ssh://sci@192.168.67.128:22/usr/bin/python3 -u /home/win_pymine_clean/feature_wifi/word2vec_basic.py

Found and verified text8.zip

Data size 17005207

Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]

Sample data [5238, 3084, 12, 6, 195, 2, 3136, 46, 59, 156] ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']

3084 originated -> 5238 anarchism

3084 originated -> 12 as

12 as -> 6 a

12 as -> 3084 originated

6 a -> 195 term

6 a -> 12 as

195 term -> 2 of

195 term -> 6 a

2017-09-18 01:35:09.854800: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.

2017-09-18 01:35:09.874305: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

2017-09-18 01:35:09.874359: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.

2017-09-18 01:35:09.874379: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.

2017-09-18 01:35:09.874396: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

Initialized

Average loss at step  0 :  261.962097168

Nearest to but: rtp, sheng, modules, defenders, banknote, contrasting, proximal, va,

Nearest to state: pigeon, neutralize, gacy, independent, lug, rebellious, scholar, infestation,

Nearest to five: analytics, bhp, exams, synapomorphies, paolo, nanotubes, leaching, lombards,

Nearest to their: fragment, allocations, filthy, forgets, leaks, myron, albatross, response,

Nearest to as: bodybuilders, expecting, indivisible, rembrandt, clades, redirection, summand, checkmate,

Nearest to other: editorship, aan, beauties, exterminated, guan, nolo, bess, pearce,

Nearest to than: explainable, sword, capacitor, savory, rally, coleridge, imr, subordinates,

Nearest to are: ignatius, repetitive, eras, soter, elan, collectivity, fractions, perugia,

Nearest to its: castillo, hogshead, rna, ljubljana, fiberglass, ausgleich, toxin, balderus,

Nearest to see: abrasive, rifles, und, sufferer, nvidia, caricatured, manly, chee,

Nearest to and: meiosis, bridges, insurrection, feistel, sacrum, sepsis, reinforced, oscillator,

Nearest to three: honest, communally, gast, hawkins, highway, menzies, flocked, nijmegen,

Nearest to it: peh, namespaces, revolting, murat, mcgrath, toltec, fiorello, aediles,

Nearest to war: buprenorphine, rakis, decide, funky, stilicho, rnir, satirized, hydrolysis,

Nearest to however: pyramid, whittier, edifices, heraclea, alveolar, counties, carousel, carnap,

Nearest to states: renormalization, ideology, defending, immerse, semi, fireplace, roach, classically,

Average loss at step  2000 :  113.145377743

Average loss at step  4000 :  53.0118778615

Average loss at step  6000 :  33.3697857151

Average loss at step  8000 :  23.4925061049

Average loss at step  10000 :  17.8379190623

Nearest to but: and, soroban, var, electromagnetism, suitable, yeast, ruins, connotation,

Nearest to state: feel, independent, refrigerator, scholar, inventor, once, archive, disambiguation,

Nearest to five: zero, bckgr, nine, coke, eight, one, seven, hakama,

Nearest to their: response, wiesenthal, the, fragment, a, naturally, dawn, prevent,

Nearest to as: by, namibia, is, in, encourage, and, heroes, nazi,

Nearest to other: clock, concluded, bang, house, western, existent, obey, strategic,

Nearest to than: sword, rally, equilibrium, first, for, dhabi, contained, sample,

Nearest to are: is, under, certain, were, fractions, was, wickets, ignatius,

Nearest to its: faber, the, explanations, aristophanes, rna, ed, apo, one,

Nearest to see: abbot, entire, yeast, kohanim, rifles, solstice, multi, jurisdiction,

Nearest to and: in, of, with, or, nine, the, UNK, by,

Nearest to three: zero, jpg, nine, one, two, six, seven, five,

Nearest to it: archie, ideas, he, decided, viet, sigma, a, this,

Nearest to war: decide, density, from, patent, supposedly, passenger, agave, evil,

Nearest to however: pyramid, and, atheists, cuisine, counties, neq, spotted, technique,

Nearest to states: agave, semi, ideology, what, nine, defending, defendants, classically,

Average loss at step  12000 :  14.0006338406

Average loss at step  14000 :  11.8489933434

Average loss at step  16000 :  9.97434587622

Average loss at step  18000 :  8.41560820138

Average loss at step  20000 :  7.94222211516

Nearest to but: and, circ, electromagnetism, northrop, suitable, soroban, is, which,

