标签：import dir fir 练习 color conda highlight lstm ast
单向LSTM
import torch.nn as nn import torch seq_len = 20 batch_size = 64 embedding_dim = 100 num_embeddings = 300 hidden_size = 128 number_layer = 3 input = torch.randint(low=0,high=256,size=[batch_size,seq_len]) #[64,20] embedding = nn.Embedding(num_embeddings,embedding_dim) input_embeded = embedding(input) #[64,20,100] #转置，变换batch_size 和seq_len # input_embeded = input_embeded.transpose(0,1) # input_embeded = input_embeded.permute(1,0,2) #实例化lstm lstm = nn.LSTM(input_size=embedding_dim,hidden_size=hidden_size,batch_first=True,num_layers=number_layer) output,(h_n,c_n) = lstm(input_embeded) print(output.size()) #[64,20,128] [batch_size,seq_len,hidden_size] print(h_n.size()) #[3,64,128] [number_layer,batch_size,hidden_size] print(c_n.size()) #同上 #获取最后时间步的output output_last = output[:,-1,:] #获取最后一层的h_n h_n_last = h_n[-1] print(output_last.size()) print(h_n_last.size()) #最后的output等于最后一层的h_n print(output_last.eq(h_n_last))
D:\anaconda\python.exe C:/Users/liuxinyu/Desktop/pytorch_test/day4/LSTM练习.py
torch.Size([64, 20, 128])
torch.Size([3, 64, 128])
torch.Size([3, 64, 128])
torch.Size([64, 128])
torch.Size([64, 128])
tensor([[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]])
Process finished with exit code 0
双向LSTM
import torch.nn as nn import torch seq_len = 20 batch_size = 64 embedding_dim = 100 num_embeddings = 300 hidden_size = 128 number_layer = 3 input = torch.randint(low=0,high=256,size=[batch_size,seq_len]) #[64,20] embedding = nn.Embedding(num_embeddings,embedding_dim) input_embeded = embedding(input) #[64,20,100] #转置，变换batch_size 和seq_len # input_embeded = input_embeded.transpose(0,1) # input_embeded = input_embeded.permute(1,0,2) #实例化lstm lstm = nn.LSTM(input_size=embedding_dim,hidden_size=hidden_size,batch_first=True,num_layers=number_layer,bidirectional=True) output,(h_n,c_n) = lstm(input_embeded) print(output.size()) #[64,20,128*2] [batch_size,seq_len,hidden_size] print(h_n.size()) #[3*2,64,128] [number_layer,batch_size,hidden_size] print(c_n.size()) #同上 #获取反向的最后一个output output_last = output[:,0,-128:] #获反向最后一层的h_n h_n_last = h_n[-1] print(output_last.size()) print(h_n_last.size()) # 反向最后的output等于最后一层的h_n print(output_last.eq(h_n_last)) #获取正向的最后一个output output_last = output[:,-1,:128] #获取正向最后一层的h_n h_n_last = h_n[-2] # 反向最后的output等于最后一层的h_n print(output_last.eq(h_n_last))
D:\anaconda\python.exe C:/Users/liuxinyu/Desktop/pytorch_test/day4/双向LSTM练习.py
torch.Size([64, 20, 256])
torch.Size([6, 64, 128])
torch.Size([6, 64, 128])
torch.Size([64, 128])
torch.Size([64, 128])
tensor([[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]])
tensor([[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
...,
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True],
[True, True, True, ..., True, True, True]])
Process finished with exit code 0
pytorch 中LSTM模型获取最后一层的输出结果，单向或双向
标签：import dir fir 练习 color conda highlight lstm ast
原文地址：https://www.cnblogs.com/LiuXinyu12378/p/12322993.html
