from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping

from keras.callbacks import ReduceLROnPlateau

from keras.preprocessing.image import ImageDataGenerator

from load_and_process import load_fer2013

from load_and_process import preprocess_input

from models.cnn import mini_XCEPTION

from sklearn.model_selection import train_test_split

# parameters

batch_size = 32

num_epochs = 10000

input_shape = (48, 48, 1)

validation_split = .2

verbose = 1

num_classes = 7

patience = 50

base_path = 'models/'

# data generator

data_generator = ImageDataGenerator(

                        featurewise_center=False,

                        featurewise_std_normalization=False,

                        rotation_range=10,

                        width_shift_range=0.1,

                        height_shift_range=0.1,

                        zoom_range=.1,

                        horizontal_flip=True)

# model parameters/compilation

model = mini_XCEPTION(input_shape, num_classes)

model.compile(optimizer='adam', loss='categorical_crossentropy',

              metrics=['accuracy'])

model.summary()

    # callbacks

log_file_path = base_path + '_emotion_training.log'

csv_logger = CSVLogger(log_file_path, append=False)

early_stop = EarlyStopping('val_loss', patience=patience)

reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,

                                  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))