在具有不同隐藏尺寸的Pytorch LSTM模型上附加一个循环层

如果我没弄错的话,可以这样做:

import torch.nn as nn
import torch.nn.functional as F

class RnnWith2HiddenSizesModel(nn.Module):
    def __init__(self):
        super(RnnWith2HiddenSizesModel, self).__init__()
        self.rnn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2)
        self.rnn_two = nn.LSTM(input_size=20, hidden_size=2)

    def forward(self, inp, hc):
        output, _ = self.rnn(inp, hc)
        output2, _ = self.rnn_two(output)
        return output2


inp = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
c0 = torch.randn(2, 3, 20)

rnn = RnnWith2HiddenSizesModel()

output = RnnWith2HiddenSizesModel()(inp, (h0, c0))


tensor([[[-0.0305,  0.0327],
     [-0.1534, -0.1193],
     [-0.1393,  0.0474]],

    [[-0.0370,  0.0519],
     [-0.2081, -0.0693],
     [-0.1809,  0.0826]],

    [[-0.0561,  0.0731],
     [-0.2307, -0.0229],
     [-0.1780,  0.0901]],

    [[-0.0612,  0.0891],
     [-0.2378,  0.0164],
     [-0.1760,  0.0929]],

    [[-0.0660,  0.1023],
     [-0.2176,  0.0508],
     [-0.1611,  0.1053]]], grad_fn=<CatBackward>)

虽然@mikhail berlinkov的答案是根据需要有效的,但它并不是一般情况(问题中甚至没有要求),对此我想提出第二个解决方案:

import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import reduce

class RNNModel(nn.Module):
    def __init__(self, *models):
        super(RNNModel, self).__init__()
        self.models = models

    def forward(self, inp):
        return reduce(lambda arg, model: model(arg, None)[0], self.models, inp)

可称为:

rnn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2)
rnn_two = nn.LSTM(input_size=20, hidden_size=2)
inp = torch.randn(5, 3, 10)

rnn_model = RNNModel(rnn, rnn_two)

output = rnn_model(inp)

output.shape 等于预期值(即, 5,3,2 )