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System information

• TensorFlow version (you are using): 2.1
• Are you willing to contribute it (Yes/No): No

https://github.com/pytorch/pytorch/pull/34093/files

cc @ezyang @anjali411 @dylanbespalko

Considering the MNIST dataset, wich has 5923 instances of the 0 class in the training set, I'm alittle confused about the following code for detemining the relative errors of the SGD classification model:

row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums

(https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb // In: 67)

Since using `axi

What's the ETA for updating the massively outdated documentation?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

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

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

Is there an overview, how the config object for the constructors of the neural network are defined?

This would be interesting:

• Has brain.NeuralNetwork and brain.recurrent.RNN the same config attributes?
• What is the description of the inputSize attribute?

Thank you for the great work with this library, it is realy amazing!

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

Tutorial 19 worked fine with TensorFlow v. 1.4.0 and Keras v. 2.0.8-tf. But I have just upgraded to TensorFlow 1.9 and Keras 2.1.6-tf and now I get an error when trying to load the model with the best hyper-parameters. I think it is a bug in Keras because many other people have experienced variations of the same error-message, see e.g. tensorflow/tensorflow#20073

We wi

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

is it Grid Search can solve CASH problems with NNI , it seems that it is usually used for hyper-parameters optimization, have you guys have finished some revision for Grid Search for solving CASH problems.

about Cash problems can refer to ：microsoft/nni#1178

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)))