Greetings,

While the document about masking is super good, I found it misses an important point: how the mask associated with the previous mask in compute_mask(input, previous_mask)

Specifically, let us assume we have two inputs A and B. I wrote a custom Add layers:

class CustomAddingWithMasking(tf.keras.layers.Layer):
  

In the given documentation, the mentioned key are acc and val_acc, but actually it is accuracy and val_accuracy.

Given documentation screenshot:

Whereas the actual keys are `dict_keys(['val_loss', 'val_accuracy

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.

Short description

I am trying to train Tesseract on Akkadian language. The language-specific.sh script was modified accordingly. When converting the training text to TIFF images, the text2image program crashes.

Environment

• Tesseract Version: 3.04.01
• Commit Number: the standard package in Ubuntu, package version 3.04.01-4, commit unknown
• Platform: Linux ubuntu

• face_recognition version: 1.2.3
• Python version: 3.7
• Operating System: Pop!_OS 19.04
• CUDA + CuDNN version: 10.0

I'm trying to use the facerec_from_webcam_multiprocessing.py example with cuda support, but everytime I run it I get this error:

Width: 640, Height: 480, FPS: 30
Process Process-5:
Traceback (most recent call last):
  File "/usr/lib/python3.7/multiprocessing/proce

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.