transformer.py

class Transformer(Model):
    def __init__(self, num_layers, num_neurons, num_hidden_neurons, num_heads, input_vocabular_size, target_vocabular_size):
        super(Transformer, self).__init__()
        self.encoder = Encoder(num_neurons, num_hidden_neurons, num_heads, input_vocabular_size, num_layers)
        self.decoder = Decoder(num_neurons, num_hidden_neurons, num_heads, target_vocabular_size, num_layers)
        self.linear_layer = Dense(target_vocabular_size)

    def call(self, transformer_input, tar, training, encoder_padding_mask, look_ahead_mask, decoder_padding_mask):
        encoder_output = self.encoder(transformer_input, training, encoder_padding_mask)
        decoder_output, attention_weights = self.decoder(tar, encoder_output, training, look_ahead_mask, decoder_padding_mask)
        output = self.linear_layer(decoder_output)

        return output, attention_weights