|
・In Japanese
■Description of RNN and LSTM functions
Calculate Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)
■How to use the RNN function
import torch
import torch.nn as nn
class CalRNN(nn.Module):
def __init__(self , in_size , hidden , output , layer):
super().__init__()
self.rnn = nn.RNN(in_size, hidden, layer, batch_first=True, nonlinearity='relu') #Default is 'tanh'
self.fc = nn.Linear(hidden, output)
def forward(self, x):
x_rnn, _ = self.rnn(x, None)
y = self.fc(x_rnn[:, -1, :])
return y
model_rnn = CalRNN(10, 64, 1, 2) #input:10, middle layer:64, output:1, 2 layers
x = torch.randn(5,1,10) #batch size:5、input:10
ans = model_rnn(x)
print(ans)
⇒tensor([[-0.0328],
[ 0.0598],
[-0.1709],
[ 0.0603],
[-0.1373]], grad_fn=＜AddmmBackward＞)
The "forward" part above is equivalent to the red part below. It can be confirmed that the above description and the result match.
def forward(self, x):
x_rnn, _ = self.rnn(x, None) # For comparison
y = self.fc(x_rnn[:, -1, :]) # For comparison
h0 = torch.zeros(2, 5, 64) # layer, batch size, middle layer
x2_rnn, h = self.rnn(x, h0)
y2 = self.fc(x2_rnn[:, -1, :])
return y , y2
■How to use the LSTM function
Change the "RNN" part to "LSTM" and remove the nonlinearity part. (only tanh can be used)
import torch
import torch.nn as nn
class CalRNN(nn.Module):
def __init__(self , in_size , hidden , output , layer):
super().__init__()
self.rnn = nn.LSTM(in_size, hidden, layer, batch_first=True) #Default is 'tanh'
self.fc = nn.Linear(hidden, output)
def forward(self, x):
x_rnn, _ = self.rnn(x, None)
y = self.fc(x_rnn[:, -1, :])
return y
model_rnn = CalRNN(10, 64, 1, 2) #input:10, middle layer:64, output:1, 2 layers
x = torch.randn(5,1,10) #batch size:5、input:10
ans = model_rnn(x)
print(ans)
⇒tensor([[0.0127],
[0.0224],
[0.0103],
[0.0214],
[0.0134]], grad_fn=＜AddmmBackward＞)
|
|
TOP MENU