我們有時會看到GRU中輸入的參數有時是一個，但是有時又有兩個。這難免會讓人們感到疑惑，那么這些參數到底是什么呢。

一、輸入到GRU的參數

輸入的參數有兩個，分別是input和h_0。

Inputs: input, h_0

①input的shape

The shape of input:(seq_len, batch, input_size) : tensor containing the feature of the input sequence. The input can also be a packed variable length sequence。

See functorch.nn.utils.rnn.pack_padded_sequencefor details.

②h_0的shape

從下面的解釋中也可以看出，這個參數可以不提供，那么就默認為0.

The shape of h_0:(num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch.

Defaults to zero if not provided. If the RNN is bidirectional num_directions should be 2, else it should be 1.

綜上，可以只輸入一個參數。當輸入兩個參數的時候，那么第二個參數相當于是一個隱含層的輸出。

為了便于理解，下面是一幅圖：

二、GRU返回的數據

輸出有兩個，分別是output和h_n

①output

output 的shape是：(seq_len, batch, num_directions * hidden_size): tensor containing the output features h_t from the last layer of the GRU, for each t.

If a class:torch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence.

For the unpacked case, the directions can be separated using output.view(seq_len, batch, num_directions, hidden_size), with forward and backward being direction 0 and 1 respectively.

Similarly, the directions can be separated in the packed case.

②h_n

h_n的shape是：(num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len
Like output, the layers can be separated using
h_n.view(num_layers, num_directions, batch, hidden_size).

三、代碼示例

數據的shape是[batch,seq_len,emb_dim]

RNN接收輸入的數據的shape是[seq_len,batch,emb_dim]

即前兩個維度調換就行了。

可以知道，加入批處理的時候一次處理128個句子，每個句子中有5個單詞，那么上圖中展示的input_data的shape是:[128,5,emb_dim]。

結合代碼分析，本例子將演示有1個句子和5個句子的情況。假設每個句子中有9個單詞，所以seq_len=9,并且每個單詞對應的emb_dim=3，所以對應數據的shape是: [batch,9,3]，由于輸入到RNN中數據格式的格式，所以為[9,batch,3]

import torch
import torch.nn as nn

emb_dim = 3
hidden_dim = 2
rnn = nn.GRU(emb_dim,hidden_dim)
#rnn = nn.GRU(9,1,3)
print(type(rnn))

tensor1 = torch.tensor([[-0.5502, -0.1920, 1.1845],
[-0.8003, 2.0783, 0.0175],
[ 0.6761, 0.7183, -1.0084],
[ 0.9514, 1.4772, -0.2271],
[-1.0146, 0.7912, 0.2003],
[-0.5502, -0.1920, 1.1845],
[-0.8003, 2.0783, 0.0175],
[ 0.1718, 0.1070, 0.4255],
[-2.6727, -1.5680, -0.8369]])

tensor2 = torch.tensor([[-0.5502, -0.1920]])

# 假設input只有一個句子，那么batch為1
print('--------------batch=1時------------')
data = tensor1.unsqueeze(0)
h_0 = tensor2[0].unsqueeze(0).unsqueeze(0)
print('data.shape: [batch,seq_len,emb_dim]',data.shape)
print('')
input = data.transpose(0,1)
print('input.shape: [seq_len,batch,emb_dim]',input.shape)
print('h_0.shape: [1,batch,hidden_dim]',h_0.shape)
print('')
# 輸入到rnn中
output,h_n = rnn(input,h_0)
print('output.shape: [seq_len,batch,hidden_dim]',output.shape)
print('h_n.shape: [1,batch,hidden_dim]',h_n.shape)

# 假設input中有5個句子，所以,batch = 5
print('\n--------------batch=5時------------')
data = tensor1.unsqueeze(0).repeat(5,1,1) # 由于batch為5
h_0 = tensor2[0].unsqueeze(0).repeat(1,5,1) # 由于batch為5
print('data.shape: [batch,seq_len,emb_dim]',data.shape)
print('')
input = data.transpose(0,1)

print('input.shape: [seq_len,batch,emb_dim]',input.shape)
print('h_0.shape: [1,batch,hidden_dim]',h_0.shape)
print('')
# 輸入到rnn中
output,h_n = rnn(input,h_0)
print('output.shape: [seq_len,batch,hidden_dim]',output.shape)
print('h_n.shape: [1,batch,hidden_dim]',h_n.shape)

四、輸出

<class ‘torch.nn.modules.rnn.GRU’>
--------------batch=1時------------
data.shape: [batch,seq_len,emb_dim] torch.Size([1, 9, 3])

input.shape: [seq_len,batch,emb_dim] torch.Size([9, 1, 3])
h_0.shape: [1,batch,hidden_dim] torch.Size([1, 1, 2])

output.shape: [seq_len,batch,hidden_dim] torch.Size([9, 1, 2])
h_n.shape: [1,batch,hidden_dim] torch.Size([1, 1, 2])

--------------batch=5時------------
data.shape: [batch,seq_len,emb_dim] torch.Size([5, 9, 3])

input.shape: [seq_len,batch,emb_dim] torch.Size([9, 5, 3])
h_0.shape: [1,batch,hidden_dim] torch.Size([1, 5, 2])

output.shape: [seq_len,batch,hidden_dim] torch.Size([9, 5, 2])
h_n.shape: [1,batch,hidden_dim] torch.Size([1, 5, 2])

總結