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| import torch.nn as nn
import torch
# gru = nn.GRU(input_size=50, hidden_size=50, batch_first=True)
# embed = nn.Embedding(3, 50)
# x = torch.LongTensor([[0, 1, 2]])
# x_embed = embed(x)
# out, hidden = gru(x_embed)
gru = nn.GRU(input_size=5, hidden_size=6,
num_layers=2, # gru层数
batch_first=False, # 默认参数 True:(batch, seq, feature) False：True:( seq,batch, feature),
bidirectional=False, # 默认参数
)
# N=batch size
# L=sequence length
# D=2 if bidirectional=True else 1
# Hin=input size
# Hout=outout size
input_ = torch.randn(1, 3, 5) # (L,N,hin)(序列长度，batch size大小，输入维度大小)
h0 = torch.randn(2 * 1, 3, 6) # (D∗num_layers,N,Hout)（是否双向乘以层数，batch size大小，输出维度大小）
output, hn = gru(input_, h0)
# output:[1, 3, 6] (L,N,D*Hout)=(1,3,1*6)
# hn:[2, 3, 6] (D*num_layers,N,Hout)(1*2,3,6)
print(output.shape, hn.shape)
# torch.Size([1, 3, 6]) torch.Size([2, 3, 6])
样例二
# 创建一个输入输入50维的GRU
gru = nn.GRU(input_size=50, hidden_size=50, batch_first=True)
# 创建一个字典大小为3，词向量维度为50维的Embedding。
embed = nn.Embedding(3, 50)
# 创建一个二维LongTensor索引数据（作为索引数据的时候一般都使用LongTensor）
x = torch.LongTensor([[0, 1, 2]]) # x.size() --> torch.Size([1, 3])
# 将索引映射到向量
x_embed = embed(x) # x_embed.size() --> torch.Size([1, 3, 50])
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