bvaughn

This API was removed in React 17

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Interaction tracing with React

React recently introduced an experimental profiler API. After discussing this API with several teams at Facebook, one common piece of feedback was that the performance information would be more useful if it could be associated with the events that caused the application to render (e.g. button click, XHR response). Tracing these events (or "interactions") would enable more powerful tooling to be built around the timing information, capable of answering questions like "What caused this really slow commit?" or "How long does it typically take for this interaction to update the DOM?".

With version 16.4.3, React added experimental support for this tracing by way of a new NPM package, scheduler. However the public API for this package is not yet finalized and will likely change with upcoming minor releases, so it should be used with caution.

venik

#!/usr/bin/env bash

# Author: Sasha Nikiforov

# source of inspiration
# https://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions

# Detect platform
if [ "$(uname)" == "Darwin" ]; then
	# MacOS

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

dannguyen

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

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

balajikvijayan

from gensim import models

sentence = models.doc2vec.LabeledSentence(
    words=[u'so`bme', u'words', u'here'], tags=["SENT_0"])
sentence1 = models.doc2vec.LabeledSentence(
    words=[u'here', u'we', u'go'], tags=["SENT_1"])

sentences = [sentence, sentence1]

class LabeledLineSentence(object):

erikbern

# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

elad

Start a g2.2xlarge or better (GPU instance) with https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-ffba7b94

Login, username is ubuntu

Update a bunch of stuff and make sure cudnn R2 is used:

luarocks install image
luarocks install loadcaffe
luarocks install torch
export LD_LIBRARY_PATH=/home/ubuntu/torch-distro/install/lib:/home/ubuntu/torch-distro/install/lib:/home/ubuntu/cudnn-6.5-linux-x64-v2-rc2

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

cbeyls

package android.support.v4.preference;

import java.lang.reflect.Constructor;
import java.lang.reflect.Method;

import android.annotation.SuppressLint;
import android.app.Activity;
import android.content.Context;
import android.content.Intent;
import android.os.Build;