Nearest to state: subkey, refrigerator, feel, inventor, once, rebellious, first, independent,

Nearest to five: nine, eight, zero, seven, six, three, four, agouti,

Nearest to their: his, the, acacia, a, wiesenthal, fragment, response, its,

Nearest to as: and, in, for, by, is, agouti, from, harbour,

Nearest to other: concluded, clock, existent, operatorname, western, rai, obey, bang,

Nearest to than: for, sword, rally, archimedean, antwerp, equilibrium, or, nine,

Nearest to are: is, were, was, agouti, have, ignatius, gollancz, under,

Nearest to its: the, his, operatorname, rna, magdeburg, their, faber, requisite,

Nearest to see: kohanim, seabirds, agouti, abbot, entire, yeast, rifles, subkey,

Nearest to and: or, circ, dasyprocta, operatorname, in, agouti, of, for,

Nearest to three: six, two, nine, seven, five, eight, one, zero,

Nearest to it: he, this, she, there, archie, which, viet, dasyprocta,

Nearest to war: decide, density, passenger, patent, operatorname, stilicho, dasyprocta, ns,

Nearest to however: and, cuisine, educators, pyramid, edifices, credo, atheists, operatorname,

Nearest to states: agave, agouti, semi, dasyprocta, ideology, hadith, what, defending,

Average loss at step  22000 :  7.19506471896

Average loss at step  24000 :  6.85889063323

Average loss at step  26000 :  6.72703241718

Average loss at step  28000 :  6.40524325681

Average loss at step  30000 :  5.92727108788

Nearest to but: and, circ, which, suitable, electromagnetism, although, operatorname, agouti,

Nearest to state: subkey, feel, refrigerator, inventor, diodes, rebellious, first, once,

Nearest to five: eight, six, seven, four, three, zero, nine, two,

Nearest to their: his, the, its, acacia, a, wiesenthal, fragment, punts,

Nearest to as: by, harbour, and, agouti, in, circ, is, for,

Nearest to other: clock, concluded, western, rai, different, operatorname, traits, existent,

Nearest to than: or, for, rally, sword, and, ann, archimedean, antwerp,

Nearest to are: were, is, was, have, agouti, be, under, gollancz,

Nearest to its: the, their, his, operatorname, magdeburg, requisite, rna, tonnage,

Nearest to see: kohanim, seabirds, trinomial, agouti, aba, abbot, yeast, subkey,

Nearest to and: or, circ, dasyprocta, agouti, operatorname, with, in, of,

Nearest to three: six, five, two, four, seven, eight, nine, agouti,

Nearest to it: he, this, she, there, which, amalthea, archie, dasyprocta,

Nearest to war: decide, buprenorphine, density, ns, patent, passenger, aforementioned, northrop,

Nearest to however: and, almighty, educators, cuisine, technique, edifices, expressly, operatorname,

Nearest to states: agave, semi, agouti, defending, ideology, dasyprocta, hadith, what,

Average loss at step  32000 :  5.91289714551

Average loss at step  34000 :  5.74882777894

Average loss at step  36000 :  5.79514622164

Average loss at step  38000 :  5.50678780043

Average loss at step  40000 :  5.23573713326

Nearest to but: and, circ, although, which, or, zero, that, electromagnetism,

Nearest to state: subkey, inventor, feel, refrigerator, diodes, rebellious, independent, first,

Nearest to five: four, three, six, eight, seven, zero, two, nine,

Nearest to their: his, its, the, acacia, operatorname, wiesenthal, agouti, punts,

Nearest to as: agouti, harbour, circ, zero, and, in, by, aba,

Nearest to other: different, clock, western, operatorname, concluded, fountain, dasyprocta, rai,

Nearest to than: or, for, rally, and, sword, simple, hbf, qualification,

Nearest to are: were, is, have, agouti, zero, be, was, multivibrator,

Nearest to its: their, the, his, operatorname, magdeburg, dasyprocta, agouti, zero,

Nearest to see: kohanim, trinomial, seabirds, agouti, aba, rifles, abrasive, abbot,

Nearest to and: or, zero, circ, dasyprocta, operatorname, in, agouti, but,

Nearest to three: four, five, six, seven, eight, two, zero, agouti,

Nearest to it: he, she, this, which, there, zero, archie, sal,

Nearest to war: decide, buprenorphine, northrop, density, ordinarily, aforementioned, slovenes, collide,

