
params = net.params
norm = torch.sqrt(sum(torch.sum((p.grad**2)) for p in params))
if norm > theta:
for param in params:
param.grad[:] *= theta / norm
# 训练函数
def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):
"""训练模型一个迭代周期（定义见第8章）。"""
state = None
metric = d2l.Accumulator(2)
for X, Y in train_iter:
if state is None or use_random_iter:
state = net.begin_state(batch_size=X.shape[0], device=device)
else:
if isinstance(net, nn.Module) and not isinstance(state, tuple):
state.detach_()
else:
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)
updater(batch_size=1)
metric.add(l * y.numel(), y.numel())
return math.exp(metric[0] / metric[1])
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()
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)
for epoch in range(num_epochs):
ppl = train_epoch_ch8(net, train_iter, loss, updater, device,
use_random_iter)
if (epoch + 1) % 10 == 0:
print(predict('But'))
print(f'困惑度 {ppl:.1f}, {str(device)}')
print(predict('But'))
# 批量大小为32 时序序列的长度为35 隐藏层大小512
batch_size, num_steps, num_hiddens = 32, 35, 512
# 获取迭代数据和字典
train_iter, vocab = load_data_time_machine(batch_size, num_steps)
# 定义网络
net = RNNModelScratch(len(vocab), num_hiddens, torch.device('cpu'), get_params,
init_rnn_state, rnn)
# 训练500轮 学习率为1
num_epochs, lr = 50, 1
# 训练
train_ch8(net, train_iter, vocab, lr, num_epochs, torch.device('cpu'),
use_random_iter=True)