Causal Convolution 1D

causal_conv1d.py

import tensorflow as tf

class CausalConv1D(tf.layers.Conv1D):
    def __init__(self, filters,
               kernel_size,
               strides=1,
               dilation_rate=1,
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=tf.zeros_initializer(),
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               trainable=True,
               name=None,
               **kwargs):
        super(CausalConv1D, self).__init__(
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding='valid',
            data_format='channels_last',
            dilation_rate=dilation_rate,
            activation=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            trainable=trainable,
            name=name, **kwargs
        )
       
    def call(self, inputs):
        padding = (self.kernel_size[0] - 1) * self.dilation_rate[0]
        inputs = tf.pad(inputs, tf.constant([(0, 0,), (1, 0), (0, 0)]) * padding)
        return super(CausalConv1D, self).call(inputs)