72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
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"""
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File: train_gender_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 gender 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 utils.datasets import DataManager
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from models.cnn import mini_XCEPTION
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from utils.data_augmentation import ImageGenerator
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from utils.datasets import split_imdb_data
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# parameters
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batch_size = 32
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num_epochs = 1000
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validation_split = .2
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do_random_crop = False
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patience = 100
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num_classes = 2
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dataset_name = 'imdb'
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input_shape = (64, 64, 1)
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if input_shape[2] == 1:
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grayscale = True
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images_path = '../datasets/imdb_crop/'
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log_file_path = '../trained_models/gender_models/gender_training.log'
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trained_models_path = '../trained_models/gender_models/gender_mini_XCEPTION'
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# data loader
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data_loader = DataManager(dataset_name)
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ground_truth_data = data_loader.get_data()
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train_keys, val_keys = split_imdb_data(ground_truth_data, validation_split)
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print('Number of training samples:', len(train_keys))
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print('Number of validation samples:', len(val_keys))
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image_generator = ImageGenerator(ground_truth_data, batch_size,
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input_shape[:2],
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train_keys, val_keys, None,
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path_prefix=images_path,
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vertical_flip_probability=0,
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grayscale=grayscale,
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do_random_crop=do_random_crop)
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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',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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model.summary()
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# model callbacks
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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/2), verbose=1)
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csv_logger = CSVLogger(log_file_path, append=False)
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model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
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model_checkpoint = ModelCheckpoint(model_names,
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monitor='val_loss',
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verbose=1,
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save_best_only=True,
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save_weights_only=False)
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callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
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# training model
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model.fit_generator(image_generator.flow(mode='train'),
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steps_per_epoch=int(len(train_keys) / batch_size),
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epochs=num_epochs, verbose=1,
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callbacks=callbacks,
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validation_data=image_generator.flow('val'),
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validation_steps=int(len(val_keys) / batch_size))
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