paddle.fluid.layers.affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None, act=None)[source]

Applies a separate affine transformation to each channel of the input. Useful for replacing spatial batch norm with its equivalent fixed transformation. The input also can be 2D tensor and applies a affine transformation in second dimension.

  • x (Variable) – Feature map input can be a 4D tensor with order NCHW or NHWC. It also can be a 2D tensor and the affine transformation is applied in the second dimension.The data type is float32 or float64.

  • scale (Variable) – 1D input of shape (C), the c-th element is the scale factor of the affine transformation for the c-th channel of the input.The data type is float32 or float64.

  • bias (Variable) – 1D input of shape (C), the c-th element is the bias of the affine transformation for the c-th channel of the input. The data type is float32 or float64.

  • data_layout (str, optional) – Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: “NCHW”, “NHWC”. The default is “NCHW”. When it is “NCHW”, the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. If input is 2D Tensor, you can ignore data_layout.

  • name (str, default None) – The name of this layer. For more information, please refer to Name .

  • act (str, default None) – Activation to be applied to the output of this layer.


A tensor which has the same shape, data layout and data type with x.

Return type



import numpy as np
import paddle.fluid as fluid

use_gpu = False
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)

data ='data', shape=[None, 1, 2, 2], dtype='float32')
input_scale = fluid.layers.create_parameter(shape=[1], dtype="float32",
input_bias = fluid.layers.create_parameter(shape=[1],dtype="float32",
out = fluid.layers.affine_channel(data,scale=input_scale,
test_program = fluid.default_main_program().clone(for_test=True)

[out_array] =,
                      feed={'data': np.ones([1,1,2,2]).astype('float32')})
# out_array is [[[[2.5, 2.5],
#                [2.5, 2.5]]]] with shape: [1, 1, 2, 2]