Nearest to however: and, but, educators, almighty, technique, that, operatorname, cuisine,

Nearest to states: semi, agouti, agave, defending, ideology, dasyprocta, hadith, what,

Average loss at step  42000 :  5.36539960575

Average loss at step  44000 :  5.24343806195

Average loss at step  46000 :  5.22763335347

Average loss at step  48000 :  5.23191133296

Average loss at step  50000 :  4.98666904318

Nearest to but: and, although, circ, however, or, four, which, two,

Nearest to state: subkey, inventor, feel, refrigerator, rebellious, leche, diodes, independent,

Nearest to five: six, four, three, eight, seven, dasyprocta, agouti, two,

Nearest to their: his, its, the, operatorname, punts, acacia, agouti, biostatistics,

Nearest to as: harbour, circ, agouti, kapoor, six, is, by, aba,

Nearest to other: different, four, many, clock, some, western, three, operatorname,

Nearest to than: or, and, for, rally, sword, simple, but, operatorname,

Nearest to are: were, is, have, be, was, agouti, gad, multivibrator,

Nearest to its: their, the, his, operatorname, dasyprocta, magdeburg, agouti, her,

Nearest to see: kohanim, trinomial, seabirds, aba, originator, erythrocytes, rifles, agouti,

Nearest to and: or, dasyprocta, circ, but, operatorname, six, agouti, four,

Nearest to three: four, six, five, seven, eight, two, one, dasyprocta,

Nearest to it: he, this, she, there, which, amalthea, archie, dasyprocta,

Nearest to war: decide, buprenorphine, ordinarily, northrop, aforementioned, density, slovenes, scenario,

Nearest to however: but, and, almighty, operatorname, technique, educators, that, four,

Nearest to states: defending, agouti, semi, agave, ideology, dasyprocta, hadith, classically,

Average loss at step  52000 :  5.02374834335

Average loss at step  54000 :  5.19878741825

Average loss at step  56000 :  5.04657789743

Average loss at step  58000 :  5.05832611966

Average loss at step  60000 :  4.95238713527

Nearest to but: and, however, or, although, circ, which, operatorname, kapoor,

Nearest to state: subkey, inventor, refrigerator, feel, pulau, ursinus, leche, michelob,

Nearest to five: six, four, three, eight, seven, two, dasyprocta, zero,

Nearest to their: his, its, the, her, operatorname, agouti, punts, a,

Nearest to as: in, circ, agouti, kapoor, by, harbour, is, aba,

Nearest to other: different, many, some, three, operatorname, clock, four, western,

Nearest to than: or, and, for, but, simple, rally, qualification, sword,

Nearest to are: were, is, have, be, gad, agouti, was, fractions,

Nearest to its: their, his, the, her, operatorname, magdeburg, biostatistics, agouti,

Nearest to see: but, kohanim, trinomial, seabirds, erythrocytes, originator, aba, solstice,

Nearest to and: or, circ, operatorname, dasyprocta, agouti, including, but, pulau,

Nearest to three: six, four, five, two, seven, eight, dasyprocta, agouti,

Nearest to it: he, this, she, there, which, they, amalthea, archie,

Nearest to war: buprenorphine, aforementioned, ordinarily, decide, rivaling, northrop, density, ursus,

Nearest to however: but, and, that, almighty, operatorname, technique, when, which,

Nearest to states: agouti, agave, defending, dasyprocta, ideology, semi, hadith, kingdom,

Average loss at step  62000 :  5.00446435535

Average loss at step  64000 :  4.85484569466

Average loss at step  66000 :  4.64125810647

Average loss at step  68000 :  5.0052472105

Average loss at step  70000 :  4.88670423937

Nearest to but: and, however, although, or, callithrix, circ, mico, which,

Nearest to state: subkey, inventor, ursinus, refrigerator, pulau, feel, capuchin, leche,

Nearest to five: four, six, eight, three, seven, zero, nine, two,

Nearest to their: its, his, the, her, some, operatorname, agouti, michelob,

Nearest to as: agouti, circ, kapoor, harbour, aba, operatorname, thaler, in,

Nearest to other: different, many, some, three, profits, operatorname, several, fountain,

Nearest to than: or, but, and, simple, qualification, rally, operatorname, wadsworth,

Nearest to are: were, have, is, be, gad, agouti, almighty, was,

Nearest to its: their, his, the, her, operatorname, agouti, ajanta, biostatistics,

