Comments (6)
Thank you, im my fork I have implemented a second option, similar to the test mode
https://github.com/Sangheli/chatbot-retrieval
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Two options:
- You could try using the
X
andy
arguments you mention above instead of theinput_fn
. Then you can make X an iterator of examples instead of just a single one. - You could modify the
get_features
function to use a Queue and produce multiple examples instead of a single one
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@e-budur This is the case for Sangheli's version of prediction. It always takes the first response as the best one, but when I give the potential_responses one by one to the estimator as it is in Denny's original prediction code, it works correctly but it is as slow as the prediction takes 4 minutes for 500 potential responses!!!
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@Samurais thanks for posting your branch! I had the same question, very helpful.
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@Sangheli In your prediction code (your udc_predict.py), it is true that you are sending all the potential responses to the estimator at once but the answer is always the first response in the potential responses simply because the estimator ignore other elements in the potential responses list. In fact, although you give the features extracted from whole potential responses into the estimator but based on my observations it always gives the first element in the potential_responses as an answer. However, if you put the estimator in the loop exactly like what it is in Denny's code, you will get the correct response but unfortunately, it takes very long time to get the response for 500 potential responses....
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Related Issues (20)
- any code for tensoflow > 2.0
- 这个代码是否可以用于中文状态下的封闭域问答? HOT 6
- How to solve AttributeError: module 'tensorflow.contrib.learn' has no attribute 'estimators' HOT 4
- Derive actual response from the probability? Just wondering how do I generate actual response from this model? HOT 1
- Gettin error while running idc_train.py HOT 3
- How to select candidate answers when predict HOT 1
- How to Deal with Context of multiple column ?
- How can I export/serve this model using saved_model_cli ?
- How to stops training after specied number of steps? HOT 1
- InvalidArgumentError (see above for traceback): indices[24,12] = 135816 is not in [0, 91620)
- InvalidArgumentError: Name: <unknown>, Feature: distractor_1 (data type: int64) is required but could not be found. [[{{node read_batch_features_eval/ParseExample/ParseExample}}]]
- InvalidArgumentError (see above for traceback): indices[7,16] = 99296 is not in [0, 91620)
- ValueError: Shapes (10, ?, 160) and () are incompatible
- Incompatible shapes: [20,1] vs. [80,1] HOT 2
- UnicodeDecodeError: 'gbk' codec can't decode byte 0xbf in position 2: illegal multibyte sequence HOT 1
- The question about Tensorflow about Incompatible shapes: [730,5] vs. [30,5]
- udc_test.py出错
- Data missing from drive
- any examples of chatbot conversation?
- InvalidArgumentError: Incompatible shapes: [128,14,14,16] vs. [8] [[{{node max_unpooling2d_4/max_unpooling2d_4/mul_4}}]] [[{{node Mean_1}}]] HOT 1
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