Deep
  • Introduction
  • First Chapter
  • LSTM - 1. encoder
  • LSTM - 2. decode_training_set
  • Symbolic Reasoning
  • GAN & VA
  • Unsupervised 1 - traditional
  • Unsupervised 2 - Autoencoder
  • Torch - Basic n NN Basic
  • Torch - nn
  • Torch - nn - Save/Load & STATE_DICT
  • Torch - cnn
  • TF
  • Trick_1
  • Trick_2
  • Trick_TODO
  • F1
  • Save & restore
  • GRAPH 1
  • GRAPH 2
  • Spark RDD
  • Secure AI
  • FasterRCNN
  • Terms
  • HandsonML 8. Dimension Reduction
  • HandsonML 9. Unsupervised Learning
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  • tf.train.Saver
  • tf.saved_model.simple_save

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Save & restore

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assets/
assets.extra/
variables/ #  learned weights
    variables.data-*****-of-*****
    variables.index
saved_model.pb # graph structure

tf.train.Saver

# construct graph!
...
# add save/restore ops
saver = tf.train.Saver()
...
# save after training
save_path = saver.save(sess, "/tmp/model.ckpt")

you need to reconstruct graph to restore.

Use case = splitting training into separate sessions and want a quick way to resume training.

tf.saved_model.simple_save

simple_save(sess,
            export_dir,
            inputs={"myInput": x},
            outputs={"myOutput": y})
https://medium.com/@jsflo.dev/saving-and-loading-a-tensorflow-model-using-the-savedmodel-api-17645576527