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The following activation functions are supported:
- relu: Rectified Linear Unit, y=max(x,0)y=max(x,0)
- sigmoid: y=11+exp(−x)y=11+exp(−x)
- tanh: Hyperbolic tangent, y=exp(x)−exp(−x)exp(x)+exp(−x)y=exp(x)−exp(−x)exp(x)+exp(−x)
- softrelu: Soft ReLU, or SoftPlus, y=log(1+exp(x))y=log(1+exp(x))
- softsign: y=x1+abs(x)
mxnet.ndarray.Activation(data=None, act_type=_Null, out=None, name=None, **kwargs)¶
- data (NDArray) – The input array.
- act_type ({'relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'}, required) – Activation function to be applied.
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import mxnet as mx
import numpy as np
x = mx.nd.array([[1.085,-2.75,-5.6,9.9],[3.087,5.32,3.75,11.865]])
y = mx.nd.Activation(x,act_type='relu')
print y
y = mx.nd.Activation(x,act_type='softrelu')
print y[[ 1.085 0. 0. 9.9 ]
[ 3.087 5.32 3.75 11.865]]
<NDArray 2x4 @cpu(0)>
[[1.3761026e+00 6.1967585e-02 3.6910437e-03 9.9000502e+00]
[3.1316278e+00 5.3248811e+00 3.7732456e+00 1.1865006e+01]]
<NDArray 2x4 @cpu(0)>
>>>