Comments (5)
Actually, tutorial 1 only illustrates training. Inference with tf.nn.dynamic_rnn
decoder is tricky. All I can think of is defining inference dynamic_rnn
decoder that shares weights with training decoder, but takes as input a [1, batch_size] int32 tensor. That is, one timestep of a minibatch. And then we unroll decoder manually in for loop for every timestep, calling session.run
for each step.
This is totally impractical with TF. It might be a nice illustration, but I think it more of a tutorial 2/raw_rnn scope. Might worth focusing attention on it in the text though. What do you think?
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The two-graph approach is used in raindeer/seq2seq_experiments, though they don't use dynamic_rnn
. I think you're right about it being closer to tutorial 2's scope, but I'm not sure if it's worth putting a lot of energy into now that the seq2seq API is about to change (again!). However, it's probably worth noting that inference is difficult in the text of tutorial 1 - it might save someone a bit of time.
from tensorflow-seq2seq-tutorials.
Any info on if tf.nn.raw_rnn
would change? I find new TF's seq2seq API rather cumbersome, hiding important details while not providing much simplifaction over tf.nn.raw_rnn
. Perhaps using tf.nn.raw_rnn
directly is a better way.
from tensorflow-seq2seq-tutorials.
I added a bit that explains that inference is not possible with tf.nn.dynamic_rnn
. Closing now.
from tensorflow-seq2seq-tutorials.
Thank you! Unfortunately I don't know if raw_rnn
is changing - I haven't heard anything about that happening, but I'm not very in the know.
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Related Issues (20)
- new changes in tensorflow HOT 4
- My own embedding HOT 1
- Multilayered Bidirectional seq2seq HOT 6
- Potential bug of the dynamic_decoder HOT 2
- OOM error after running some batches HOT 3
- Beam search toy example
- Using the same weights in two different positions seems wrong
- module 'tensorflow.contrib.seq2seq' has no attribute 'prepare_attention' HOT 2
- there is no input argument when loop_fn method is called. HOT 1
- Increasing max sequence length and vocab size HOT 1
- How does inference work? Always getting same predictions HOT 2
- Duplicated computation of decoder outputs?
- toy task-example1-error
- testing
- Attempted an implementation
- good job
- Logits and labes have different shapes when computing cross-entropy loss HOT 1
- decoder input
- ask for help
- How to connect multilayered encoder to decoder?
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