callbacks = [ EarlyStopping(monitor='loss', patience=3, mode='auto'), ModelCheckpoint(filepath='best' + MODEL_NAME + '.h5', monitor='val_loss', save_best_only=True, mode='auto') ] history_object = model.fit_generator( training_generator, len(train_samples) // BATCH_SIZE, epochs=EPOCH, verbose=1, callbacks=callbacks, validation_data=validation_generator, validation_steps=len(validation_samples) // BATCH_SIZE, class_weight=None, workers=1, initial_epoch=0, use_multiprocessing=False, max_queue_size=10) t2 = time.time() print('Training model complete...') print(' Time Taken:', (t2 - t1) / 60, 'minutes') print('Loss: ') print(history_object.history['loss']) print('Validation Loss: ') print(history_object.history['val_loss'])
batch_size=BATCH_SIZE, type=type_) print('Training model...') model = CNNModel() callbacks = [ EarlyStopping(monitor='val_loss', patience=3), ModelCheckpoint(filepath='best' + MODEL_NAME + '.h5', monitor='val_loss', save_best_only=True) ] history_object = model.fit_generator(training_generator, samples_per_epoch= \ len(train_samples)//BATCH_SIZE, validation_data=validation_generator, \ validation_steps=len(validation_samples)//BATCH_SIZE, callbacks=callbacks, epochs=EPOCH, verbose=1) print('Training model complete...') print(history_object.history.keys()) print('Loss') print(history_object.history['loss']) print('Validation Loss') print(history_object.history['val_loss']) plt.figure(figsize=[10, 8]) plt.plot(np.arange(1, len(history_object.history['loss']) + 1), history_object.history['loss'], 'r',