train_simple_autoencoder.py

# compile the model with adam optimizer and mean squeared error as its loss function
autoencoder.compile(optimizer='adam', loss='mse', metrics=['mse'])

# some constants for training
BATCH_SIZE = 32
SHUFFLE_BUFFER_SIZE = 100

# shuffle and batch the examples
train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
eval_dataset = eval_dataset.batch(BATCH_SIZE)

# train the model
autoencoder.fit(train_dataset, epochs=100, validation_data=eval_dataset)