Comments (2)
More data, while I don't know your usecase, if you can generate more data to train the model it will almost always translate to better generalization.
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Closing for lack of response.
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Related Issues (20)
- How to compute accuracy both from train and test set HOT 2
- How to save model? HOT 1
- How to predict a picture HOT 1
- how many times your model iterates when you train?
- loss fall slow
- Convert code to use functional TensorFlow
- There is no run.py in the TextRecognitionDataGenerator HOT 1
- The Error rate is always 1.0 HOT 1
- fail to test
- Testing result is poor HOT 2
- CHAR_VECTOR is correct? HOT 1
- Unable to run `python3 run.py -ex ../data/test --test --restore` HOT 2
- Scaling input data between 0 and 1 HOT 2
- Default Model gives poor testing result. HOT 5
- Possible bug in using Batch normalisation HOT 1
- batch_dt = sparse_tuple_from(np.reshape(np.array(raw_batch_la), (-1)))应该去掉reshape HOT 1
- Why the output is the same when using the pretrained model for prediction?
- Problems with training Tibetan pictures HOT 3
- always training the first 100 batch
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