b97fe6310224efd2e7449e53d067f9e3adc178a5,layers/eight_mile/tf/layers.py,TransformerEncoderStack,__init__,#TransformerEncoderStack#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,2524

Before Change

        super().__init__(name=name)
        self.encoders = []
        self.ln = tf.identity if layer_norms_after else tf.keras.layers.LayerNormalization(epsilon=layer_norm_eps)

        if not is_sequence(rpr_k):
            rpr_k = [rpr_k] * layers

After Change

    
    output_keep_prob = tf.contrib.framework.smart_cond(training, lambda: 1.0 - pdrop, lambda: 1.0)
    state_keep_prob = tf.contrib.framework.smart_cond(training, lambda: 1.0 - pdrop if variational else 1.0, lambda: 1.0)
    cell = rnn_cell(hsz, rnntype, st)
    output = tf.contrib.rnn.DropoutWrapper(cell,
                                           output_keep_prob=output_keep_prob,
                                           state_keep_prob=state_keep_prob,
                                           variational_recurrent=variational,
                                           dtype=tf.float32)

Instances