train.py

#!/usr/bin env python

from keras.callbacks import EarlyStopping, ReduceLROnPlateau, CSVLogger, ModelCheckpoint
# DO NOT REMOVE THIS:
from model.cnn_models import *
from utils.data_generator import DataGenerator
from model.amsoftmax import wrap_cnn, amsoftmax_loss

input_shape = (64, 64, 1)
batch_size = 64
num_epochs = 1000
patience = 100
log_file_path = "./log.csv"
cnn = "ResNet18"
trained_models_path = "./trained_models/" + cnn

generator = DataGenerator(dataset="olivettifaces",
                          path="./data/olivetti_faces/olivettifaces.jpg",
                          batch_size=batch_size,
                          input_size=input_shape,
                          is_shuffle=True,
                          data_augmentation=10,
                          validation_split=.2)
num_classes, num_images, training_set_size, validation_set_size = generator.get_number()
print(num_classes, num_images, training_set_size, validation_set_size)


model = wrap_cnn(model=eval(cnn),
                 feature_layer="feature",
                 input_shape=input_shape,
                 num_classes=num_classes)
model.compile(optimizer='adam',
              loss=amsoftmax_loss,
              metrics=['accuracy'])
model.summary()
# callbacks
early_stop = EarlyStopping('loss', 0.1, patience=patience)
reduce_lr = ReduceLROnPlateau('loss', factor=0.1,
                              patience=int(patience / 2), verbose=1)
csv_logger = CSVLogger(log_file_path, append=False)
model_names = trained_models_path + '.{epoch:02d}-{acc:2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
                                   monitor='loss',
                                   verbose=1,
                                   save_best_only=True,
                                   save_weights_only=False)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]

# train model by generator
model.fit_generator(generator=generator.flow('train'),
                    steps_per_epoch=int(training_set_size / batch_size),
                    epochs=num_epochs,
                    verbose=1,
                    callbacks=callbacks,
                    validation_data=generator.flow('validate'),
                    validation_steps=int(validation_set_size) / batch_size)