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TF2.0: Translation model: Error when restoring the saved model: Unresolved object in checkpoint (root).optimizer.iter: attributes
Asked Answered
python-3.xtensorflownlptensorflow2.0machine-translation
T

3

23

I am trying to restore the checkpoints and predict on different sentences NMT Attention Model. While restoring the checkpoints and predicting, I am getting gibberish results with warning below:

   Unresolved object in checkpoint (root).optimizer.iter: attributes {
  name: "VARIABLE_VALUE"
  full_name: "Adam/iter"
  checkpoint_key: "optimizer/iter/.ATTRIBUTES/VARIABLE_VALUE"
}

Below is the additional warnings that I am getting and the results:

WARNING: Logging before flag parsing goes to stderr.
W1008 09:57:52.766877 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer.iter
W1008 09:57:52.767037 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer.beta_1
W1008 09:57:52.767082 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer.beta_2
W1008 09:57:52.767120 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer.decay
W1008 09:57:52.767155 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer.learning_rate
W1008 09:57:52.767194 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.embedding.embeddings
W1008 09:57:52.767228 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.gru.state_spec
W1008 09:57:52.767262 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.fc.kernel
W1008 09:57:52.767296 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.fc.bias
W1008 09:57:52.767329 4594230720 util.py:244] Unresolved object in checkpoint: (root).encoder.embedding.embeddings
W1008 09:57:52.767364 4594230720 util.py:244] Unresolved object in checkpoint: (root).encoder.gru.state_spec
W1008 09:57:52.767396 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.gru.cell.kernel
W1008 09:57:52.767429 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.gru.cell.recurrent_kernel
W1008 09:57:52.767461 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.gru.cell.bias
W1008 09:57:52.767493 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.attention.W1.kernel
W1008 09:57:52.767526 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.attention.W1.bias
W1008 09:57:52.767558 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.attention.W2.kernel
W1008 09:57:52.767590 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.attention.W2.bias
W1008 09:57:52.767623 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.attention.V.kernel
W1008 09:57:52.767657 4594230720 util.py:244] Unresolved object in checkpoint: (root).decoder.attention.V.bias
W1008 09:57:52.767688 4594230720 util.py:244] Unresolved object in checkpoint: (root).encoder.gru.cell.kernel
W1008 09:57:52.767721 4594230720 util.py:244] Unresolved object in checkpoint: (root).encoder.gru.cell.recurrent_kernel
W1008 09:57:52.767755 4594230720 util.py:244] Unresolved object in checkpoint: (root).encoder.gru.cell.bias
W1008 09:57:52.767786 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.embedding.embeddings
W1008 09:57:52.767818 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.fc.kernel
W1008 09:57:52.767851 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.fc.bias
W1008 09:57:52.767884 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).encoder.embedding.embeddings
W1008 09:57:52.767915 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.gru.cell.kernel
W1008 09:57:52.767949 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.gru.cell.recurrent_kernel
W1008 09:57:52.767981 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.gru.cell.bias
W1008 09:57:52.768013 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W1.kernel
W1008 09:57:52.768044 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W1.bias
W1008 09:57:52.768077 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W2.kernel
W1008 09:57:52.768109 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.W2.bias
W1008 09:57:52.768143 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.V.kernel
W1008 09:57:52.768175 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).decoder.attention.V.bias
W1008 09:57:52.768207 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).encoder.gru.cell.kernel
W1008 09:57:52.768239 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).encoder.gru.cell.recurrent_kernel
W1008 09:57:52.768271 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).encoder.gru.cell.bias
W1008 09:57:52.768303 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.embedding.embeddings
W1008 09:57:52.768335 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.fc.kernel
W1008 09:57:52.768367 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.fc.bias
W1008 09:57:52.768399 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).encoder.embedding.embeddings
W1008 09:57:52.768431 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.gru.cell.kernel
W1008 09:57:52.768463 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.gru.cell.recurrent_kernel
W1008 09:57:52.768495 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.gru.cell.bias
W1008 09:57:52.768527 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W1.kernel
W1008 09:57:52.768559 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W1.bias
W1008 09:57:52.768591 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W2.kernel
W1008 09:57:52.768623 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.W2.bias
W1008 09:57:52.768654 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.V.kernel
W1008 09:57:52.768686 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).decoder.attention.V.bias
W1008 09:57:52.768718 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).encoder.gru.cell.kernel
W1008 09:57:52.768750 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).encoder.gru.cell.recurrent_kernel
W1008 09:57:52.768782 4594230720 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).encoder.gru.cell.bias
W1008 09:57:52.768816 4594230720 util.py:252] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.
Input: <start> hola <end>
Predicted translation: ? attack now relax hello

The warning at the very end says:

'A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used...' what does it mean?

Trivia answered 8/10, 2019 at 15:17 Comment(0)
D
32

It means you are not using all the checkpointed values you have restored.

It happens because you are restoring a model that has training information (such as optimizer variables) but you are only using it for prediction, not training. When predicting, you don't need the saved optimizer values, which is why the program is telling you they were not used.

If you were using this restored model for training on new data, this warning would disappear.

You could silence these warning with model.load_weights(...).expect_partial() or tf.train.Checkpoint.restore(...).expect_partial().

A better solution would be to only save the variables required for inference when training:

saver = tf.train.Saver(tf.model_variables())

tf.model_variables() is the subset of Variable objects that are used in the model for inference (see tensorflow doc).

Disclaimer answered 3/1, 2020 at 17:18 Comment(0)
V
5

Anyone hitting this error in Tensorflow Object Detection, a working solution to continue training, update num_steps value in the pipeline.config to a higher value than the previous run:

Original:

  num_steps: 25000
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .04
          total_steps: 25000

Updated:

  num_steps: 50000
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .04
          total_steps: 50000
Valladolid answered 1/7, 2021 at 10:44 Comment(0)
M
1

i used : tf.train.Checkpoint.restore(...).expect_partial() to restore my checkpoints and use it for inferences. it worked for me

Miskolc answered 28/9, 2020 at 12:20 Comment(0)

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