shantanu1109 / coursera-deeplearning.ai-natural-language-processing-specialization Goto Github PK

def back_prop(x, yhat, y, h, W1, W2, b1, b2, batch_size):
    '''
    Inputs: 
        x:  average one hot vector for the context 
        yhat: prediction (estimate of y)
        y:  target vector
        h:  hidden vector (see eq. 1)
        W1, W2, b1, b2:  matrices and biases  
        batch_size: batch size 
     Outputs: 
        grad_W1, grad_W2, grad_b1, grad_b2:  gradients of matrices and biases   
    '''
    
    # Compute z1 as "W1⋅x + b1"
    z1 = np.dot(W1, x) + b1
    
    ### START CODE HERE (Replace instanes of 'None' with your code) ###
    
    # Compute l1 as W2^T (Yhat - Y)
    l1 = np.dot(W2.T,(yhat-y))

    # if z1 < 0, then l1 = 0
    # otherwise l1 = l1
    # (this is already implemented for you)
    
    l1[z1 < 0] = 0 # use "l1" to compute gradients below

    # compute the gradient for W1
    grad_W1 = np.dot(l1, x.T) / batch_size

    # Compute gradient of W2
    grad_W2 = np.dot((yhat-y),h.T) / batch_size
    
    # compute gradient for b1
    grad_b1 = np.sum(l1, axis=1, keepdims=True) / batch_size

    # compute gradient for b2
    grad_b2 = np.sum((yhat-y), axis=1, keepdims=True) / batch_size
    ### END CODE HERE ####
    
    return grad_W1, grad_W2, grad_b1, grad_b2