PReLU¶

class paddle.nn. PReLU ( num_parameters=1, init=0.25, weight_attr=None, data_format='NCHW', name=None ) [source]

PReLU Activation. The calculation formula is follows:

If approximate calculation is used:

\[PReLU(x) = max(0, x) + weight * min(0, x)\]

x is input Tensor.

Parameters

num_parameters (int, optional) – Number of weight to learn. The supported values are: 1 - a single parameter alpha is used for all input channels; Number of channels - a separate alpha is used for each input channel. Default is 1.
init (float, optional) – Init value of learnable weight. Default is 0.25.
weight_attr (ParamAttr, optional) – The parameter attribute for the learnable weight. Default is None. For more information, please refer to ParamAttr.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
data_format (str, optional) – Data format that specifies the layout of input. It may be “NC”, “NCL”, “NCHW”, “NCDHW”, “NLC”, “NHWC” or “NDHWC”. Default: “NCHW”.

Shape:

input: Tensor with any shape. Default dtype is float32.
output: Tensor with the same shape as input.

Examples

           import paddle
paddle.set_default_dtype("float64")

data = paddle.to_tensor([[[[-2.0,  3.0, -4.0,  5.0],
                        [ 3.0, -4.0,  5.0, -6.0],
                        [-7.0, -8.0,  8.0,  9.0]],
                        [[ 1.0, -2.0, -3.0,  4.0],
                        [-5.0,  6.0,  7.0, -8.0],
                        [ 6.0,  7.0,  8.0,  9.0]]]])

m = paddle.nn.PReLU(1, 0.25)
out = m(data)
print(out)
# [[[[-0.5 ,  3.  , -1.  ,  5.  ],
#    [ 3.  , -1.  ,  5.  , -1.5 ],
#    [-1.75, -2.  ,  8.  ,  9.  ]],
#   [[ 1.  , -0.5 , -0.75,  4.  ],
#    [-1.25,  6.  ,  7.  , -2.  ],
#    [ 6.  ,  7.  ,  8.  ,  9.  ]]]]

          

forward ( x ) forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments

extra_repr ( ) extra_repr¶: Extra representation of this layer, you can have custom implementation of your own layer.

PReLU¶

forward¶

extra_repr¶