From 9349b618f3d845c40fa421412c518241d17dc6f1 Mon Sep 17 00:00:00 2001 From: Henry Bigelow Date: Thu, 26 Jul 2018 08:04:44 -0700 Subject: [PATCH] histogram_summary -> summary.histogram --- wavenet/model.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/wavenet/model.py b/wavenet/model.py index d2e79a9af..fb0637499 100644 --- a/wavenet/model.py +++ b/wavenet/model.py @@ -320,15 +320,15 @@ def _create_dilation_layer(self, input_batch, layer_index, dilation, if self.histograms: layer = 'layer{}'.format(layer_index) - tf.histogram_summary(layer + '_filter', weights_filter) - tf.histogram_summary(layer + '_gate', weights_gate) - tf.histogram_summary(layer + '_dense', weights_dense) - tf.histogram_summary(layer + '_skip', weights_skip) + tf.summary.histogram(layer + '_filter', weights_filter) + tf.summary.histogram(layer + '_gate', weights_gate) + tf.summary.histogram(layer + '_dense', weights_dense) + tf.summary.histogram(layer + '_skip', weights_skip) if self.use_biases: - tf.histogram_summary(layer + '_biases_filter', filter_bias) - tf.histogram_summary(layer + '_biases_gate', gate_bias) - tf.histogram_summary(layer + '_biases_dense', dense_bias) - tf.histogram_summary(layer + '_biases_skip', skip_bias) + tf.summary.histogram(layer + '_biases_filter', filter_bias) + tf.summary.histogram(layer + '_biases_gate', gate_bias) + tf.summary.histogram(layer + '_biases_dense', dense_bias) + tf.summary.histogram(layer + '_biases_skip', skip_bias) input_cut = tf.shape(input_batch)[1] - tf.shape(transformed)[1] input_batch = tf.slice(input_batch, [0, input_cut, 0], [-1, -1, -1]) @@ -421,11 +421,11 @@ def _create_network(self, input_batch, global_condition_batch): b2 = self.variables['postprocessing']['postprocess2_bias'] if self.histograms: - tf.histogram_summary('postprocess1_weights', w1) - tf.histogram_summary('postprocess2_weights', w2) + tf.summary.histogram('postprocess1_weights', w1) + tf.summary.histogram('postprocess2_weights', w2) if self.use_biases: - tf.histogram_summary('postprocess1_biases', b1) - tf.histogram_summary('postprocess2_biases', b2) + tf.summary.histogram('postprocess1_biases', b1) + tf.summary.histogram('postprocess2_biases', b2) # We skip connections from the outputs of each layer, adding them # all up here.