

In [14]:

    
import numpy as np
def sigmoid(x):
    return 1.0 / ( 1.0 + np.exp(-x) )

q 3



In [20]:

    
sigmoid(1.5)









    Out[20]:





0.81757447619364365



In [21]:

    
h0=sigmoid( (0.5 * 9) - 1)



In [22]:

    
h1=sigmoid( ((0.5 * 4) - 1) - h0 )



In [23]:

    
z1 = h1 * (-0.7)



In [24]:

    
y1 = sigmoid(z1)
y1









    Out[24]:





0.4121391408076901

q 4



In [12]:

    
np.exp(0.2) / (1+ np.exp(0.2))**2









    Out[12]:





0.24751657271186001