def conv2d_bn(x, nb_filter, nb_row, nb_col,
border_mode='same', subsample=(1, 1),
name=None):
'''Utility function to apply conv + BN.
'''
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
if K.image_dim_ordering() == 'th':
bn_axis = 1
else:
bn_axis = 3
x = Convolution2D(nb_filter, nb_row, nb_col,
subsample=subsample,
activation='relu',
border_mode=border_mode,
name=conv_name)(x)
x = BatchNormalization(axis=bn_axis, name=bn_name)(x)
return x
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