Comments (2)
One way is to look at scripts/create_datasets.py
, we convert datasets into a TFRecord file of tf.Example with two fields:
image
is a string containing the png encoded image.label
is an int64 (unused, you can set it to 0)
Once you created two record files for your dataset (train and test), you can simply modify libml/data.py
to add your dataset to the dictionary. And then when calling lag.py
you would need to add --dataset=your_dataset_name
.
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Thank you very much for the answer :)
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