piobab

def L_layer_model(X, Y, nn_architecture, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):
    np.random.seed(1)
    # keep track of cost
    costs = []
    
    # Parameters initialization.
    parameters = initialize_parameters(nn_architecture)
    
    # Loop (gradient descent)
    for i in range(0, num_iterations):

piobab

def update_parameters(parameters, grads, learning_rate):
    L = len(parameters) // 2 # number of layers in the neural network

    for l in range(1, L):
        parameters["W" + str(l)] = parameters["W" + str(l)] - learning_rate * grads["dW" + str(l)]
        parameters["b" + str(l)] = parameters["b" + str(l)] - learning_rate * grads["db" + str(l)]

    return parameters

piobab

def L_model_backward(AL, Y, parameters, forward_cache, nn_architecture):
    grads = {}
    number_of_layers = len(nn_architecture)
    m = AL.shape[1]
    Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
    
    # Initializing the backpropagation
    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
    dA_prev = dAL

piobab

def compute_cost(AL, Y):
    m = Y.shape[1]

    # Compute loss from AL and y
    logprobs = np.multiply(np.log(AL),Y) + np.multiply(1 - Y, np.log(1 - AL))
    # cross-entropy cost
    cost = - np.sum(logprobs) / m

    cost = np.squeeze(cost)
    

piobab

def L_model_forward(X, parameters, nn_architecture):
    forward_cache = {}
    A = X
    number_of_layers = len(nn_architecture)
    
    for l in range(1, number_of_layers):
        A_prev = A 
        W = parameters['W' + str(l)]
        b = parameters['b' + str(l)]
        activation = nn_architecture[l]["activation"]

piobab

def sigmoid(Z):
    S = 1 / (1 + np.exp(-Z))
    return S

def relu(Z):
    R = np.maximum(0, Z)
    return R

def sigmoid_backward(dA, Z):
    S = sigmoid(Z)

piobab

import numpy as np
import matplotlib.pyplot as plt

nn_architecture = [
    {"layer_size": 4, "activation": "none"}, # input layer
    {"layer_size": 5, "activation": "relu"},
    {"layer_size": 4, "activation": "relu"},
    {"layer_size": 3, "activation": "relu"},
    {"layer_size": 1, "activation": "sigmoid"}
]