Comments (8)
The error seems come from gen_o
has not been properly converted to a Tensor from a TenorArray. Could you add gen_o.stack()
in your code? I also updated the rmc_att and rmc_vdcnn in case that you were using them.
from relgan.
But after adding gen_o.stack()
,Another Error happended:
\oracle\oracle_gan\oracle_train.py", line 314, in get_train_ops
temp_train_op = temp_optimizer.apply_gradients(zip(temp_grads, [temperature]))
lib\site-packages\tensorflow\python\training\optimizer.py", line 472, in apply_gradients
([str(v) for _, _, v in converted_grads_and_vars],))
ValueError: No gradients provided for any variable: ['<tensorflow.python.training.optimizer._RefVariableProcessor object at 0x000001CF5ED1C390>'].
from relgan.
Not sure where you added gen_o.stack()
. Can you confirm if you are using rmc_att or rmc_vdcnn? If so, maybe you can pull it again and see if you can run properly.
from relgan.
I changed the get_losses()
function in oracle_train.py:
from log_pg = tf.reduce_mean(tf.log(gen_o + EPS))
to 'log_pg = tf.reduce_mean(tf.log(gen_o.stack() + EPS))'
I used the default parameters, So I think that the generator and discriminator are both rmc_att, and type of the GAN is standard
from relgan.
OK, there is something wrong with rmc_att
and I fixed it. Also, I changed the default GAN architecture in run.py to rmc_vanilla
, because all the experiments in the paper are based on rmc_vanilla
actually. Can you pull it again?
from relgan.
I downloaded all of your new code again,. This time I think the default GAN architecture is rmc_vanilla
.
However it seems that I'm facing a new Error :
oracle\oracle_gan\oracle_train.py", line 355, in get_metric_summary_op
metric_summary_op = tf.summary.merge(metrics_sum)
lib\site-packages\tensorflow\python\summary\summary.py", line 271, in merge
val = _gen_logging_ops._merge_summary(inputs=inputs, name=name)
lib\site-packages\tensorflow\python\ops\gen_logging_ops.py", line 314, in _merge_summary
"MergeSummary", inputs=inputs, name=name)
lib\site-packages\tensorflow\python\framework\op_def_library.py", line 570, in _apply_op_helper
(input_name, op_type_name, len(values), num_attr.minimum))
ValueError: List argument 'inputs' to 'MergeSummary' Op with length 0 shorter than minimum length 1.
from relgan.
This is because all the metrics are set to False
with the default setting in run.py (line 41-45). You can set at least one of the metrics to True
to avoid this issue.
from relgan.
Oh,solved..
Thank you very much :)
from relgan.
Related Issues (20)
- NLL_gen loss with initialization HOT 14
- Where generated text is being stored? HOT 1
- How much time it took to run a single cycle of 5 discriminator steps and 1 generator step during the adversarial training? HOT 3
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- AttributeError: module 'tensorflow' has no attribute 'truncated_normal_initializer' HOT 1
- Teacher Forcing during Adversarial Training HOT 1
- Why Gumbel-Softmax relaxation applied in sample generation? HOT 4
- Dis loss decays to zero while training oracle HOT 3
- How do i train the model with my own data? HOT 2
- Loss function for LSTM based model HOT 1
- LICENSE? HOT 2
- Questions on reproducing the results HOT 18
- How long is expected to take to complete the pre-training stage (on single GPU) ? HOT 4
- Thank you for your contribution. Can you briefly explain how to run and validate your model? HOT 1
- Improving sample output quality RELGAN. HOT 2
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- [Self-BLEU] what metric values do you get? HOT 4
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