mindspore.ops.PReLU

class mindspore.ops.PReLU[source]

Parametric Rectified Linear Unit activation function.

Refer to mindspore.ops.prelu() for more details.

Inputs:

x (Tensor) - The input Tensor of the activation function. The data type is float16 or float32. The shape is \((N, C, *)\) where \(*\) means, any number of additional dimensions.
weight (Tensor) - Weight Tensor. The data type is float16 or float32. The weight can only be a vector, and the length is the same as the number of channels C of the input_x. On GPU devices, when the input is a scalar, the shape is 1.

Outputs:

Tensor, with the same type as x.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, nn, ops
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.prelu = ops.PReLU()
...     def construct(self, x, weight):
...         result = self.prelu(x, weight)
...         return result
...
>>> x = Tensor(np.arange(-6, 6).reshape((2, 3, 2)), mindspore.float32)
>>> weight = Tensor(np.array([0.1, 0.6, -0.3]), mindspore.float32)
>>> net = Net()
>>> output = net(x, weight)
>>> print(output)
[[[-0.60 -0.50]
  [-2.40 -1.80]
  [ 0.60  0.30]]
 [[ 0.00  1.00]
  [ 2.00  3.00]
  [ 4.0   5.00]]]