ANN_Model.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr  7 21:43:56 2019

@author: Enyang
"""
import ANN_Functions as annf
import numpy as np
import matplotlib.pyplot as plt

def L_layer_model(X, Y, num_iterations = 2000, learning_rate = 0.005, print_cost = False, layers_dims= [30000, 20, 7, 5, 1]):
    np.random.seed(1)
    costs = []
    
    # Parameters initialization.
    parameters = annf.initialize_parameters_deep(layers_dims)
    
    # Loop (gradient descent)
    for i in range(0, num_iterations):

        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.
        AL, caches = annf.L_model_forward(X, parameters)
        
        # Compute cost.
        cost = annf.compute_cost(AL, Y)
    
        # Backward propagation.
        grads = annf.L_model_backward(AL, Y, caches)
 
        # Update parameters.
        parameters = annf.update_parameters(parameters, grads, learning_rate)
                
        # Print the cost every 100 training example
        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
        if print_cost and i % 100 == 0:
            costs.append(cost)
            
    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()
    
    return parameters