在PyTorch中准备序列的解码器以对网络进行排序

Question

我在Pytorch中使用Sequence to Sequence模型.序列到序列模型包括编码器和解码器.

编码器转换a (batch_size X input_features X num_of_one_hot_encoded_classes) -> (batch_size X input_features X hidden_size)

解码器将采用此输入序列并将其转换为 (batch_size X output_features X num_of_one_hot_encoded_classes)

一个例子是 -

所以在上面的例子中,我需要将22个输入功能转换为10个输出功能.在Keras中,可以使用RepeatVector(10)完成.

一个例子 -

model.add(LSTM(256, input_shape=(22, 98)))
model.add(RepeatVector(10))
model.add(Dropout(0.3))
model.add(LSTM(256, return_sequences=True))

虽然,我不确定它是否是将输入序列转换为输出序列的正确方法.

所以,我的问题是 -

• 将输入序列转换为输出序列的标准方法是什么.例如.转换自(batch_size,22,98) - >(batch_size,10,98)？或者我应该如何准备解码器？

编码器代码片段(用Pytorch编写) -

class EncoderRNN(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(EncoderRNN, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,
          num_layers=1, batch_first=True)

    def forward(self, input):
        output, hidden = self.lstm(input)
        return output, hidden

Answer 1

好吧，你必须选择，第一个是重复编码器的最后状态 10 次并将其作为解码器的输入，如下所示：

import torch
input = torch.randn(64, 22, 98)
encoder = torch.nn.LSTM(98, 256, batch_first=True)
encoded, _ = encoder(input)
decoder_input = encoded[:, -1:].repeat(1, 10, 1)
decoder = torch.nn.LSTM(256, 98, batch_first=True)
decoded, _ = decoder(decoder_input)
print(decoded.shape) #torch.Size([64, 10, 98])

另一种选择是使用注意力机制，如下所示：

#assuming we have obtained the encoded sequence and declared the decoder as before
attention_calculator = torch.nn.Conv1d(256+98, 1, kernel_size=1)
hidden = (torch.zeros(1, 64, 98), torch.zeros(1, 64, 98))
outputs = []
for i in range(10):
    attention_input = torch.cat([hidden[0][0][:, None, :].expand(-1, 22, -1), encoded], dim=2).permute(0, 2, 1)
    attention_value = torch.nn.functional.softmax(attention_calculator(attention_input).squeeze(), dim=1)
    decoder_input = (attention_value[:, :, None] * encoded).sum(dim=1, keepdim=True)
    output, hidden = decoder(decoder_input, hidden)
    outputs.append(output)
outputs = torch.cat(outputs, dim=1)