main.py

import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
from helper_functions import *
import tkinter as tk
from tkinter import filedialog

train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
m_train = train_x_orig.shape[0]
num_px = train_x_orig.shape[1]
m_test = test_x_orig.shape[0]
train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T 
test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T


train_x = train_x_flatten/255.
test_x = test_x_flatten/255.
layers_dims = [12288, 20, 7, 5, 1]

def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):
    np.random.seed(1)
    costs = []                         
    parameters = initialize_parameters_deep(layers_dims)
    for i in range(0, num_iterations):
        AL, caches = L_model_forward(X, parameters) 
        
        cost = compute_cost(AL, Y)
    
        grads = L_model_backward(AL, Y, caches)
        parameters = update_parameters(parameters, grads, learning_rate)
        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)
            
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()
    
    return parameters

parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)

pred_train = predict(train_x, train_y, parameters)

pred_test = predict(test_x, test_y, parameters)
images = "C:/Users/prate/OneDrive/Documents/Python Projects/cat-vs-noncat-deep-network/test-images"

def load_file(testimage):
    testimage = tk.filedialog.askopenfilename(initialdir = images)
    return testimage

testimage = "C:/Users/prate/OneDrive/Documents/Python Projects/cat-vs-noncat-deep-network/test-images/cat"
my_image = load_file(testimage)
my_label_y = [1]
image = np.array(ndimage.imread(my_image, flatten=False))
my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((num_px*num_px*3,1))
my_image = my_image/255.
my_predicted_image = predict(my_image, my_label_y, parameters)

plt.imshow(image)
print ("y = " + str(np.squeeze(my_predicted_image)) + ", your L-layer model predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture.")