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

Before Change

                    return tf.identity(mean), tf.identity(variance)

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

After Change

                                      trainable=False,)//   restore=restore)
            moving_variance = tf.get_variable("moving_variance",
                                          params_shape,
                                          initializer=tf.constant_initializer(1.),
                                          trainable=False,)//   restore=restore)

            //// 3.
            // These ops will only be preformed when training.
            mean, variance = tf.nn.moments(self.inputs, axis)
            try:    // TF12
                update_moving_mean = moving_averages.assign_moving_average(
                                moving_mean, mean, decay, zero_debias=False)     // if zero_debias=True, has bias
                update_moving_variance = moving_averages.assign_moving_average(
                                moving_variance, variance, decay, zero_debias=False) // if zero_debias=True, has bias
                // print("TF12 moving")
            except Exception as e:  // TF11
                update_moving_mean = moving_averages.assign_moving_average(
                                moving_mean, mean, decay)
                update_moving_variance = moving_averages.assign_moving_average(
                                moving_variance, variance, decay)
                // print("TF11 moving")

            def mean_var_with_update():
                with tf.control_dependencies([update_moving_mean, update_moving_variance]):
                    return tf.identity(mean), tf.identity(variance)

            // ema = tf.train.ExponentialMovingAverage(decay=decay)    // Akara
            // def mean_var_with_update():
            //     ema_apply_op = ema.apply([moving_mean, moving_variance])
            //     with tf.control_dependencies([ema_apply_op]):
            //         return tf.identity(mean), tf.identity(variance)

            //// 4. behaviour for training and testing
            // if not is_train:    // test : mean=0, std=1
            // // if is_train:      // train : mean=0, std=1
            //     is_train = tf.cast(tf.ones([]), tf.bool)
            // else:
            //     is_train = tf.cast(tf.zeros([]), tf.bool)
            //
            // // mean, var = control_flow_ops.cond(
            // mean, var = tf.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
            //
            // self.outputs = act( tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon) )
            if is_train:
                mean, var = mean_var_with_update()
                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
            // variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=vs.name)    // 2 params beta, gamma
            variables = [beta, gamma, moving_mean, moving_variance]
            // variables = [beta, gamma]

            // print(len(variables))
            // for idx, v in enumerate(variables):

Instances