Nearest to see: kohanim, but, trinomial, methodist, entire, originator, clo, aba,

Nearest to and: or, circ, callithrix, but, dasyprocta, operatorname, thaler, agouti,

Nearest to three: six, five, four, seven, eight, two, dasyprocta, one,

Nearest to it: he, she, this, there, which, they, amalthea, abakan,

Nearest to war: callithrix, ordinarily, buprenorphine, aforementioned, rivaling, decide, northrop, kapoor,

Nearest to however: but, and, that, which, almighty, although, when, though,

Nearest to states: thaler, kingdom, agouti, agave, defending, semi, ideology, dasyprocta,

Average loss at step  72000 :  4.7651747334

Average loss at step  74000 :  4.80770084751

Average loss at step  76000 :  4.72523927844

Average loss at step  78000 :  4.80518674481

Average loss at step  80000 :  4.79347246975

Nearest to but: however, and, although, callithrix, mico, circ, or, which,

Nearest to state: subkey, feel, pulau, inventor, capuchin, hadith, refrigerator, ursinus,

Nearest to five: four, six, seven, eight, three, two, nine, zero,

Nearest to their: its, his, the, her, our, agouti, operatorname, michelob,

Nearest to as: agouti, circ, kapoor, microcebus, thaler, callithrix, harbour, astoria,

Nearest to other: many, different, some, three, profits, operatorname, western, clock,

Nearest to than: or, but, and, kosar, simple, qualification, operatorname, wadsworth,

Nearest to are: were, is, have, gad, be, agouti, almighty, was,

Nearest to its: their, his, the, her, biostatistics, operatorname, agouti, dasyprocta,

Nearest to see: but, kohanim, trinomial, originator, aba, clo, milestones, yankees,

Nearest to and: or, callithrix, but, operatorname, circ, dasyprocta, thaler, agouti,

Nearest to three: five, four, six, two, seven, eight, dasyprocta, agouti,

Nearest to it: he, this, there, she, which, they, amalthea, abakan,

Nearest to war: ordinarily, callithrix, buprenorphine, aforementioned, rivaling, northrop, decide, lossy,

Nearest to however: but, that, although, when, though, almighty, operatorname, and,

Nearest to states: kingdom, thaler, agouti, defending, agave, dasyprocta, ideology, semi,

Average loss at step  82000 :  4.76438662171

Average loss at step  84000 :  4.76674228442

Average loss at step  86000 :  4.76899926138

9joml ;;'.Average loss at step  90000 :  4.7310421375

Nearest to but: and, however, or, which, callithrix, although, while, circ,

Ne

==

ij

arest to state: subkey, pulau, capuchin, ursinus, michelob, hadith, feel, refrigerator,

Nearest to five: four, seven, eight, six, three, zero, nine, two,

Nearest to their: its, his, the, her, our, them, agouti, some,

Nearest to as: agouti, by, in, microcebus, harbour, circ, callithrix, aba,

Nearest to other: different, many, some, including, several, profits, operatorname, three,

Nearest to than: or, but, and, kosar, simple, qualification, archaeopteryx, wadsworth,

Nearest to are: were, have, is, be, include, gad, agouti, almighty,

Nearest to its: their, his, the, her, biostatistics, agouti, dasyprocta, operatorname,

Nearest to see: but, kohanim, originator, trinomial, aba, bragi, yankees, clo,

Nearest to and: or, but, callithrix, operatorname, circ, including, however, dasyprocta,

Nearest to three: four, five, two, six, seven, eight, one, dasyprocta,

Nearest to it: he, this, she, there, which, they, amalthea, abakan,

Nearest to war: callithrix, ordinarily, globemaster, aforementioned, buprenorphine, northrop, rivaling, kapoor,

Nearest to however: but, that, and, although, which, calypso, though, operatorname,

Nearest to states: kingdom, thaler, defending, agave, agouti, ashes, state, dasyprocta,

Average loss at step  92000 :  4.66148071206

Average loss at step  94000 :  4.72550550318

Average loss at step  96000 :  4.68572968149

Average loss at step  98000 :  4.59241366351

Average loss at step  100000 :  4.68939590132

Nearest to but: however, and, although, or, while, which, callithrix, circ,

Nearest to state: subkey, pulau, capuchin, ursinus, michelob, leche, inventor, hadith,

