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Commit 428a8614 authored by Martin Karlsson's avatar Martin Karlsson
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hw11

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bash-4.3$ python 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 [5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156] ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']
3084 originated -> 12 as
3084 originated -> 5239 anarchism
12 as -> 3084 originated
12 as -> 6 a
6 a -> 195 term
6 a -> 12 as
195 term -> 2 of
195 term -> 6 a
Initialized
Average loss at step 0 : 260.113525391
Nearest to a: expired, colony, vassar, documentation, renamo, parades, uncompetitive, garbled,
Nearest to years: rolf, sciences, arid, subtlety, benzene, prefecture, ventilated, tupac,
Nearest to may: gabor, moussa, marathon, mccain, mondo, robbie, ordinations, koenigsegg,
Nearest to new: reznor, monophosphate, bonnie, kornilov, subordinates, arcas, holiness, offices,
Nearest to all: unfold, newton, campbell, rote, dong, kabbalist, newly, tice,
Nearest to used: collision, contents, phonetic, besht, fondly, unguarded, millay, servicio,
Nearest to called: landing, pi, acetylcholine, attorney, entrance, importer, crowe, position,
Nearest to that: prohibit, habit, comedian, mythical, mir, financi, sweyn, mages,
Nearest to three: reeves, wounding, blass, humans, myth, leukemia, ritually, manitoba,
Nearest to the: consisting, gibb, oman, seleucids, semiarid, promote, vectorborne, macs,
Nearest to when: interference, carnivores, wco, eine, suppress, continual, linton, teachings,
Nearest to its: vidzeme, aoc, missile, grapple, caterpillars, accessed, aaas, boyle,
Nearest to use: rigorously, ashoka, weizenbaum, shedding, white, thuringiensis, gettysburg, expectation,
Nearest to he: dont, hutchins, whit, withhold, reina, push, anatolian, illustrious,
Nearest to six: equilateral, viscosity, self, zed, epa, ecuadorian, pap, gisela,
Nearest to s: mendeleev, vulgaris, pack, zangger, blind, respectful, attica, grenades,
Average loss at step 2000 : 113.902854166
Average loss at step 4000 : 52.8573114848
Average loss at step 6000 : 33.0519484727
Average loss at step 8000 : 24.1580560323
Average loss at step 10000 : 17.9564257891
Nearest to a: the, this, and, UNK, influence, scrimmage, victoriae, analogue,
Nearest to years: sciences, amsterdam, nine, victoriae, six, arid, mathbf, reginae,
Nearest to may: meeting, developers, psychology, plot, spread, amyotrophic, alpina, koenigsegg,
Nearest to new: and, gb, circles, go, offices, proposal, vesta, seam,
Nearest to all: newton, reach, newly, campbell, absence, austin, history, coordinate,
Nearest to used: phonetic, contents, mya, breeds, left, decline, disagree, appeal,
Nearest to called: position, landing, attorney, railway, pi, entrance, split, vichy,
Nearest to that: never, victoriae, and, habit, johan, photographs, reality, austin,
Nearest to three: gb, mathbf, gland, tubing, nine, eight, zero, victoriae,
Nearest to the: a, UNK, and, his, victoriae, mathbf, one, its,
Nearest to when: interference, austin, suppress, eine, carnivores, mathbf, teachings, unfiltered,
Nearest to its: the, accessed, version, striking, victoriae, pure, a, pierce,
Nearest to use: gland, recommend, white, rigorously, accept, austin, politically, gettysburg,
Nearest to he: it, mathbf, and, gollancz, gland, you, an, they,
Nearest to six: nine, zero, mathbf, vs, phi, reginae, one, victoriae,
Nearest to s: and, sahara, troops, currently, the, zero, boroughs, of,
Average loss at step 12000 : 13.8088547783
Average loss at step 14000 : 11.5112998215
Average loss at step 16000 : 9.71534106708
Average loss at step 18000 : 8.59706710231
Average loss at step 20000 : 7.75074116433
Nearest to a: the, this, agouti, or, adiabatic, and, culture, agnostic,
Nearest to years: amsterdam, sciences, benzene, four, victoriae, six, five, happens,
Nearest to may: zee, developers, psychology, polyhedra, extremophiles, precisely, meeting, koenigsegg,
