trainModel.py

from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from util.ml import create_prototype_emotion_model

train_dir = 'train'
val_dir = 'test'
train_datagen = ImageDataGenerator(rescale=1./255.)
val_datagen = ImageDataGenerator(rescale=1./255.)
train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=(48, 48),
        batch_size=64,
        color_mode="grayscale",
        class_mode='categorical')
validation_generator = val_datagen.flow_from_directory(
        val_dir,
        target_size=(48, 48),
        batch_size=64,
        color_mode="grayscale",
        class_mode='categorical')

emotion_model = create_prototype_emotion_model()

emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
emotion_model_info = emotion_model.fit_generator(
        train_generator,
        steps_per_epoch=28709 // 64,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=7178 // 64)

emotion_model.save_weights('models/trained_model.h5')