d42df89c351e0c2a031ea3a9ae17fb7b844e7b79,tensorlayer/layers.py,BatchNormLayer,__init__,#BatchNormLayer#Any#Any#Any#Any#Any#Any#Any#Any#,1680

Before Change

            else:
                is_train = tf.cast(tf.zeros([]), tf.bool)

            mean, variance = control_flow_ops.cond(
                // is_train, lambda: (mean, variance),     // when training, (x-mean(x))/var(x)
                is_train, mean_var_with_update,
                lambda: (moving_mean, moving_variance)) // when inferencing, (x-0)/1

After Change

                self.outputs = act( tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon) )
            else:
                // self.outputs = act( tf.nn.batch_normalization(self.inputs, ema.average(mean), ema.average(variance), beta, gamma, epsilon) ) // Akara
                self.outputs = act( tf.nn.batch_normalization(self.inputs, moving_mean, moving_variance, beta, gamma, epsilon) )    // Simiao
                // self.outputs = act( tf.nn.batch_normalization(self.inputs, mean, variance, beta, gamma, epsilon) )

            // variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)  // 8 params in TF12 if zero_debias=True

Instances