kevinrobinson

// Before, with Sprockets:
//= require style
//= require tablesort
//= require tooltipster
//= require jquery-ui
// ...

@import 'variables';
@import 'overall';
@import 'type';

kevinrobinson

    loaders: [
      { test: /\.css$/, loader: "style-loader!css-loader" },
      { test: /\.scss$/, loaders: ["style", "css", "sass"] }
    ]

kevinrobinson

    loaders: [
      ...
      { test: /\.png$/, loader: 'url-loader?limit=100000' },
      { test: /\.jpg$/, loader: 'file-loader' },
      { test: /\.svg$/, loader: 'file-loader' }
    ]
  }

kevinrobinson

{
  ...
  output: {
    path: outputPath,
    publicPath: "https://cnd.com/production/js/", // hostname needs to match Rails config
    filename: '[name]-[chunkhash].js'
  },
  plugins: [
    new AssetsPlugin({ path: outputPath }),
    new webpack.optimize.UglifyJsPlugin(),

kevinrobinson

"""Python wrappers around Brain.

This file is MACHINE GENERATED! Do not edit.
"""

from google.protobuf import text_format

from tensorflow.core.framework import op_def_pb2
from tensorflow.python.framework import op_def_registry
from tensorflow.python.framework import ops

kevinrobinson

# example input:
sentence = 'The quick brown fox jumped over the lazy dog.'

# tokenize and normalize words, building the set of all vocabulary ever seen
words_set = {}
def tokenize(sentence):
  return map(str.lower, sentence[0:-1].split(' '))
for word in tokenize(sentence):
  words_set[word] = True

kevinrobinson

(words, counts, words_per_epoch, self._epoch, self._words, examples, labels) = word2vec.skipgram(filename=opts.train_data,
  batch_size=opts.batch_size,
  window_size=opts.window_size,
  min_count=opts.min_count,
  subsample=opts.subsample)

kevinrobinson

REGISTER_OP(“Skipgram”)
 .Output(“vocab_word: string”)
 .Output(“vocab_freq: int32”)
 .Output(“words_per_epoch: int64”)
 .Output(“current_epoch: int32”)
 .Output(“total_words_processed: int64”)
 .Output(“examples: int32”)
 .Output(“labels: int32”)
 .Attr(“filename: string”)
 .Attr(“batch_size: int”)

kevinrobinson

lr = opts.learning_rate * tf.maximum(0.0001, 1.0 — tf.cast(self._words, tf.float32) / words_to_train)

kevinrobinson

node {
  name: "Cast_1"
  op: "Cast"
  input: "Skipgram:4"
  attr {
    key: "DstT"
    value {
      type: DT_FLOAT
    }
  }