Temporal Convolutional Networks

tcn.py

class TemporalConvNet(tf.layers.Layer):
    def __init__(self, num_channels, kernel_size=2, dropout=0.2,
                 trainable=True, name=None, dtype=None, 
                 activity_regularizer=None, **kwargs):
        super(TemporalConvNet, self).__init__(
            trainable=trainable, dtype=dtype,
            activity_regularizer=activity_regularizer,
            name=name, **kwargs
        )
        self.layers = []
        num_levels = len(num_channels)
        for i in range(num_levels):
            dilation_size = 2 ** i
            out_channels = num_channels[i]
            self.layers.append(
                TemporalBlock(out_channels, kernel_size, strides=1, dilation_rate=dilation_size,
                              dropout=dropout, name="tblock_{}".format(i))
            )
    
    def call(self, inputs, training=True):
        outputs = inputs
        for layer in self.layers:
            outputs = layer(outputs, training=training)
        return outputs