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class PreProcessingLayer(Layer):
def __init__(self, num_neurons, vocabular_size):
super(PreProcessingLayer, self).__init__()
# Initialize
self.num_neurons = num_neurons
# Add embedings and positional encoding
self.embedding = Embedding(vocabular_size, self.num_neurons)
positional_encoding_handler = PositionalEncoding(vocabular_size, self.num_neurons)
self.positional_encoding = positional_encoding.get_positional_encoding()
# Add embedings and positional encoding
self.dropout = Dropout(0.1)
def call(self, sequence, training, mask):
sequence_lenght = tf.shape(sequence)[1]
sequence = self.embedding(sequence)
sequence *= tf.math.sqrt(tf.cast(self.num_neurons, tf.float32))
sequence += self.positional_encoding[:, :sequence_lenght, :]
sequence = self.dropout(sequence, training=training)
return sequence
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