Гиперполосный поиск Keras с использованием итератора каталогов

Я использую flow_from_directory Tensorflow для сбора большого набора данных изображений, а затем тренируюсь на нем. Я хочу использовать Keras Tuner, но когда я запускаю

tuner.search(test_data_gen, epochs=50, 
              validation_split=0.2, callbacks=[stop_early]) 

Выдает следующую ошибку,

ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'tensorflow.python.keras.preprocessing.image.DirectoryIterator'>] 

Я мало знаю о преобразовании между типами данных в AI, поэтому любая помощь действительно приветствуется.

Вот остальная часть моего кода:

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import IPython.display as display
from PIL import Image, ImageSequence
import os
import pathlib
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import cv2
import datetime
import kerastuner as kt

tf.compat.v1.enable_eager_execution()

epochs = 50
steps_per_epoch = 10
batch_size = 20
IMG_HEIGHT = 200
IMG_WIDTH = 200

train_dir = "Data/Train"
test_dir = "Data/Val"

train_image_generator = ImageDataGenerator(rescale=1. / 255)

test_image_generator = ImageDataGenerator(rescale=1. / 255)

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='sparse')

test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,
                                                         directory=test_dir,
                                                         shuffle=True,
                                                         target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                         class_mode='sparse')


    def model_builder(hp):
        model = keras.Sequential()
        model.add(Conv2D(265, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)))
        model.add(MaxPooling2D())
        model.add(Conv2D(64, 3, padding='same', activation='relu'))
        model.add(MaxPooling2D())
        model.add(Conv2D(32, 3, padding='same', activation='relu'))
        model.add(MaxPooling2D())
        model.add(Flatten())
        model.add(keras.layers.Dense(256, activation="relu"))
        hp_units = hp.Int('units', min_value=32, max_value=512, step=32)
        model.add(keras.layers.Dense(hp_units, activation="relu"))
        model.add(keras.layers.Dense(80, activation="softmax"))
    
        hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
    
        model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),
                      loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                      metrics=['top_k_categorical_accuracy'])
    
        return model
    
    tuner = kt.Hyperband(model_builder,
                         objective='val_accuracy',
                         max_epochs=30,
                         factor=3,
                         directory='Hypertuner_Dir',
                         project_name='AIOS')
    
    stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

и начните поиск с tuner

tuner.search(train_data_gen, epochs=50, validation_split=0.2, callbacks=[stop_early])

# Get the optimal hyperparameters
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]

print(f"""
The hyperparameter search is complete. The optimal number of units in the first densely-connected
layer is {best_hps.get('units')} and the optimal learning rate for the optimizer
is {best_hps.get('learning_rate')}.
""")

model = tuner.hypermodel.build(best_hps)

model.summary()
tf.keras.utils.plot_model(model, to_file="model.png", show_shapes=True, show_layer_names=True, rankdir='TB')
checkpoint_path = "training/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
                                                 save_weights_only=True,
                                                 verbose=1)

os.system("rm -r logs")

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

#history = model.fit(train_data_gen,steps_per_epoch=steps_per_epoch,epochs=epochs,validation_data=test_data_gen,validation_steps=10,callbacks=[cp_callback, tensorboard_callback])
history = model.fit(train_data_gen,steps_per_epoch=steps_per_epoch,epochs=epochs,validation_split=0.2,validation_steps=10,callbacks=[cp_callback, tensorboard_callback])
model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))
model.save('model.h5', include_optimizer=True)

test_loss, test_acc = model.evaluate(test_data_gen)
print("Tested Acc: ", test_acc)
print("Tested Acc: ", test_acc*100, "%")

val_acc_per_epoch = history.history['val_accuracy']
best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))

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