tf.layers.batch_normalization(inputs, axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=tf.zeros_initializer(), gamma_initializer=tf.ones_initializer(), moving_mean_initializer=tf.zeros_initializer(), moving_variance_initializer=tf.ones_initializer(), beta_regularizer=None, gamma_regularizer=None, training=False, trainable=True, name=None, reuse=None)

tf.layers.batch_normalization(inputs, axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=tf.zeros_initializer(), gamma_initializer=tf.ones_initializer(), moving_mean_initializer=tf.zeros_initializer(), moving_variance_initializer=tf.ones_initializer(), beta_regularizer=None, gamma_regularizer=None, training=False, trainable=True, name=None, reuse=None)

Functional interface for the batch normalization layer.

Reference: http://arxiv.org/abs/1502.03167

"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"

Sergey Ioffe, Christian Szegedy

Arguments:

• inputs: Tensor input.
• axis: Integer, the axis that should be normalized (typically the features axis). For instance, after a Convolution2D layer with data_format="channels_first", set axis=1 in BatchNormalization.
• momentum: Momentum for the moving average.
• epsilon: Small float added to variance to avoid dividing by zero.
• center: If True, add offset of beta to normalized tensor. If False, beta is ignored.
• scale: If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling can be done by the next layer.
• beta_initializer: Initializer for the beta weight.
• gamma_initializer: Initializer for the gamma weight.
• moving_mean_initializer: Initializer for the moving mean.
• moving_variance_initializer: Initializer for the moving variance.
• beta_regularizer: Optional regularizer for the beta weight.
• gamma_regularizer: Optional regularizer for the gamma weight.
• training: Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (normalized with statistics of the current batch) or in inference mode (normalized with moving statistics).
• trainable: Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
• name: String, the name of the layer.
• reuse: Boolean, whether to reuse the weights of a previous layer by the same name.

Returns:

Output tensor.

Defined in tensorflow/python/layers/normalization.py.