batch_size, input_size, sequence_size = static.size()
时间: 2024-03-31 21:25:35 浏览: 20
好的,你的问题是:batch_size, input_size, sequence_size = static.size() 的含义是什么?
这行代码的含义是将一个名为 static 的 tensor 的尺寸(size)分别赋值给 batch_size、input_size 和 sequence_size 变量。具体来说,static.size() 返回一个包含三个元素的元组,分别表示 static tensor 的批次数(batch_size)、输入维度(input_size)和序列长度(sequence_size)。
相关问题
这段代码哪里错了 class my_BiGRU(torch.nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )
这段代码存在两个问题:
1.函数 `_setup_BiGRU_layers` 中的代码应该是在类的构造函数 `__init__` 中进行初始化,而不是在另外一个函数中进行初始化。
2.在调用 `my_BiGRU` 类时,参数名称有误,应该将 `apsule_out_features` 改为 `capsule_out_features`。
改正后的代码如下所示:
```
class my_BiGRU(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout, capsule_out_features):
super(my_BiGRU, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.gru_dropout = gru_dropout
self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True)
self.fc = torch.nn.Linear(hidden_size * 2, output_size)
self.BiGRU_layers = my_BiGRU(input_size=self.input_size,
output_size=capsule_out_features,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
gru_dropout=self.gru_dropout,
)
```
注意:这里假设 `capsule_out_dim` 和 `args` 都已经在代码中被定义好了。
class Net(nn.Module): def __init__(self,input_size,hidden_size,num_layers,output_size,batch_size,seq_length) -> None: super(Net,self).__init__() self.input_size=input_size self.hidden_size=hidden_size self.num_layers=num_layers self.output_size=output_size self.batch_size=batch_size self.seq_length=seq_length self.num_directions=1 # 单向LSTM self.lstm=nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True) # LSTM层 self.fc=nn.Linear(hidden_size,output_size) # 全连接层 def forward(self,x): # e.g. x(10,3,100) 三个句子,十个单词,一百维的向量,nn.LSTM(input_size=100,hidden_size=20,num_layers=4) # out.shape=(10,3,20) h/c.shape=(4,b,20) batch_size, seq_len = x.size()[0], x.size()[1] # x.shape=(604,3,3) h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) # output(batch_size, seq_len, num_directions * hidden_size) output, _ = self.lstm(x, (h_0, c_0)) # output(5, 30, 64) pred = self.fc(output) # (5, 30, 1) pred = pred[:, -1, :] # (5, 1) return pred改成python代码
这段代码是一个简单的使用 PyTorch 实现的 LSTM 模型。下面是将其转化为 Python 代码的版本:
```python
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, seq_length):
super(Net, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.batch_size = batch_size
self.seq_length = seq_length
self.num_directions = 1 # 单向LSTM
# LSTM层
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
# 全连接层
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
# x.shape=(10,3,100),三个句子,十个单词,一百维的向量
# out.shape=(10,3,20),h/c.shape=(4,b,20)
batch_size, seq_len = x.size()[0], x.size()[1]
h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
output, _ = self.lstm(x, (h_0, c_0)) # output(batch_size, seq_len, num_directions * hidden_size)
pred = self.fc(output) # (5, 30, 1)
pred = pred[:, -1, :] # (5, 1)
return pred
```