INNER CODE UNIT · Python

patience

otaha178/Emotion-recognition · train_emotion_classifier.py:48

                                  patience=int(patience/4), verbose=1)
trained_models_path = base_path + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,
                                                    save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]

# loading dataset
faces, emotions = load_fer2013()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
xtrain, xtest,ytrain,ytest = train_test_split(faces, emotions,test_size=0.2,shuffle=True)
model.fit_generator(data_generator.flow(xtrain, ytrain,
                                            batch_size),
                        steps_per_epoch=len(xtrain) / batch_size,
                        epochs=num_epochs, verbose=1, callbacks=callbacks,
                        validation_data=(xtest,ytest))

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