def __init__(self, params, train):
super(DecoderStack, self).__init__()
self.layers = []
for _ in range(params["num_hidden_layers"]):
self_attention_layer = attention_layer.SelfAttention(
params["hidden_size"], params["num_heads"],
params["attention_dropout"], train)
feed_forward_network = ffn_layer.FeedFowardNetwork( # NOTYPO
params["hidden_size"], params["filter_size"],
params["relu_dropout"], train, params["allow_ffn_pad"])
proj_layer = tf.layers.Dense(
# TODO 加了一层ＭＬＰ，project the concatenated [encoder activations, hidden state] to the hidden size
params["hidden_size"],
use_bias=True,
name="proj_layer")
self.layers.append([
transformer.PrePostProcessingWrapper(self_attention_layer,
params, train),
transformer.PrePostProcessingWrapper(feed_forward_network,
params, train), proj_layer
])
self.output_normalization = transformer.LayerNormalization(
params["hidden_size"])
def __init__(self, params, train):
super(EncoderStack, self).__init__()
self.layers = []
for _ in range(params["num_hidden_layers"]):
# Create sublayers for each layer.
self_attention_layer = attention_layer.SelfAttention(
params["hidden_size"], params["num_heads"],
params["attention_dropout"], train)
feed_forward_network = ffn_layer.FeedFowardNetwork(
params["hidden_size"], params["filter_size"],
params["relu_dropout"], train, params["allow_ffn_pad"])
self.layers.append([
PrePostProcessingWrapper(self_attention_layer, params, train),
PrePostProcessingWrapper(feed_forward_network, params, train)])
# Create final layer normalization layer.
self.output_normalization = LayerNormalization(params["hidden_size"])
