Question

前提1：

关于RNN层中的神经元-我的理解是，“在每个时间步长，每个神经元都从前一个时间步长y（t –1）接收输入向量x（t）和输出向量” > [1] ：

前提2：

据我了解，在Pytorch的GRU层中， input_size 和 hidden_size 的含义如下：

input_size –输入x中的预期功能数量

hidden_size –处于隐藏状态h的要素数量

很自然， hidden_size 应该代表GRU层中神经元的数量。

我的问题：

给出以下GRU层：

# assume that hidden_size = 3

class Encoder(nn.Module):
    def __init__(self, src_dictionary_size, hidden_size):
        super(Encoder, self).__init__()
        self.embedding = nn.Embedding(src_dictionary_size, hidden_size)
        self.gru = nn.GRU(input_size = hidden_size, hidden_size = hidden_size)

假设hidden_size为3，我的理解是，上面的GRU层将具有3个神经元，每个神经元在每个时间步同时接受大小为3的输入向量。

我的问题是：为什么 hidden_size 和 input_size 的参数必须相等？即为什么3个神经元中的每个神经元都不能接受5个输入向量？

关键点：以下两个产品尺寸不匹配：

self.gru = nn.GRU(input_size = hidden_size, hidden_size = hidden_size-1)
self.gru = nn.GRU(input_size = hidden_size, hidden_size = hidden_size+1)

[1]盖伦，欧瑞莲。使用Scikit-Learn和TensorFlow进行动手机器学习（第388页）。 O'Reilly Media。 Kindle版。

[3] https://pytorch.org/docs/stable/nn.html#torch.nn.GRU

添加完整代码以提高可重复性：

import torch
import torch.nn as nn

class Encoder(nn.Module):
    def __init__(self, src_dictionary_size, hidden_size):
        super(Encoder, self).__init__()
        self.hidden_size = hidden_size
        self.embedding = nn.Embedding(src_dictionary_size, hidden_size)
        self.gru = nn.GRU(input_size = hidden_size, hidden_size = hidden_size-1)

    def forward(self, pad_seqs, seq_lengths, hidden):
        """
        Args:
          pad_seqs of shape (max_seq_length, batch_size, 1): Padded source sequences.
          seq_lengths: List of sequence lengths.
          hidden of shape (1, batch_size, hidden_size): Initial states of the GRU.

        Returns:
          outputs of shape (max_seq_length, batch_size, hidden_size): Padded outputs of GRU at every step.
          hidden of shape (1, batch_size, hidden_size): Updated states of the GRU.
        """
        embedded_sqs = self.embedding(pad_seqs).squeeze(2)
        packed_sqs = pack_padded_sequence(embedded_sqs, seq_lengths)
        packed_output, h_n = self.gru(packed_sqs, hidden)
        output, input_sizes = pad_packed_sequence(packed_output)

        return output, h_n

    def init_hidden(self, batch_size=1):
        return torch.zeros(1, batch_size, self.hidden_size)

def test_Encoder_shapes():
    hidden_size = 5
    encoder = Encoder(src_dictionary_size=5, hidden_size=hidden_size)

    # maximum word count
    max_seq_length = 4

    # num sentences
    batch_size = 2
    hidden = encoder.init_hidden(batch_size=batch_size)

    # these are padded sequences (sentences of words). There are 2 sentences (i.e. 2 batches) with a maximum of 4 words.
    pad_seqs = torch.tensor([
        [1, 2],
        [2, 3],
        [3, 0],
        [4, 0]
    ]).view(max_seq_length, batch_size, 1)

    outputs, new_hidden = encoder.forward(pad_seqs=pad_seqs, seq_lengths=[4, 2], hidden=hidden)
    assert outputs.shape == torch.Size([4, batch_size, hidden_size]), f"Bad outputs.shape: {outputs.shape}"
    assert new_hidden.shape == torch.Size([1, batch_size, hidden_size]), f"Bad new_hidden.shape: {new_hidden.shape}"
    print('Success')

test_Encoder_shapes()

Answer 1

我刚刚解决了这个问题，而这个错误是自我造成的。

结论： input_size 和 hidden_size 的大小可以不同，并且这没有固有的问题。问题中的前提已正确说明。

上面（完整）代码的问题是GRU的初始隐藏状态没有正确的尺寸。初始隐藏状态必须具有与后续隐藏状态相同的尺寸。在我的情况下，初始隐藏状态的形状为（1,2,5）而不是（1,2,4）。在前者中，5表示嵌入向量的维数。 4表示GRU中的hidden_size（神经元数）。正确的代码如下：

import torch
import torch.nn as nn

class Encoder(nn.Module):
    def __init__(self, src_dictionary_size, input_size, hidden_size):
        super(Encoder, self).__init__()
        self.hidden_size = hidden_size
        self.embedding = nn.Embedding(src_dictionary_size, input_size)
        self.gru = nn.GRU(input_size = input_size, hidden_size = hidden_size)

    def forward(self, pad_seqs, seq_lengths, hidden):
        """
        Args:
          pad_seqs of shape (max_seq_length, batch_size, 1): Padded source sequences.
          seq_lengths: List of sequence lengths.
          hidden of shape (1, batch_size, hidden_size): Initial states of the GRU.

        Returns:
          outputs of shape (max_seq_length, batch_size, hidden_size): Padded outputs of GRU at every step.
          hidden of shape (1, batch_size, hidden_size): Updated states of the GRU.
        """
        embedded_sqs = self.embedding(pad_seqs).squeeze(2)
        packed_sqs = pack_padded_sequence(embedded_sqs, seq_lengths)
        packed_output, h_n = self.gru(packed_sqs, hidden)
        output, input_sizes = pad_packed_sequence(packed_output)

        return output, h_n

    def init_hidden(self, batch_size=1):
        return torch.zeros(1, batch_size, self.hidden_size)

def test_Encoder_shapes():
    hidden_size = 4
    embedding_size = 5
    encoder = Encoder(src_dictionary_size=5, input_size = embedding_size, hidden_size = hidden_size)
    print(encoder)

    max_seq_length = 4
    batch_size = 2
    hidden = encoder.init_hidden(batch_size=batch_size)
    pad_seqs = torch.tensor([
        [1, 2],
        [2, 3],
        [3, 0],
        [4, 0]
    ]).view(max_seq_length, batch_size, 1)

    outputs, new_hidden = encoder.forward(pad_seqs=pad_seqs, seq_lengths=[4, 2], hidden=hidden)
    assert outputs.shape == torch.Size([4, batch_size, hidden_size]), f"Bad outputs.shape: {outputs.shape}"
    assert new_hidden.shape == torch.Size([1, batch_size, hidden_size]), f"Bad new_hidden.shape: {new_hidden.shape}"
    print('Success')

test_Encoder_shapes()

RNN中的隐藏大小与输入大小

1 个答案: