unnessessary code lines in SurnameGenerationModel class in7_3_Model1_Unconditioned_Surname_Generation-Origin.ipynb

[zhangjiekui]

It seems these code lines below are unnessessry, I uncommented these lines and the runned result is just the same. Would you agree that?

class SurnameGenerationModel(nn.Module):
def init(self, char_embedding_size, char_vocab_size, rnn_hidden_size,
batch_first=True, padding_idx=0, dropout_p=0.5):
"""
Args:
char_embedding_size (int): The size of the character embeddings
char_vocab_size (int): The number of characters to embed
rnn_hidden_size (int): The size of the RNN's hidden state
batch_first (bool): Informs whether the input tensors will
have batch or the sequence on the 0th dimension
padding_idx (int): The index for the tensor padding;
see torch.nn.Embedding
dropout_p (float): the probability of zeroing activations using
the dropout method. higher means more likely to zero.
"""
super(SurnameGenerationModel, self).init()

    self.char_emb = nn.Embedding(num_embeddings=char_vocab_size,
                                 embedding_dim=char_embedding_size,
                                 padding_idx=padding_idx)

    self.rnn = nn.GRU(input_size=char_embedding_size, 
                      hidden_size=rnn_hidden_size,
                      batch_first=batch_first)
    
    self.fc = nn.Linear(in_features=rnn_hidden_size, 
                        out_features=char_vocab_size)
    
    self._dropout_p = dropout_p

def forward(self, x_in, apply_softmax=False):
    """The forward pass of the model
    
    Args:
        x_in (torch.Tensor): an input data tensor. 
            x_in.shape should be (batch, input_dim)
        apply_softmax (bool): a flag for the softmax activation
            should be false if used with the Cross Entropy losses
    Returns:
        the resulting tensor. tensor.shape should be (batch, char_vocab_size)
    """
    x_embedded = self.char_emb(x_in)

    y_out, _ = self.rnn(x_embedded)

    #batch_size, seq_size, feat_size = y_out.shape
    #y_out = y_out.contiguous().view(batch_size * seq_size, feat_size)

    y_out = self.fc(F.dropout(y_out, p=self._dropout_p))
                     
    if apply_softmax:
        y_out = F.softmax(y_out, dim=1)
        
    #new_feat_size = y_out.shape[-1]
    #y_out = y_out.view(batch_size, seq_size, new_feat_size)
        
    return y_out