w3_tools.py

import numpy as np

def backward_propagation(A, X, Y):
    """
    Implements the backward propagation, calculating gradients
    
    Arguments:
    parameters -- python dictionary containing our parameters 
    A -- the output of the neural network of shape (1, number of examples)
    X -- input data of shape (n_x, number of examples)
    Y -- "true" labels vector of shape (n_y, number of examples)
    
    Returns:
    grads -- python dictionary containing gradients with respect to different parameters
    """
    m = X.shape[1]
    
    # Backward propagation: calculate dW, db.
    dZ = A - Y
    dW = 1/m * np.matmul(dZ, X.T)
    db = 1/m * np.sum(dZ, axis = 1, keepdims = True)
    
    grads = {"dW": dW,
             "db": db}
    
    return grads

def update_parameters(parameters, grads, learning_rate = 1.2):
    """
    Updates parameters using the gradient descent update rule
    
    Arguments:
    parameters -- python dictionary containing parameters 
    grads -- python dictionary containing gradients 
    
    Returns:
    parameters -- python dictionary containing updated parameters 
    """
    # Retrieve each parameter from the dictionary "parameters".
    W = parameters["W"]
    b = parameters["b"]
    
    # Retrieve each gradient from the dictionary "grads".
    dW = grads["dW"]
    db = grads["db"]
    
    # Update rule for each parameter.
    W = W - learning_rate * dW
    b = b - learning_rate * db
    
    parameters = {"W": W,
                  "b": b}
    
    return parameters

def train_nn(parameters, A, X, Y):
    # Backpropagation. Inputs: "A, X, Y". Outputs: "grads".
    grads = backward_propagation(A, X, Y)
    
    # Gradient descent parameter update. Inputs: "parameters, grads". Outputs: "parameters".
    parameters = update_parameters(parameters, grads)
    
    return parameters