Nearest to five: four, seven, three, six, eight, two, zero, nine,

Nearest to their: its, his, the, her, our, them, some, agouti,

Nearest to as: harbour, agouti, circ, aba, operatorname, kapoor, by, goo,

Nearest to other: different, many, some, including, operatorname, profits, several, these,

Nearest to than: or, and, but, kosar, simple, much, archaeopteryx, qualification,

Nearest to are: were, have, is, be, include, these, gad, agouti,

Nearest to its: their, his, the, her, biostatistics, agouti, dasyprocta, apo,

Nearest to see: kohanim, aba, batted, bragi, but, trinomial, originator, milestones,

Nearest to and: or, but, callithrix, circ, dasyprocta, operatorname, agouti, including,

Nearest to three: five, four, six, two, seven, eight, dasyprocta, agouti,

Nearest to it: he, this, there, she, they, which, abakan, amalthea,

Nearest to war: callithrix, globemaster, ordinarily, aforementioned, northrop, buprenorphine, kapoor, rivaling,

Nearest to however: but, although, that, though, which, and, while, calypso,

Nearest to states: kingdom, thaler, agouti, agave, defending, dasyprocta, state, ideology,

Please install sklearn, matplotlib, and scipy to show embeddings.

Process finished with exit code 0

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Basic word2vec example."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections

import math

import os

import random

import zipfile

import numpy as np

from six.moves import urllib

from six.moves import xrange  # pylint: disable=redefined-builtin

import tensorflow as tf

# Step 1: Download the data.

url = 'http://mattmahoney.net/dc/'

def maybe_download(filename, expected_bytes):

  """Download a file if not present, and make sure it's the right size."""

  if not os.path.exists(filename):

    filename, _ = urllib.request.urlretrieve(url + filename, filename)

  statinfo = os.stat(filename)

  if statinfo.st_size == expected_bytes:

    print('Found and verified', filename)

  else:

    print(statinfo.st_size)

    raise Exception(

        'Failed to verify ' + filename + '. Can you get to it with a browser?')

  return filename

filename = maybe_download('text8.zip', 31344016)

# Read the data into a list of strings.

def read_data(filename):

  """Extract the first file enclosed in a zip file as a list of words."""

  with zipfile.ZipFile(filename) as f:

    data = tf.compat.as_str(f.read(f.namelist()[0])).split()

  return data

vocabulary = read_data(filename)

print('Data size', len(vocabulary))

# Step 2: Build the dictionary and replace rare words with UNK token.

vocabulary_size = 50000

def build_dataset(words, n_words):

  """Process raw inputs into a dataset."""

  count = [['UNK', -1]]

  count.extend(collections.Counter(words).most_common(n_words - 1))

  dictionary = dict()

  for word, _ in count:

    dictionary[word] = len(dictionary)

  data = list()

  unk_count = 0

  for word in words:

    if word in dictionary:

      index = dictionary[word]

    else:

      index = 0  # dictionary['UNK']

      unk_count += 1

    data.append(index)

  count[0][1] = unk_count

  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))

  return data, count, dictionary, reversed_dictionary

data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,

                                                            vocabulary_size)

del vocabulary  # Hint to reduce memory.

print('Most common words (+UNK)', count[:5])

print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0

# Step 3: Function to generate a training batch for the skip-gram model.

def generate_batch(batch_size, num_skips, skip_window):

  global data_index

  assert batch_size % num_skips == 0

  assert num_skips <= 2 * skip_window

  batch = np.ndarray(shape=(batch_size), dtype=np.int32)

  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)

  span = 2 * skip_window + 1  # [ skip_window target skip_window ]

  buffer = collections.deque(maxlen=span)

  if data_index + span > len(data):

    data_index = 0

  buffer.extend(data[data_index:data_index + span])

  data_index += span

  for i in range(batch_size // num_skips):

    target = skip_window  # target label at the center of the buffer

    targets_to_avoid = [skip_window]

    for j in range(num_skips):

      while target in targets_to_avoid:

        target = random.randint(0, span - 1)

      targets_to_avoid.append(target)

      batch[i * num_skips + j] = buffer[skip_window]

      labels[i * num_skips + j, 0] = buffer[target]

    if data_index == len(data):

      buffer[:] = data[:span]

      data_index = span

    else:

      buffer.append(data[data_index])

      data_index += 1

  # Backtrack a little bit to avoid skipping words in the end of a batch

  data_index = (data_index + len(data) - span) % len(data)

  return batch, labels

batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)

for i in range(8):

  print(batch[i], reverse_dictionary[batch[i]],

        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.

batch_size = 128

embedding_size = 128  # Dimension of the embedding vector.

skip_window = 1       # How many words to consider left and right.

num_skips = 2         # How many times to reuse an input to generate a label.

