neural-networks-from-scratch | An implementation of convolutional networks in NumPy | Machine Learning library

def forward_propagation(X, W1, b1, W2, b2, W3, b3):
    forward_params = {}

    Z1 = np.dot(W1, X.T) + b1
    A1 = relu(Z1)
    Z2 = np.dot(W2, A1) + b2
    A2 = relu(Z2)
    Z3 = np.dot(W3, A2) + b3
    A3 = sigmoid(Z3)

    forward_params = {
        "Z1": Z1,
        "A1": A1,
        "Z2": Z2,
        "A2": A2,
        "Z3": Z3,
        "A3": A3
        }

    return forward_params



def backward_propagation(forward_params, X, Y):
    A3 = forward_params["A3"]
    Z3 = forward_params["Z3"]
    A2 = forward_params["A2"]
    Z2 = forward_params["Z2"]
    A1 = forward_params["A1"]
    Z1 = forward_params["Z1"]

    data_size = Y.shape[1]

    dZ3 = A3 - Y
    dW3 = np.dot(dZ3, A2.T) / data_size

    db3 = np.sum(dZ3, axis=1) / data_size


    dZ2 = np.dot(dW3.T, dZ3) * prime_relu(Z2)
    dW2 = np.dot(dZ2, A1.T) / data_size
    db2 = np.sum(dZ2, axis=1) / data_size

    db2 = np.reshape(db2, (db2.shape[0], 1))


    dZ1 = np.dot(dW2.T, dZ2) * prime_relu(Z1)
    dW1 = np.dot(dZ1, X) / data_size
    db1 = np.sum(dZ1, axis=1) / data_size

    db1 = np.reshape(db1, (db1.shape[0], 1))

    grads = {
        "dZ3": dZ3,
        "dW3": dW3,
        "db3": db3,
        "dZ2": dZ2,
        "dW2": dW2,
        "db2": db2,
        "dZ1": dZ1,
        "dW1": dW1,
        "db1": db1,
    }

    return grads