Hi,
I want to use a different convolutional filter for each data in a minibatch, that's said:
if the input images have shape: [batch_size, height, width, channel], and I want to use different filters for each image in the minibatch, so the filter shape is: [batch_size, f_width, f_height, in_channel, out_channel]. So each image in the minibatch corresponds to different filters.

I try to solv

Small thing, but costed me several hours to find :)
In the documentation example of Siamese mnist .

We see a code for contrastive loss, based on a paper. But the labels in this function are reversed from
the paper. Meaning in the paper Y=0 if X1,X2 are from same domain, Y=1 other

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?

Current implementation does sequential sigmoid_out and mul_. We can get better performance by fusing this operations together.

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.