activations.py

import numpy as np

def relu(x):
    ''' Rectified Linear Unit (ReLU) '''
    return np.maximum(0., x)
    
def drelu(x):
    ''' derivative of ReLU '''
    y=np.zeros(x.shape) 
    y[np.where(x>0)]=1
    return y 

def softmax(x):    
    ''' softmax '''
    z = np.exp(x)
    return z / np.sum(z,axis=-1,keepdims=True)