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I am struggling with understanding how to get hidden layers and concatenate them.

I am using the following code as an example:

class classifier(nn.Module):

#define all the layers used in model
def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, 
             bidirectional, dropout):

    #Constructor
    super().__init__()

    self.batch = BATCH_SIZE
    self.hidden = hidden_dim
    self.layers = n_layers
    if(bidirectional):
        self.directions = 2
    else:
        self.directions = 1

    #embedding layer
    self.embedding = nn.Embedding(vocab_size, embedding_dim)

    #lstm layer
    self.gru = nn.GRU(embedding_dim, 
                       hidden_dim, 
                       num_layers=n_layers, 
                       bidirectional=bidirectional, 
                       dropout=dropout,
                       batch_first=True)

    #dense layer
    self.fc = nn.Linear(hidden_dim * 2, output_dim)

    #activation function
    self.act = nn.Sigmoid()

def forward(self, text, text_lengths):

    #text = [batch size,sent_length]
    embedded = self.embedding(text)
    #embedded = [batch size, sent_len, emb dim]

    #packed sequence
    packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths,batch_first=True)

    packed_output, (hidden, cell) = self.lstm(packed_embedded)
    #hidden = [batch size, num layers * num directions,hid dim]
    #cell = [batch size, num layers * num directions,hid dim]

    #concat the final forward and backward hidden state
    hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)

    #hidden = [batch size, hid dim * num directions]
    dense_outputs=self.fc(hidden)

    #Final activation function
    outputs=self.act(dense_outputs)

    return outputs

The line hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1) I did not get it.

As per my understanding, I was doing this which did not work.

hidden2 = hidden.view(batch_size,self.layers,self.directions,self.hidden)  
hidden2 = torch.cat((hidden2[:,:,0,:],hidden2[:,:,1,:]),dim=1)
dense_outputs=self.fc(hidden2)

Can somebody please explain. I went through PyTorch documentation but did not get.

Abdul Wahab
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1 Answers1

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The shape[0] of hidden output for bidirectional GRU is 2. You should just concat two hidden output on dim=1:

hid_enc = torch.cat([hid_enc[0,:, :], hid_enc[1,:,:]], dim=1).unsqueeze(0)

As the explanation for usage of -1 and -2 as the index , as you know in python lists, the object in index -1 is the last object of the list(second object in our tensor list) and index -2 refers to the object before last object(first object in our case). So the code you did not understand is equivalent to the code in my answer

Marzi Heidari
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