| class Decoder(Layer): | |
| def __init__(self, num_neurons, num_hidden_neurons, num_heads, vocabular_size, num_dec_layers=6): | |
| super(Decoder, self).__init__() | |
| self.num_dec_layers = num_dec_layers | |
| self.pre_processing_layer = PreProcessingLayer(num_neurons, vocabular_size) | |
| self.decoder_layers = [DecoderLayer(num_neurons, num_hidden_neurons, num_heads) for _ in range(num_dec_layers)] | |
| def call(self, sequence, enconder_output, training, look_ahead_mask, padding_mask): | |
| sequence = self.pre_processing_layer(sequence, training, mask) | |
| for i in range(self.num_dec_layers): | |
| sequence, attention_weights1, attention_weights2 = self.dec_layers[i](sequence, enconder_output, training, look_ahead_mask, padding_mask) | |
| attention_weights['decoder_layer{}_attention_weights1'.format(i+1)] = attention_weights1 | |
| attention_weights['decoder_layer{}_attention_weights2'.format(i+1)] = attention_weights2 | |
| return sequence, attention_weights |
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