model = CNN() history = model.fit(xTrainData, yTrainData, batch_size = 16, epochs = 20, validation_split= 0.25, verbose = 2, ) # model_test testEvaluate = model.evaluate(xTestData, yTestData, verbose=0) print("loss: " + str(testEvaluate[0]) + "\t accuracy: " + str(testEvaluate[1])) #%% Save weights model.save("my_h5_model.h5") model.save_weights("covid19_weights.h5") #%% Load weights model.load_weights("my_h5_model.h5") #%% Plotting print(history.history.keys()) #Accuracy and Loss accuracy = history.history['accuracy'] val_accuracy = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(accuracy))
batch_size=batch_size, target_size=shape, class_mode='categorical', color_mode='grayscale') valid_gen = data_gen.flow_from_directory(path_valid, batch_size=batch_size, target_size=shape, class_mode='categorical', color_mode='grayscale') history = [] try: history = model.fit_generator(train_gen, steps_epoch, epochs, valid_gen, valid_steps) print("Saving weights") model.save_weights(obj_weight) print("Saving history") pickle.dump(history.history, open(obj_history, 'wb')) except KeyboardInterrupt: print("\n\n --- Interruption ---\n ---Saving weights---") model.save_weights(obj_weight) print(" ---Saving history---") pickle.dump(history.history, open(obj_history, 'wb')) sys.exit(0)