How to feed TFRecord data (over 60GB) to the DEC-keras model?

[wangjiangyuan]

Thanks for your great implementation!
I' ve tried to solve classification problem whose input data have the shape of 1000*221 by DEC model.
I want to train my data with over 80 thousand data (very large size [8000000,1000,221],dtype=float32 about 60GB ), so it's not possible load whole dataset into python array.
After googling, I found tf.TFRecord helps me to get out this capacity problem.

I followed the tutorial in the official TensorFlow site to write TFRecord file and I can load the TFReocrd into the conventional Keras Model. However, I can't find how to feed into the DEC-model. The input (mnist) of DEC-model is one numpy file that has the shape [70000,784].

Like flowing：

dataset = tf.data.TFRecordDataset(filenames=[filenames])
parsed_dataset = dataset.map(_parse_function, num_parallel_calls=8)
final_dataset = parsed_dataset.shuffle(buffer_size=number_of_sample).batch(10)
iterator = dataset.make_one_shot_iterator()
parsed_record = iterator.get_next()
feature, label = parsed_record['feature'], parsed_record['label']
#keras
inputs = keras.Input(shape=(1000，221 ), name='feature', tensor=feature)
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss='categorical_crossentropy',
metrics=['accuracy','categorical_crossentropy'],
target_tensors=[label]
)
train_model.fit(epochs= 30,
steps_per_epoch= 800000/256)