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we only address code/doc bugs, performance issues, feature requests and
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System information

• Have I written custom code (as opposed to using a stock
  example script provided in TensorFlow): Yes

In Keras documentation, glorot_uniform says that the initializer is using Glorot Uniform from this paper. However, the Keras implementation is totally different from the equation on the paper. Also, there are some arguments such as mode ='fan_avg' is the default. It should be same as the referenced paper. 'fan_sum'. Golort uniform is shown

Not sure when this happened but i love the new left-hand side navigation https://scikit-learn.org/dev/user_guide.html

(@adrinjalali did this maybe?)

However, when clicking the different entries, the result is inconsistent. For some, it shows a TOC that expands the existing toc with m

cc @ezyang @anjali411 @dylanbespalko

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb

trainable_variables = weights.values() + biases.values() doesn't work.
Also if I write trainable_variables = list(weights.values()) + list(biases.values()), I have to turn on tf.enable_eager_execution(), but the training result is wrong, accuracy is ar

Current Behavior:

The the wiki page APIExample, for the python example, the handle api is is run through the TessBaseAPIDelete funciton if the api failed to be initialized whereas for the C example below, this is not the case.

python:

rc = tesseract.TessBaseAPIInit3(api, TESSDATA_PREFIX, lang)
if (rc):
    te

• face_recognition version: latest docs
• Python version: N/A
• Operating System: N/A

Description

the docs say that default value for 'model' is "large". in fact, it is "small", which makes a different. please correct the docs

I am executing ML-examples ->cmsisnn-cifar10. While converting trained Caffe model into cmsis-nn by below command

python nn_quantizer.py --model models/cifar10_m7_train_test.prototxt --weights models/cifar10_m7_iter_300000.caffemodel.h5 --save models/cifar10_m7.pkl

getting an error message.

Traceback (most recent call last):
File "nn_quantizer.py", line 614, in
my_model.get_gra

So the three main functions in the stdlib: lu, cholesky, and qr all have docstrings but they are not listed in the documentation because of the missing docs/src/index.md file. Ref #35599

I'm not sure if XGBoost s model is well calibrated with softmax. It would be nice to have a doc with various experiments including random forest, dart etc.

Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

Issue Summary

Documentation and error messages are misleading when using a release version of Caffe on Ubuntu.

Executed Command (if any)

cmake .. -DBUILD_CAFFE=OFF -DCaffe_INCLUDE_DIRS=/usr/include/caffe -DCaffe_LIBS=/usr/lib/x86_64-linux-gnu/libcaffe.so

OpenPose Output (if any)

-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identificatio

What's the ETA for updating the massively outdated documentation?

Please update all documents that are related building CNTK from source with latest CUDA dependencies that are indicated in CNTK.Common.props and CNTK.Cpp.props.
I tried to build from source, but it's a futile effort.

In doc.pyx' s line 590:

 if not self.is_parsed:
            raise ValueError(Errors.E029)

I can still do a good job of chunking by tokenization and pos tagging only, without the full parse. Also in some languages parse isn't available. This will leave more flexibilities to users. I can comment out this in my copy of spacy, but when I update spacy to a new release, I have to chang