def update_parameters(parameters, grads, learning_rate=1.2):
    """
    Updates parameters using the gradient descent update rule given above

    Arguments:
    parameters -- python dictionary containing your parameters
    grads -- python dictionary containing your gradients

    Returns:
    parameters -- python dictionary containing your updated parameters
    """
    # Retrieve each parameter from the dictionary "parameters"
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]

    # Retrieve each gradient from the dictionary "grads"
    dW1 = grads["dW1"]
    db1 = grads["db1"]
    dW2 = grads["dW2"]
    db2 = grads["db2"]

    # Update rule for each parameter
    W1 -= learning_rate * dW1
    b1 -= learning_rate * db1
    W2 -= learning_rate * dW2
    b2 -= learning_rate * db2

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}

    return parameters
def update_parameters ( parameters , grads , learning_rate = 1.2 ) : W1 = parameters [ "W1" ] b1 = parameters [ "b1" ] W2 = parameters [ "W2" ] b2 = parameters [ "b2" ] dW1 = grads [ "dW1" ] db1 = grads [ "db1" ] dW2 = grads [ "dW2" ] db2 = grads [ "db2" ] W1 -= learning_rate * dW1 b1 -= learning_rate * db1 W2 -= learning_rate * dW2 b2 -= learning_rate * db2 parameters = { "W1" : W1 , "b1" : b1 , "W2" : W2 , "b2" : b2 } return parameters
Updates parameters using the gradient descent update rule given above

    Arguments:
    parameters -- python dictionary containing your parameters
    grads -- python dictionary containing your gradients

    Returns:
    parameters -- python dictionary containing your updated parameters