def build(self, input_shape):
"""Builds the encoder stack."""
params = self.params
self.projection_layer = ffn_layer.FeedForwardNetwork(
params["enc_hidden_size"], params["enc_filter_size"],
params["relu_dropout"])
for _ in range(params["num_hidden_layers"]):
# Create sublayers for each layer.
self_attention_layer = attention_layer.SelfAttention(
params["enc_hidden_size"], params["num_heads"],
params["attention_dropout"])
feed_forward_network = ffn_layer.FeedForwardNetwork(
params["enc_hidden_size"], params["enc_filter_size"],
params["relu_dropout"])
self.layers.append([
PrePostProcessingWrapper(self_attention_layer, params,
params["enc_hidden_size"]),
PrePostProcessingWrapper(feed_forward_network, params,
params["enc_hidden_size"])
])
# Create final layer normalization layer.
self.output_normalization = LayerNormalization(
params["enc_hidden_size"])
super(EncoderStack, self).build(input_shape)
def build(self, input_shape):
"""Builds the decoder stack."""
params = self.params
for _ in range(params["num_hidden_layers"]):
self_attention_layer = attention_layer.SelfAttention(
params["hidden_size"], params["num_heads"],
params["attention_dropout"])
enc_dec_attention_layer = attention_layer.Attention(
params["hidden_size"], params["num_heads"],
params["attention_dropout"])
feed_forward_network = ffn_layer.FeedForwardNetwork(
params["hidden_size"], params["filter_size"], params["relu_dropout"])
self.layers.append([
PrePostProcessingWrapper(self_attention_layer, params),
PrePostProcessingWrapper(enc_dec_attention_layer, params),
PrePostProcessingWrapper(feed_forward_network, params)
])
self.output_normalization = LayerNormalization(params["hidden_size"])
super(DecoderStack, self).build(input_shape)
