Sigmoid_Relu.py

def linear_forward(W,A,b):
    """
    Implement the linear part of a layer's forward propagation.
    Arguments:
    A -- activations from previous layer (or input data): (size of previous layer, number of examples)
    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
    b -- bias vector, numpy array of shape (size of the current layer, 1)
    Returns:
    Z -- the input of the activation function, also called pre-activation parameter 
    cache -- a python dictionary containing "A", "W" and "b" ; stored for computing the backward pass efficiently
    """
    Z = W.dot(A) + b
    assert(Z.shape == (W.shape[0], A.shape[1]))
    cache = (W,A,b)
    return Z, cache

def sigmoid(Z):
    """
    INPUTS:
    Z: this is WX+b
    
    RETURN:
    σ(Z)=σ(WA+b) =1/1+(e−(Z))  
    """
    A = 1/(1+np.exp(-Z))
    
    return A, Z

def relu(Z):
    """
    INPUTS:
    Z: this is WX+b
    
    RETURN:
    max between 0 and Z   
    """
    A = np.maximum(0,Z)
    
    assert(A.shape == Z.shape)
    
    cache = Z 
    return A, cache