decoder_init_inject.py

import torch
import torch.optim as optim
import torch.nn.functional as F
import torch.nn as nn


class Decoder(nn.Module):
    def __init__(self, hidden_size, output_size, embed_size, num_layers):
        super(Decoder, self).__init__()
        
        self.hidden_size = hidden_size
        self.embedding = nn.Embedding(output_size, embed_size)
        self.gru = nn.GRU(embed_size, hidden_size, num_layers=num_layers)
        self.out = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)
# Init inject
    def forward(self, input, hidden):
        
        output = self.embedding(input)
        output = F.relu(output)
        output, hidden = self.gru(output, hidden)
        output = self.softmax(self.out(output[0]))

        return output, hidden


def get_decoder(hidden_size=2048, output_size=10000, embed_size=128, num_layers=1):
    return Decoder(hidden_size=hidden_size, output_size=output_size, embed_size=embed_size, num_layers=num_layers)