Nearest to new: circles, holiness, and, gb, proposal, offices, seam, vesta,
Nearest to all: polyhedra, newton, history, ceiling, reach, coordinate, the, absence,
Nearest to used: contents, breeds, phonetic, decline, disagree, mya, dasyprocta, left,
Nearest to called: position, pi, attorney, exchanging, landing, entrance, railway, split,
Nearest to that: which, habit, and, johan, never, victoriae, in, dasyprocta,
Nearest to three: eight, five, two, six, seven, zero, nine, dasyprocta,
Nearest to the: a, his, its, agouti, their, one, victoriae, adiabatic,
Nearest to when: austin, interference, suppress, eine, carnivores, mathbf, bosnia, and,
Nearest to its: the, his, their, aoc, tumor, accessed, metis, agouti,
Nearest to use: ashoka, gland, rigorously, recommend, polyhedra, accept, shedding, gettysburg,
Nearest to he: it, they, and, who, mathbf, there, was, zero,
Nearest to six: nine, eight, zero, four, seven, three, five, mathbf,
Nearest to s: and, zero, the, his, of, dasyprocta, sahara, troops,
Average loss at step 22000 : 7.29002120793
Average loss at step 24000 : 6.93063378406
Average loss at step 26000 : 6.62375506437
Average loss at step 28000 : 6.15877870786
Average loss at step 30000 : 6.14214335346
Nearest to a: the, this, agouti, or, aba, abet, victoriae, their,
Nearest to years: amsterdam, sciences, benzene, four, victoriae, five, subtlety, happens,
Nearest to may: zee, can, trudeau, developers, extremophiles, nine, koenigsegg, jack,
Nearest to new: circles, holiness, yangon, seam, offices, gb, proposal, profound,
Nearest to all: polyhedra, newton, history, absence, ceiling, virgin, coordinate, unfold,
Nearest to used: contents, breeds, decline, disagree, left, phonetic, appeal, dasyprocta,
Nearest to called: exchanging, pi, position, iic, attorney, entrance, champlain, split,
Nearest to that: which, habit, never, johan, victoriae, polyhedra, antitrust, objective,
Nearest to three: eight, four, five, six, seven, two, nine, zero,
Nearest to the: their, its, his, a, agouti, adiabatic, this, victoriae,
Nearest to when: primigenius, austin, suppress, and, eine, with, interference, mathbf,
Nearest to its: the, their, his, aoc, metis, tumor, accessed, a,
Nearest to use: ashoka, gland, rigorously, polyhedra, recommend, accept, gettysburg, rooms,
Nearest to he: it, they, she, who, and, there, mathbf, gotten,
Nearest to six: eight, nine, four, seven, five, three, zero, two,
Nearest to s: and, his, of, the, zero, or, two, dasyprocta,
Average loss at step 32000 : 5.85488548732
Average loss at step 34000 : 5.83861638045
Average loss at step 36000 : 5.72251720762
Average loss at step 38000 : 5.25196716714
Average loss at step 40000 : 5.48282074535
Nearest to a: the, agouti, or, albury, this, aba, victoriae, abatis,
Nearest to years: amsterdam, six, sciences, benzene, four, five, victoriae, reuptake,
Nearest to may: can, zee, trudeau, should, will, eight, might, would,
Nearest to new: circles, goo, albury, gb, monophosphate, holiness, profound, yangon,
Nearest to all: polyhedra, newton, history, absence, coordinate, virgin, roper, mathbf,
Nearest to used: decline, dasyprocta, breeds, contents, disagree, mya, monatomic, continuum,
Nearest to called: exchanging, pi, position, iic, acetylcholine, entrance, champlain, split,
Nearest to that: which, this, what, it, polyhedra, never, victoriae, habit,
Nearest to three: four, six, five, eight, seven, two, zero, nine,
Nearest to the: its, a, adiabatic, their, his, this, agouti, abet,
Nearest to when: primigenius, austin, with, suppress, eine, including, and, but,
Nearest to its: their, the, his, tumor, metis, aoc, agouti, abet,
Nearest to use: ashoka, gland, polyhedra, recommend, rigorously, accept, gettysburg, rooms,
Nearest to he: it, she, they, who, there, mathbf, but, gotten,
Nearest to six: seven, eight, four, five, nine, three, zero, two,
Nearest to s: and, his, dasyprocta, cinque, the, metis, two, kifl,
Average loss at step 42000 : 5.32659191382
Average loss at step 44000 : 5.27528748202
Average loss at step 46000 : 5.24571657467
