我为神经网络实现了一堆激活函数，我只想验证它们是否正常工作。我实现了sigmoid，tanh，relu，arctan，step function，squash和gaussian，我使用它们的隐式导数（就输出而言）进行反向传播。
import numpy as np
def sigmoid(x, derivative=False):
if (derivative == True):
return x * (1 - x)
return 1 / (1 + np.exp(-x))
def tanh(x, derivative=False):
if (derivative == True):
return (1 - (x ** 2))
return np.tanh(x)
def relu(x, derivative=False):
if (derivative == True):
for i in range(0, len(x)):
for k in range(len(x[i])):
if x[i][k] > 0:
x[i][k] = 1
else:
x[i][k] = 0
return x
for i in range(0, len(x)):
for k in range(0, len(x[i])):
if x[i][k] > 0:
pass # do nothing since it would be effectively replacing x with x
else:
x[i][k] = 0
return x
def arctan(x, derivative=False):
if(derivative == True):
return (np.cos(x) ** 2)
return np.arctan(x)
def step(x, derivative=False):
if(derivative == True):
for i in range(0, len(x)):
for k in range(len(x[i])):
if x[i][k] > 0:
x[i][k] = 0
return x
for i in range(0, len(x)):
for k in range(0, len(x[i])):
if x[i][k] > 0:
x[i][k] = 1
else:
x[i][k] = 0
return x
def squash(x, derivative=False):
if(derivative == True):
for i in range(0, len(x)):
for k in range(0, len(x[i])):
if x[i][k] > 0:
x[i][k] = (x[i][k])/(1+x[i][k])
else:
x[i][k] = (x[i][k]) / (1 - x[i][k])
return x
for i in range(0, len(x)):
for k in range(0, len(x[i])):
x[i][k] = (x[i][k]) / (1 + abs(x[i][k]))
return x
def gaussian(x, derivative=False):
if (derivative == True):
for i in range(0, len(x)):
for k in range(0, len(x[i])):
x[i][k] = -2* x[i][k] * np.exp(-x[i][k] ** 2)
for i in range(0, len(x)):
for k in range(0, len(x[i])):
x[i][k] = np.exp(-x[i][k] ** 2)
return x
输入x是前一层的输出矩阵乘以权重矩阵的结果。输出x是要用于下一层的矩阵上的激活函数的结果。
我是否按照预期使用的方式实施它们，或者其中一些不能达到预期效果？