brainstorm/layers/batch_normalization_layer.py
def BatchNorm(name=None, decay=0.9, epsilon=1.0e-5):
    """Create a BatchNormalization layer.

    This layer implements batch normalization over the last (right-most)
    dimension. Thus, it can be use with both fully connected and convolutional
    layers (but only with data in NHWC format).
    """
    return ConstructionWrapper.create(BatchNormLayerImpl,
                                      name=name,
                                      decay=decay,
                                      epsilon=epsilon)
Create a BatchNormalization layer.

    This layer implements batch normalization over the last (right-most)
    dimension. Thus, it can be use with both fully connected and convolutional
    layers (but only with data in NHWC format).
    
Create a BatchNormalization layer.
This layer implements batch normalization over the last (right-most)
dimension. Thus, it can be use with both fully connected and convolutional
layers (but only with data in NHWC format).
Create a BatchNormalization layer . This layer implements batch normalization over the last ( right - most ) dimension . Thus it can be use with both fully connected and convolutional layers ( but only with data in NHWC format ) .
def BatchNorm(name=None, decay=0.9, epsilon=1.0e-5):
    return ConstructionWrapper.create(BatchNormLayerImpl,
                                      name=name,
                                      decay=decay,
                                      epsilon=epsilon)
def BatchNorm ( name = None , decay = 0.9 , epsilon = 1.0e-5 ) : return ConstructionWrapper . create ( BatchNormLayerImpl , name = name , decay = decay , epsilon = epsilon )
Create a BatchNormalization layer.
Create a BatchNormalization layer .
"""Create a BatchNormalization layer.

    This layer implements batch normalization over the last (right-most)
    dimension. Thus, it can be use with both fully connected and convolutional
    layers (but only with data in NHWC format).
    """
name
decay
epsilon