tf.function makes invalid assumptions about arguments that are Mapping instances. In general, there are no requirements for Mapping instances to have constructors that accept [(key, value)] initializers, as assumed here.

This leads to cryptic exceptions when used with perfectly valid Mapping s

As indicated in the FAQ, the way to import ResNeXt101 is from keras.applications.resnext import ResNeXt101. But in the latest Keras 2.3.1 and Keras-Applications 1.0.8, it gives ImportError: No module named 'keras.applications.resnext'. I try to change it to `from keras_applications.resnext import preproces

Basically, this is a GSoC project which has not been applied this year:

We want to see some kind of higher level API which can manage DL networks. There are several criterias for now:

• Hide asynchronous inference management internally. User just choose the number of requests (what about heterogenous scenario? In example, use both GPU and VPU with specific number of requestst for each other

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

Currently, PyTorch C++ API is missing many torch::nn layers that are available in the Python API. As part of the Python/C++ API parity work, we would like to add the following torch::nn modules and utilities in C++ API:

Containers

• Module (TODO: some APIs are missing in C++, e.g. register_forward_hook / register_forward_pre_hook)
• Sequential (@ShahriarSS)
• Modul

Mish is a new novel activation function proposed in this paper.
It has shown promising results so far and has been adopted in several packages including:

• TensorFlow-Addons
• SpaCy (Tok2Vec Layer)
• [Thinc - SpaCy's official NLP based ML

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.

Original line 87:
with open('README.md') as readme:

Corrected version of line 87:
with open('README.md','r',encoding='utf-8') as readme:

Explanation:
Windows uses GBK to decode rather than utf-8 at default setting

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.

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

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 am having difficulty in running this package as a Webservice. Would appreciate if we could provide any kind of documentation on implementing an API to get the keypoints from an image. Our aim is to able to deploy this API as an Azure Function and also know if it is feasible.

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,

With the latest version of scipy.misc, scipy.misc.toimage is no longer available. To load and save an image as png we now have to use PIL, breaking tensorboard image summary.

Here is how I fixed the bug:
1./ At the end of main.py, log a uint8 image
logger.image_summary(tag, (images * 255).astype(np.uint8), step+1)
2./ In Logger class, package image as bytes with the PIL library (mode="L

From here:

A particularity of the SV2TTS framework is that all models can be trained
separately and on distinct datasets. For the encoder, one seeks to have a model
that is robust to noise and able to capture the many characteristics of the human
voice. Therefore, a large corpus of many different speakers wou

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