40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
a=1.0
|
|
b=0.0
|
|
theta=0
|
|
# Step activation function
|
|
# Constant 'theta' determines the level of neuron activation.
|
|
# Parameter 'x' Weighted sum of initial data.
|
|
def ActStep (x):
|
|
return 1 if x>=theta else 0
|
|
|
|
# Linear activation function
|
|
# Constant 'a' defines the angle of inclination of the line, and 'b' - the vertical offset of the line
|
|
# Parameter 'x' Weighted sum of initial data.
|
|
def ActLinear (x):
|
|
return a*x+b
|
|
|
|
# Sigmoid activation function
|
|
# Constant 'a' stretches the range of values of the function from '0' to 'a'
|
|
# Constant 'b' shifts the resulting value
|
|
# Parameter 'x' Weighted sum of initial data.
|
|
import math
|
|
def ActSigmoid(x):
|
|
return a/(1+math.exp(-x))-b
|
|
|
|
# TANH activation function
|
|
# Parameter 'x' Weighted sum of initial data.
|
|
import math
|
|
def ActTanh (x):
|
|
return math.tanh(x)
|
|
|
|
# PReLU activation function
|
|
# Constant 'a' leak parameter
|
|
# Parameter 'x' Weighted sum of initial data.
|
|
def ActPReLU (x):
|
|
return x if x>=0 else a*x
|
|
|
|
# SoftMax activation function
|
|
# Parameter 'X' array of weighted initial data.
|
|
from scipy.special import softmax
|
|
def ActSoftMax (X):
|
|
return softmax(X)
|