Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Pl

Is it a known issue (is it even an issue?) that model.test_on_batch returns the sum of losses of each entry in the batch instead of the average? I looked over the changelog and saw no reference to that.
model.train_on_batch does in fact returns the average, but in the docs their return value is documented the same.

line 123@
X, Y = read_images(DATASET_PATH, MODE, batch_size)
line 66@
classes = sorted(os.walk(dataset_path).next()[1])
StopIteration

Is there a way Tensorflow git cloned repositories can run without overhead issues?

Context

We would like to add torch::nn::functional::normalize to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.normalize.

Steps

• Add torch::nn::NormalizeOptions to torch/csrc/api/include/torch/nn/options/normalization.h (add this file if it doesn’t exist), which should include the following parameters (based on https://pytorch.

Target Leakage in mentioned steps in Data Preprocessing. Train/test split needs to be before missing value imputation. Else you will have a bias in test/eval/serve.

This should really help to keep a track of papers read so far. I would love to fork the repo and keep on checking the boxes in my local fork.

For example: Have a look at this section. People fork this repo and check the boxes as they finish reading each section.

"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easi

What's the ETA for updating the massively outdated documentation?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 got a conllU file, from my university, where the head column is filled with .
Processing such file with the cli.convert method will result in a int cast error in
https://github.com/explosion/spaCy/blob/master/spacy/cli/converters/conllu2json.py line 73
in the read_conllx method (head = (int(head) - 1) if head != "0" else id).

In the format documentation on https://universaldependencie

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