# We pick a random validation set to sample nearest neighbors. Here we limit the

# validation samples to the words that have a low numeric ID, which by

# construction are also the most frequent.

valid_size = 16     # Random set of words to evaluate similarity on.

valid_window = 100  # Only pick dev samples in the head of the distribution.

valid_examples = np.random.choice(valid_window, valid_size, replace=False)

num_sampled = 64    # Number of negative examples to sample.

graph = tf.Graph()

with graph.as_default():

  # Input data.

  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])

  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])

  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

  # Ops and variables pinned to the CPU because of missing GPU implementation

  with tf.device('/cpu:0'):

    # Look up embeddings for inputs.

    embeddings = tf.Variable(

        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))

    embed = tf.nn.embedding_lookup(embeddings, train_inputs)

    # Construct the variables for the NCE loss

    nce_weights = tf.Variable(

        tf.truncated_normal([vocabulary_size, embedding_size],

                            stddev=1.0 / math.sqrt(embedding_size)))

    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

  # Compute the average NCE loss for the batch.

  # tf.nce_loss automatically draws a new sample of the negative labels each

  # time we evaluate the loss.

  loss = tf.reduce_mean(

      tf.nn.nce_loss(weights=nce_weights,

                     biases=nce_biases,

                     labels=train_labels,

                     inputs=embed,

                     num_sampled=num_sampled,

                     num_classes=vocabulary_size))

  # Construct the SGD optimizer using a learning rate of 1.0.

  optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

  # Compute the cosine similarity between minibatch examples and all embeddings.

  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))

  normalized_embeddings = embeddings / norm

  valid_embeddings = tf.nn.embedding_lookup(

      normalized_embeddings, valid_dataset)

  similarity = tf.matmul(

      valid_embeddings, normalized_embeddings, transpose_b=True)

  # Add variable initializer.

  init = tf.global_variables_initializer()

# Step 5: Begin training.

num_steps = 100001

with tf.Session(graph=graph) as session:

  # We must initialize all variables before we use them.

  init.run()

  print('Initialized')

  average_loss = 0

  for step in xrange(num_steps):

    batch_inputs, batch_labels = generate_batch(

        batch_size, num_skips, skip_window)

    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

    # We perform one update step by evaluating the optimizer op (including it

    # in the list of returned values for session.run()

    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)

    average_loss += loss_val

    if step % 2000 == 0:

      if step > 0:

        average_loss /= 2000

      # The average loss is an estimate of the loss over the last 2000 batches.

      print('Average loss at step ', step, ': ', average_loss)

      average_loss = 0

    # Note that this is expensive (~20% slowdown if computed every 500 steps)

    if step % 10000 == 0:

      sim = similarity.eval()

      for i in xrange(valid_size):

        valid_word = reverse_dictionary[valid_examples[i]]

        top_k = 8  # number of nearest neighbors

        nearest = (-sim[i, :]).argsort()[1:top_k + 1]

        log_str = 'Nearest to %s:' % valid_word

        for k in xrange(top_k):

          close_word = reverse_dictionary[nearest[k]]

          log_str = '%s %s,' % (log_str, close_word)

        print(log_str)

  final_embeddings = normalized_embeddings.eval()

# Step 6: Visualize the embeddings.

def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):

  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'

  plt.figure(figsize=(18, 18))  # in inches

  for i, label in enumerate(labels):

    x, y = low_dim_embs[i, :]

    plt.scatter(x, y)

    plt.annotate(label,

                 xy=(x, y),

                 xytext=(5, 2),

                 textcoords='offset points',

                 ha='right',

                 va='bottom')

  plt.savefig(filename)

try:

  # pylint: disable=g-import-not-at-top

  from sklearn.manifold import TSNE

  import matplotlib.pyplot as plt

  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')

  plot_only = 500

  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])

  labels = [reverse_dictionary[i] for i in xrange(plot_only)]

  plot_with_labels(low_dim_embs, labels)

except ImportError:

  print('Please install sklearn, matplotlib, and scipy to show embeddings.')