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custom rnn layer in tensorflow keras

VVY answered on June 23, 2023 Popularity 1/10 Helpfulness 1/10

answer custom rnn layer in tensorflow keras

custom rnn layer in tensorflow keras

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import tensorflow as tf

class CustomRNNLayer(tf.keras.layers.Layer):
    def __init__(self, hidden_units, activation='tanh', **kwargs):
        super(CustomRNNLayer, self).__init__(**kwargs)
        self.hidden_units = hidden_units
        self.activation = tf.keras.activations.get(activation)
        
    def build(self, input_shape):
        self.W_xh = self.add_weight(shape=(input_shape[-1], self.hidden_units),
                                   initializer='glorot_uniform',
                                   name='W_xh')
        self.W_hh = self.add_weight(shape=(self.hidden_units, self.hidden_units)
                                   , initializer='orthogonal',
                                   name='W_hh')
        self.b_h = self.add_weight(shape=(self.hidden_units,),
                                  initializer='zeros',
                                  name='b_h')
        self.built = True

    def call(self, inputs, initial_state=None):
        if initial_state is None:
            initial_state = tf.zeros((tf.shape(inputs)[0], self.hidden_units))
        
        sequence_length = tf.shape(inputs)[1]
        hidden_states = []
        h = initial_state
        
        for t in range(sequence_length):
            x = inputs[:, t, :]
            h = self.activation(
              tf.matmul(x, self.W_xh) + tf.matmul(h, self.W_hh) + self.b_h
            )
            hidden_states.append(h)
        
        return tf.stack(hidden_states, axis=1)

Popularity 1/10 Helpfulness 1/10 Language python

Source: Grepper

Tags: keras python tensorflow

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Contributed on Jun 23 2023

VVY

0 Answers Avg Quality 2/10

custom rnn layer in tensorflow keras

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