# Standardize data to have feature values between 0 and 1. train_x = train_x_flatten / 255. test_x = test_x_flatten / 255. return train_x, test_x, train_y, test_y, classes, num_px if __name__ == "__main__": layers_dims = [12288, 20, 10, 1] # 4-layer model m = Main(layers_dims) train_x, test_x, train_y, test_y, classes, num_px = preprocessing() parameters = m.L_layer_model(train_x, train_y, layers_dims, num_epochs=5000, print_cost=True, lambd=0, learning_rate=0.001) pred_train = m.predict(train_x, train_y, parameters) pred_test = m.predict(test_x, test_y, parameters) my_image = "cat.jpg" my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat) fname = "images/" + my_image image = np.array(plt.imread(fname)) my_image = ((np.array( Image.fromarray(image).resize((num_px, num_px), Image.ANTIALIAS)).reshape( (1, num_px * num_px * 3)))).T
test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T num_px = train_x_orig.shape[1] # Standardize data to have feature values between 0 and 1. train_x = train_x_flatten / 255. test_x = test_x_flatten / 255. return train_x, test_x, train_y, test_y, classes, num_px if __name__ == "__main__": layers_dims = [12288, 20, 7, 5, 1] # 5-layer model m = Main(layers_dims) train_x, test_x, train_y, test_y, classes, num_px = preprocessing() parameters = m.L_layer_model(train_x, train_y, layers_dims, num_iterations=2500, print_cost=True) pred_train = m.predict(train_x, train_y, parameters) pred_test = m.predict(test_x, test_y, parameters) my_image = "cat.jpg" my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat) fname = "images/" + my_image image = np.array(plt.imread(fname)) my_image = ((np.array( Image.fromarray(image).resize((num_px, num_px), Image.ANTIALIAS)).reshape( (1, num_px * num_px * 3)))).T