I have 20 classes. Trying to use examples to feature part.

`label_list = []

for i in range(20):
print(i)
label_list.append(str(i))
print(label_list)

MAX_SEQ_LENGTH = 128

train_features = bert.run_classifier.convert_examples_to_features(train_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)

Error is

🚀 Feature

• Create a GPU benchmarking section in Documentation (Wiki).
• Build and Include TF2 Optimizer with gradient accumulation.

optimizer=AccumulatedOptimizer(Adam(lr=2e-5, clipnorm=1.0), accumulate_steps=4)

Motivation

I experiment with transformers library on Tensorflow 2 for a week and what you offer to us the users seems really useful. It saves a ton of t

Balloon example explains perfect how train the Mask_RCNN creating a new class to the model that has been trained with the COCO dataset.

Suppose we need to add the balloon class to the 80 (I think) categories that already the COCO contains, how should I start ? Is it something

transcribe.py has odd directory-scanning behavior which isn't documented

If you point --src to a directory, you get the error:

E Path in --src not existing

Looking at the code logic, the script expects a JSON file with a .catalog file extension. This is (1) not documented, and (2) not a really useful logic. It would be much better to point the script to a dir, and scan f

Running the first few lines of https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py gives me an error in line 29:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-220cc54080f8> in <module>
      1 X = da.from_array(np.asarray(X), chunks=(1000, 10

"scipy.misc.imsave" was removed in scipy version 1.3
--> pip install scipy==1.2.1

https://docs.scipy.org/doc/scipy/reference/release.1.3.0.html#scipy-interpolate-changes

and functions from scipy.misc (bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow, toimage) have been removed.

I've ran into this issue for a couple hours and I ended up editing the dist library adding two new functions called fetchVideo and bufferToVideo that works pretty much like the fetchImage and bufferToImage functions.

I'll leave it here to help somebody else with the same issue and in case someone wants to include it on future releases.

face-api.js

...
exports.fetchVideo = fetc

Environment:

Framework: (TensorFlow, Keras)
Framework version:
tensorflow 1.14.0
tensorflow-estimator 1.14.0
tensorflow-serving-api 1.14.0
Keras 2.2.4
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.0
Horovod version:
horovod 0.18.1
MPI version:
(tensorflow_p36) ubuntu@ip-172-31-38-183:~$ mpirun --version
mpirun (Open MPI) 4.0.1
CUDA version:
CUDA Version 10.0.130

NCCL version

Every kubeflow image should be scanned for security vulnerabilities.
It would be great to have a periodic security report.
Each of these images with vulnerability should be patched and updated.

Support for storing large tensor values in external files was introduced in #678, but AFAICT is undocumented.

This is a pretty important feature, functionally, but it's also important for end users who may not realise that they need to move around more than just the *.onnx file.

I would suggest it should be documented in IR.md, and perhaps there are other locations from which it could be s