index = list(range(testSize * NUMBER_OF_CLASS)) random.seed(660) random.shuffle(index) x_val = x_val[index] y_val = y_val[index] model_weights = "./model/aux_model" checkpoint = ModelCheckpoint(model_weights, monitor='val_loss', verbose=0, save_best_only=True, mode='min', save_weights_only=True) a_model = fed_learn.create_model((32, 32, 3), NUMBER_OF_CLASS, init_with_imagenet=False, learning_rate=0.01) plateau = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, verbose=1, min_delta=1e-4, mode='min') early_stopping = EarlyStopping(monitor="val_loss", patience=15) y_train = utils.to_categorical(y_train, NUMBER_OF_CLASS) print(a_model.summary()) a_model.fit(x_share, y_share, validation_data=(x_val, y_val), epochs=5000,
def model_fn(): return fed_learn.create_model((32, 32, 3), NUMBER_OF_CLASS, init_with_imagenet=True, learning_rate=args.learning_rate)
def model_fn(): return fed_learn.create_model((32, 32, 3), 10, init_with_imagenet=False, learning_rate=args.learning_rate)