plt.imshow(train_set_x_orig[index]) plt.show() print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") + "' picture.") ''' # Flatten the images train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T # Normalise image values train_set_x = train_set_x_flatten / 255. test_set_x = test_set_x_flatten / 255. # Create model instance model = LogisticRegression() # Fit model to the data model.fit(train_set_x, train_set_y) # Train the model model.train(2400, verbose=True) # Predict values predictions = model.predict(test_set_x) # Check accuracy model.print_accuracy(predictions, test_set_y) # Plot training loss model.plot_cost()