|
""" |
|
Description: Train emotion classification model |
|
""" |
|
|
|
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 |
|
|
|
|
|
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 = 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 = mini_XCEPTION(input_shape, num_classes) |
|
model.compile(optimizer='adam', loss='categorical_crossentropy', |
|
metrics=['accuracy']) |
|
model.summary() |
|
|
|
|
|
|
|
|
|
|
|
|
|
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] |
|
|
|
|
|
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)) |
|
|
