1.LSTM基础

长短期记忆网络（Long Short-Term Memory，简称LSTM），是RNN的一种，为了解决RNN存在长期依赖问题而设计出来的。

LSTM的基本结构：

2.LSTM的具体说明

LSTM与RNN的结构相比，在参数更新的过程中，增加了三个门，由左到右分别是遗忘门（也称记忆门）、输入门、输出门。

图片来源：

LSTM的工作原理究竟是什么？深入了解LSTM-电子发烧友网

1.点乘操作决定多少信息可以传送过去，当为0时，不传送；当为1时，全部传送。

2.1 遗忘门

对于输入xt和ht-1，遗忘门会输出一个值域为[0, 1]的数字，放进Ct−1中。当为0时，全部删除；当为1时，全部保留。

2.2 输入门

对于对于输入xt和ht-1，输入门会选择信息的去留，并且通过tanh激活函数更新临时Ct

通过遗忘门和输入门输出累加，更新最终的Ct

2.3输出门

通过Ct和输出门，更新memory

3.PyTorch的LSTM使用方法

1. __ init __(input _ size, hidden_size,num _layers)

2. LSTM.foward():

​ out,[ht,ct] = lstm(x,[ht-1,ct-1])

​ x:[一句话单词数，batch几句话，表示的维度]

​ h/c:[层数，batch，记忆(参数)的维度]

​ out:[一句话单词数，batch，参数的维度]

	import torch
	import torch.nn as nn
	lstm = nn.LSTM(input_size = 100,hidden_size = 20,num_layers = 4)
	print(lstm)
	#LSTM(100, 20, num_layers=4)
	x = torch.randn(10,3,100)
	out,(h,c)=lstm(x)
	print(out.shape,h.shape,c.shape)
	#torch.Size([10, 3, 20]) torch.Size([4, 3, 20]) torch.Size([4, 3, 20])

单层使用方法：

	cell = nn.LSTMCell(input_size = 100,hidden_size=20)
	x = torch.randn(10,3,100)
	h = torch.zeros(3,20)
	c = torch.zeros(3,20)
	for xt in x:
	h,c = cell(xt,[h,c])
	print(h.shape,c.shape)
	#torch.Size([3, 20]) torch.Size([3, 20])

LSTM实战--情感分类问题

Google CoLab环境，需要魔法。

	import torch
	from torch import nn, optim
	from torchtext import data, datasets
	print('GPU:', torch.cuda.is_available())
	torch.manual_seed(123)
	TEXT = data.Field(tokenize='spacy')
	LABEL = data.LabelField(dtype=torch.float)
	train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
	print('len of train data:', len(train_data))
	print('len of test data:', len(test_data))
	print(train_data.examples[15].text)
	print(train_data.examples[15].label)
	# word2vec, glove
	TEXT.build_vocab(train_data, max_size=10000, vectors='glove.6B.100d')
	LABEL.build_vocab(train_data)
	batchsz = 30
	device = torch.device('cuda')
	train_iterator, test_iterator = data.BucketIterator.splits(
	(train_data, test_data),
	batch_size = batchsz,
	device=device
	)
	class RNN(nn.Module):
	def __init__(self, vocab_size, embedding_dim, hidden_dim):
	"""
	"""
	super(RNN, self).__init__()
	# [0-10001] => [100]
	self.embedding = nn.Embedding(vocab_size, embedding_dim)
	# [100] => [256]
	self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=2,
	bidirectional=True, dropout=0.5)
	# [256*2] => [1]
	self.fc = nn.Linear(hidden_dim*2, 1)
	self.dropout = nn.Dropout(0.5)
	def forward(self, x):
	"""
	x: [seq_len, b] vs [b, 3, 28, 28]
	"""
	# [seq, b, 1] => [seq, b, 100]
	embedding = self.dropout(self.embedding(x))
	# output: [seq, b, hid_dim*2]
	# hidden/h: [num_layers*2, b, hid_dim]
	# cell/c: [num_layers*2, b, hid_di]
	output, (hidden, cell) = self.rnn(embedding)
	# [num_layers*2, b, hid_dim] => 2 of [b, hid_dim] => [b, hid_dim*2]
	hidden = torch.cat([hidden[-2], hidden[-1]], dim=1)
	# [b, hid_dim*2] => [b, 1]
	hidden = self.dropout(hidden)
	out = self.fc(hidden)
	return out
	rnn = RNN(len(TEXT.vocab), 100, 256)
	pretrained_embedding = TEXT.vocab.vectors
	print('pretrained_embedding:', pretrained_embedding.shape)
	rnn.embedding.weight.data.copy_(pretrained_embedding)
	print('embedding layer inited.')
	optimizer = optim.Adam(rnn.parameters(), lr=1e-3)
	criteon = nn.BCEWithLogitsLoss().to(device)
	rnn.to(device)
	import numpy as np
	def binary_acc(preds, y):
	"""
	get accuracy
	"""
	preds = torch.round(torch.sigmoid(preds))
	correct = torch.eq(preds, y).float()
	acc = correct.sum() / len(correct)
	return acc
	def train(rnn, iterator, optimizer, criteon):
	avg_acc = []
	rnn.train()
	for i, batch in enumerate(iterator):
	# [seq, b] => [b, 1] => [b]
	pred = rnn(batch.text).squeeze(1)
	#
	loss = criteon(pred, batch.label)
	acc = binary_acc(pred, batch.label).item()
	avg_acc.append(acc)
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()
	if i%10 == 0:
	print(i, acc)
	avg_acc = np.array(avg_acc).mean()
	print('avg acc:', avg_acc)
	def eval(rnn, iterator, criteon):
	avg_acc = []
	rnn.eval()
	with torch.no_grad():
	for batch in iterator:
	# [b, 1] => [b]
	pred = rnn(batch.text).squeeze(1)
	#
	loss = criteon(pred, batch.label)
	acc = binary_acc(pred, batch.label).item()
	avg_acc.append(acc)
	avg_acc = np.array(avg_acc).mean()
	print('>>test:', avg_acc)
	for epoch in range(10):
	eval(rnn, test_iterator, criteon)
	train(rnn, train_iterator, optimizer, criteon)