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System information

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

Similar to https://github.com/pytorch/pytorch/pull/34037/files we can view a complex tensor as a float tensor and pass it to uniform_ used by rand

cc @ezyang @anjali411 @dylanbespalko

What's the ETA for updating the massively outdated documentation?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

Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part

This op is used by the Music Transformer model.

It would be beneficial to write a design document on INT8 DNNL implementation and put it into
FluidDoc next to other Design documents:
https://github.com/PaddlePaddle/FluidDoc/tree/develop/doc/fluid/design/mkldnn

What is wrong?

On Windows, brain.js installs correctly and runs correctly only if the version of Node.Js is greater than 13.0. This is in no way mentioned in the README.md.

Where does it happen?

In the installation

How do we replicate the issue?

In a Windows machine with the LTS version of Node.Js installed try to install brain.Js with the command npm install brain.js

Problem description

Gensim LDAModel documentation incorrect

Steps/code/corpus to reproduce

Based on the code in log_perplexity, it looks like it should be e^(-bound) since all of the functions used in computing it seem to be using the natural logarithm/e

I've just started learning a bit about Tensorflow, and I tried out the Fashion MNIST tutorial. However, I keep getting a list index out of range error. I've copied the code from the official tutorial and also tried using tf.reset_default_graph() as suggested in some other posts, but neither have worked.

Here is the notebook with the error:
https://github.com/fsiraj/Tensorflow-Tutorials/blob/ma

我发现examples/retinaface.cpp中，如果开启OMP加速的话似乎在检测到人脸时会发生内存泄漏，但我定位不了这个问题的具体原因。

值得注意的时，如果将qsort_descent_inplace函数中的OMP指令注释掉这个问题就会消失掉。

static void qsort_descent_inplace(std::vector<FaceObject>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;
    ...
    // #pragma omp parallel sections
    {
        // #pragma

Several parts of the op sec like the main op description, attributes, input and output descriptions become part of the binary that consumes ONNX e.g. onnxruntime causing an increase in its size due to strings that take no part in the execution of the model or its verification.

Setting __ONNX_NO_DOC_STRINGS doesn't really help here since (1) it's not used in the SetDoc(string) overload (s

Inspired by: https://jigsaw.tighten.co/docs/starter-templates/

For hosting we can be use GitHub Pages 🚀

Your open source project is as good as its documentation
Arkadiusz Kondas (or maybe it's somewhere I heard)

This can help to #318

I understand that these two python files show two different methods to construct a model. The original n_epoch is 500 which works perfect for both python files. But if I change n_epoch to 20, only tutorial_mnist_mlp_static.py can achieve a high test accuracy (~0.97). The other file tutorial_mnist_mlp_static_2.py only get 0.47.

The models built from these two files looks the same for me (the s

Short summary about the issue/question:

Brief what process you are following:
When specifying a config.yml

# config.yml
tuner:
  builtinTunerName: Random
  classArgs:
    optimize_mode: m

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))