Method _create_variables

wavenet/model.py:127–234

This function creates all variables used by the network. This allows us to share them between multiple calls to the loss function and generation function.

(self)

        return receptive_field

    def _create_variables(self):
        '''This function creates all variables used by the network.
        This allows us to share them between multiple calls to the loss
        function and generation function.'''

        var = dict()

        with tf.variable_scope('wavenet'):
            if self.global_condition_cardinality is not None:
                # We only look up the embedding if we are conditioning on a
                # set of mutually-exclusive categories. We can also condition
                # on an already-embedded dense vector, in which case it's
                # given to us and we don't need to do the embedding lookup.
                # Still another alternative is no global condition at all, in
                # which case we also don't do a tf.nn.embedding_lookup.
                with tf.variable_scope('embeddings'):
                    layer = dict()
                    layer['gc_embedding'] = create_embedding_table(
                        'gc_embedding',
                        [self.global_condition_cardinality,
                         self.global_condition_channels])
                    var['embeddings'] = layer

            with tf.variable_scope('causal_layer'):
                layer = dict()
                if self.scalar_input:
                    initial_channels = 1
                    initial_filter_width = self.initial_filter_width
                else:
                    initial_channels = self.quantization_channels
                    initial_filter_width = self.filter_width
                layer['filter'] = create_variable(
                    'filter',
                    [initial_filter_width,
                     initial_channels,
                     self.residual_channels])
                var['causal_layer'] = layer

            var['dilated_stack'] = list()
            with tf.variable_scope('dilated_stack'):
                for i, dilation in enumerate(self.dilations):
                    with tf.variable_scope('layer{}'.format(i)):
                        current = dict()
                        current['filter'] = create_variable(
                            'filter',
                            [self.filter_width,
                             self.residual_channels,
                             self.dilation_channels])
                        current['gate'] = create_variable(
                            'gate',
                            [self.filter_width,
                             self.residual_channels,
                             self.dilation_channels])
                        current['dense'] = create_variable(
                            'dense',
                            [1,
                             self.dilation_channels,
                             self.residual_channels])