sorenlouv

import React, { Component } from 'react';

export default function withPropsChecker(WrappedComponent) {
  return class PropsChecker extends Component {
    componentWillReceiveProps(nextProps) {
      Object.keys(nextProps)
        .filter(key => {
          return nextProps[key] !== this.props[key];
        })
        .map(key => {

ilblackdragon

import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

aparrish

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

jarednorman

Webpack with Rails Part 2: Precompile Harder

Note: This article is more generally about how to add dependencies to a Rake task, and why you might want to do that.

If you followed my previous article on webpack and Rails, you might have built yourself a trendy little React app. Then you tried to deploy it, and that didn't work, did it?

smly

"""
An example to check the AUC score on a validation set for each 10 epochs.
I hope it will be helpful for optimizing number of epochs.
"""
# -*- coding: utf-8 -*-
import logging

from sklearn.metrics import roc_auc_score
from keras.callbacks import Callback

jcoreyes

##Information

name: LSTM image captioning model based on CVPR 2015 paper "Show and tell: A neural image caption generator" and code from Karpathy's NeuralTalk.

model_file: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/image_caption_flickr8k.py

model_weights: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/image_caption_flickr8k.p

neon_version: v1.0.rc1

mbohun

import sys
import requests
from lxml import etree

if __name__=="__main__":
    smiles = sys.argv[1]

    html_doc = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" + smiles + "/record/XML")
    root = etree.XML(html_doc.text)

mcs07

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Download all UV-Vis spectra available from NIST Chemistry Webbook."""
import os
import re

import requests
from bs4 import BeautifulSoup