The red -
lines are produced by direct references to Tensor.dfoo
, which doesn't crash.
The green +
lines are produced by #adjoint(Tensor.foo)
, which does crashes.
The red -
lines are produced by direct references to Tensor.dfoo
, which doesn't crash.
The green +
lines are produced by #adjoint(Tensor.foo)
, which does crashes.
This proposal is a follow-on to [SE-0195 - Introduce User-defined "Dynamic Member
""" | |
Demostrating how to compute the gradients for convolution with: | |
tf.nn.conv2d | |
tf.nn.conv2d_backprop_input | |
tf.nn.conv2d_backprop_filter | |
tf.nn.conv2d_transpose | |
This is the scripts for this answer: https://stackoverflow.com/a/44350789/1255535 | |
""" |
const request = require('superagent'); | |
request | |
.get('http://api.purdue.io/odata/Subjects') | |
.query({ $select: 'Name,Abbreviation' }) | |
.then((res) => { | |
const subjects = res.body.value; | |
console.log(subjects); | |
}); |
echo 'export PATH=$HOME/local/bin:$PATH' >> ~/.bashrc | |
. ~/.bashrc | |
mkdir ~/local | |
mkdir ~/node-latest-install | |
cd ~/node-latest-install | |
curl http://nodejs.org/dist/node-latest.tar.gz | tar xz --strip-components=1 | |
./configure --prefix=~/local | |
make install # ok, fine, this step probably takes more than 30 seconds... | |
curl https://www.npmjs.org/install.sh | sh |
/** | |
* Convert From/To Binary/Decimal/Hexadecimal in JavaScript | |
* https://gist.github.com/faisalman | |
* | |
* Copyright 2012-2015, Faisalman <[email protected]> | |
* Licensed under The MIT License | |
* http://www.opensource.org/licenses/mit-license | |
*/ | |
(function(){ |
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs