73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
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"""
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File: train_emotion_classifier.py
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Author: Octavio Arriaga
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Email: arriaga.camargo@gmail.com
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Github: https://github.com/oarriaga
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Description: Train emotion classification model
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"""
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from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping
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from keras.callbacks import ReduceLROnPlateau
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from keras.preprocessing.image import ImageDataGenerator
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from models.cnn import mini_XCEPTION
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from utils.datasets import DataManager
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from utils.datasets import split_data
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from utils.preprocessor import preprocess_input
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# parameters
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batch_size = 32
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num_epochs = 10000
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input_shape = (64, 64, 1)
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validation_split = .2
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verbose = 1
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num_classes = 7
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patience = 50
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base_path = '../trained_models/emotion_models/'
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# data generator
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data_generator = ImageDataGenerator(
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featurewise_center=False,
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featurewise_std_normalization=False,
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rotation_range=10,
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width_shift_range=0.1,
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height_shift_range=0.1,
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zoom_range=.1,
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horizontal_flip=True)
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# model parameters/compilation
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model = mini_XCEPTION(input_shape, num_classes)
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model.compile(optimizer='adam', loss='categorical_crossentropy',
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metrics=['accuracy'])
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model.summary()
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datasets = ['fer2013']
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for dataset_name in datasets:
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print('Training dataset:', dataset_name)
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# callbacks
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log_file_path = base_path + dataset_name + '_emotion_training.log'
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csv_logger = CSVLogger(log_file_path, append=False)
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early_stop = EarlyStopping('val_loss', patience=patience)
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reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,
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patience=int(patience/4), verbose=1)
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trained_models_path = base_path + dataset_name + '_mini_XCEPTION'
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model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
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model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,
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save_best_only=True)
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callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
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# loading dataset
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data_loader = DataManager(dataset_name, image_size=input_shape[:2])
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faces, emotions = data_loader.get_data()
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faces = preprocess_input(faces)
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num_samples, num_classes = emotions.shape
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train_data, val_data = split_data(faces, emotions, validation_split)
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train_faces, train_emotions = train_data
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model.fit_generator(data_generator.flow(train_faces, train_emotions,
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batch_size),
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steps_per_epoch=len(train_faces) / batch_size,
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epochs=num_epochs, verbose=1, callbacks=callbacks,
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validation_data=val_data)
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