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import tensorflow as tf
from .layers import EncoderLayer
@tf.keras.utils.register_keras_serializable()
class Encoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
max_tokens, dropout_rate, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
self.pos_encoding = self.positional_encoding(max_tokens, d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, dropout_rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(dropout_rate)
def call(self, x, training=None, mask=None):
seq_len = tf.shape(x)[1]
x = self.embedding(x)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x = self.enc_layers[i](x, training=training, mask=mask)
return x
def positional_encoding(self, max_len, d_model):
angle_rads = self.get_angles(tf.range(max_len, dtype=tf.float32)[:, tf.newaxis],
tf.range(d_model, dtype=tf.float32)[tf.newaxis, :],
d_model)
sines = tf.math.sin(angle_rads[:, 0::2])
cosines = tf.math.cos(angle_rads[:, 1::2])
pos_encoding = tf.concat([sines, cosines], axis=-1)
return pos_encoding[tf.newaxis, ...]
def get_angles(self, pos, i, d_model):
angle_rates = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))
return pos * angle_rates
def get_config(self):
config = super().get_config()
config.update({
'num_layers': self.num_layers,
'd_model': self.d_model,
'num_heads': self.num_heads,
'dff': self.dff,
'input_vocab_size': self.embedding.input_dim,
'max_tokens': self.pos_encoding.shape[1],
'dropout_rate': self.dropout.rate
})
return config |