nmt_with_attention no longer works on Colab
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Description
Hello,
I got stuck halfway through in the nmt_with_attention Colab:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-34-f0271e0d820d>](https://localhost:8080/#) in <cell line: 1>()
----> 1 logits = decoder(ex_context, ex_tar_in)
2
3 print(f'encoder output shape: (batch, s, units) {ex_context.shape}')
4 print(f'input target tokens shape: (batch, t) {ex_tar_in.shape}')
5 print(f'logits shape shape: (batch, target_vocabulary_size) {logits.shape}')
1 frames
[<ipython-input-30-dcee1b38b45f>](https://localhost:8080/#) in call(self, context, x, state, return_state)
13
14 # 2. Process the target sequence.
---> 15 x, state = self.rnn(x, initial_state=state)
16 shape_checker(x, 'batch t units')
17
ValueError: Exception encountered when calling Decoder.call().
too many values to unpack (expected 2)
Arguments received by Decoder.call():
• context=tf.Tensor(shape=(64, 18, 256), dtype=float32)
• x=tf.Tensor(shape=(64, 16), dtype=int64)
• state=None
• return_state=False
When I first tried this notebook a long time ago, it worked though.
Regards.
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Research direction
Open the nmt_with_attention Colab and reproduce the failure at logits = decoder(ex_context, ex_tar_in). Inspect Decoder.call() around x, state = self.rnn(x, initial_state=state) and the surrounding notebook cells. Done means the notebook executes that cell and prints the encoder, target-token, and logits shapes without the ValueError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, tensorflow
- Domain
- documentation, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100