def apply_setup(input_dim, broadcastable, conserve_memory, mean_only, learn_scale, learn_shift):
 bn = BatchNormalization(input_dim, broadcastable, conserve_memory, epsilon=0.0001, mean_only=mean_only, learn_scale=learn_scale, learn_shift=learn_shift)
 bn.initialize()
 b_len = (len(input_dim) if isinstance(input_dim, collections.Sequence) else 1)
 x = tensor.TensorType(theano.config.floatX, ([False] * (b_len + 1)))()
 return (bn, x)

def apply_setup ( input_dim , broadcastable , conserve_memory , mean_only , learn_scale , learn_shift ) : bn = BatchNormalization ( input_dim , broadcastable , conserve_memory , epsilon = 0.0001 , mean_only = mean_only , learn_scale = learn_scale , learn_shift = learn_shift ) bn . initialize ( ) b_len = ( len ( input_dim ) if isinstance ( input_dim , collections . Sequence ) else 1 ) x = tensor . TensorType ( theano . config . floatX , ( [ False ] * ( b_len + 1 ) ) ) ( ) return ( bn , x )
common setup code .