Keras autoencoder with Tensorflow Dataset API and logging to Tensorboard

I have simple autoencoder in Keras, I want to use logging to tensorboard (thus I need passing validation data), and load the data from TFRecord using the Tensorflow Dataset API using prefetch. I read some articles about it, but they either omitted validation pipeline, or the fact that passing the data directly without feed dict is significantly slower.

The source code is

import tensorflow as tf
from keras.losses import mean_squared_error
from keras.models import Sequential, Model
from keras.layers import Dense, Input, Flatten, Reshape, Convolution2D,     Convolution2DTranspose, Conv2D, Conv2DTranspose
from keras.optimizers import Adam
from keras import backend as K
from keras.callbacks import TensorBoard

def create_dataset(tf_record, batch_size):
    data = tf.data.TFRecordDataset(tf_record)
    data = data.map(TFReader._parse_example_encoded, num_parallel_calls=8)
    data = data.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=100))
    data = data.batch(batch_size, drop_remainder=True)
    data = data.prefetch(4)
    return data


def main(_):
    batch_size = 8  # todo: check and try bigger
    data = create_dataset('../../datasets/anime/no-game-no-life-ep-2.tfrecord', batch_size)
    iterator = data.make_one_shot_iterator()

    K.set_image_data_format('channels_last')  # set format

    input_tensor = Input(tensor=iterator.get_next())
    out = Conv2D(8, (3, 3), activation='elu', border_mode='valid', batch_input_shape=(batch_size, 432, 768, 3))(input_tensor)
    out = Conv2D(16, (3, 3), activation='elu', border_mode='valid')(out)
    out = Conv2D(32, (3, 3), activation='elu', border_mode='valid', name='bottleneck')(out)
    out = Conv2DTranspose(32, (3, 3), activation='elu', padding='valid')(out)
    out = Conv2DTranspose(16, (3, 3), activation='elu', padding='valid')(out)
    out = Conv2DTranspose(8, (3, 3), activation='elu', padding='valid')(out)
    out = Conv2D(3, (3, 3), activation='elu', padding='same')(out)
    m = Model(inputs=input_tensor, outputs=out)
    m.compile(loss=mean_squared_error, optimizer=Adam(), target_tensors=iterator.get_next())
    print(m.summary())
    tensorboard = TensorBoard(
        log_dir='logs/anime', histogram_freq=5, embeddings_freq=5, embeddings_layer_names=['bottleneck'],
    write_images=True, embeddings_data=iterator.get_next(), embeddings_metadata='embeddings.tsv')
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    set_session(tf.Session(config=config))

    history = m.fit(steps_per_epoch=100, epochs=50, verbose=1,
                validation_data=(iterator.get_next(), iterator.get_next()),
                validation_steps=4,
                callbacks=[tensorboard]
                )


if __name__ == '__main__':
    tf.app.run()

The training itself starts, the first epoch trains, but then it fails during validation by

File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\pydevd.py", line 1741, in <module>
main()
  File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\pydevd.py", line 1735, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\pydevd.py", line 1135, in run
pydev_imports.execfile(file, globals, locals)  # execute the script
File "C:\Users\Azathoth\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\183.5429.31\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "E:/Projects/anime-style-transfer/code/neural_style_transfer/anime_dimension_reduction_keras.py", line 95, in <module>
tf.app.run()
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "E:/Projects/anime-style-transfer/code/neural_style_transfer/anime_dimension_reduction_keras.py", line 78, in main
callbacks=[tensorboard]
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1039, in fit
validation_steps=validation_steps)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_arrays.py", line 217, in fit_loop
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\callbacks.py", line 79, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\callbacks.py", line 912, in on_epoch_end
raise ValueError("If printing histograms, validation_data must be "
ValueError: If printing histograms, validation_data must be provided, and cannot be a generator.

And I assume the problem is somewhere with passing the validation data, because it uses directly the input tensor from training tfrecord.

Although I don't need separate training and validation data, so if there would be any way to tell Keras that it can validate on the same inputs, it would be fine as long as I get my TensorBoard logs.