Average loss at step 48000 : 5.05064962864
Average loss at step 50000 : 5.15474711931
Nearest to a: the, expired, agouti, hg, thibetanus, aba, appomattox, dipyramid,
Nearest to years: amsterdam, four, six, hoax, sciences, happens, five, victoriae,
Nearest to may: can, will, might, should, would, must, trudeau, eight,
Nearest to new: circles, albury, naaman, seam, scientifically, holiness, goo, monophosphate,
Nearest to all: polyhedra, newton, two, absence, mathbf, history, acts, three,
Nearest to used: decline, dasyprocta, breeds, handicap, monatomic, found, known, disagree,
Nearest to called: exchanging, iic, pi, position, acetylcholine, split, champlain, entrance,
Nearest to that: which, what, this, never, polyhedra, victoriae, naaman, but,
Nearest to three: four, six, seven, five, two, eight, one, nine,
Nearest to the: its, their, his, agouti, adiabatic, a, this, victoriae,
Nearest to when: primigenius, but, austin, eight, suppress, and, five, seven,
Nearest to its: their, the, his, agouti, tumor, metis, aoc, accessed,
Nearest to use: ashoka, polyhedra, gland, rigorously, recommend, accept, gettysburg, rooms,
Nearest to he: it, she, they, who, there, this, gotten, mathbf,
Nearest to six: eight, four, seven, five, three, nine, one, zero,
Nearest to s: his, zero, dasyprocta, and, cinque, the, was, metis,
Average loss at step 52000 : 5.18477400446
Average loss at step 54000 : 5.11114428246
Average loss at step 56000 : 5.03586944163
Average loss at step 58000 : 5.17337947047
Average loss at step 60000 : 4.93142760962
Nearest to a: the, ssbn, wct, thibetanus, agouti, callithrix, cebus, aba,
Nearest to years: four, amsterdam, six, microcebus, five, months, hoax, victoriae,
Nearest to may: can, will, would, might, should, must, could, trudeau,
Nearest to new: circles, naaman, albury, goo, seam, scientifically, callithrix, monophosphate,
Nearest to all: microcebus, polyhedra, two, callithrix, cebus, acts, three, mathbf,
Nearest to used: decline, handicap, microcebus, dasyprocta, found, continuum, known, cebus,
Nearest to called: exchanging, pi, ssbn, acetylcholine, iic, split, champlain, tom,
Nearest to that: which, this, what, never, cebus, it, objective, tamarin,
Nearest to three: five, four, six, seven, two, eight, nine, one,
Nearest to the: its, their, a, this, adiabatic, callithrix, agouti, his,
Nearest to when: primigenius, after, austin, but, suppress, five, tamarin, and,
Nearest to its: their, his, the, callithrix, her, agouti, cebus, metis,
Nearest to use: ashoka, polyhedra, gland, rigorously, gettysburg, recommend, microcebus, expectation,
Nearest to he: it, she, they, who, there, callithrix, but, ssbn,
Nearest to six: eight, five, four, seven, nine, three, zero, callithrix,
Nearest to s: his, wct, and, dasyprocta, callithrix, zero, was, cinque,
Average loss at step 62000 : 4.80653275287
Average loss at step 64000 : 4.80298928308
Average loss at step 66000 : 4.9778061583
Average loss at step 68000 : 4.92880989146
Average loss at step 70000 : 4.78815213633
Nearest to a: the, ssbn, upanija, wct, cebus, agouti, callithrix, expired,
Nearest to years: four, five, months, amsterdam, microcebus, six, hoax, reuptake,
Nearest to may: can, will, would, might, should, must, could, to,
Nearest to new: circles, naaman, goo, albury, seam, scientifically, monophosphate, yangon,
Nearest to all: many, tico, microcebus, some, polyhedra, callithrix, various, acts,
Nearest to used: known, found, handicap, decline, microcebus, dasyprocta, cebus, agouti,
Nearest to called: exchanging, ssbn, pi, champlain, acetylcholine, tom, ecc, iic,
Nearest to that: which, what, this, never, cebus, objective, dinar, tamarin,
Nearest to three: four, five, six, seven, two, eight, callithrix, nine,
Nearest to the: its, their, this, callithrix, agouti, a, adiabatic, wct,
Nearest to when: after, primigenius, austin, but, as, suppress, tamarin, with,
Nearest to its: their, his, the, callithrix, her, cebus, metis, agouti,
Nearest to use: polyhedra, ashoka, gland, unassigned, microcebus, callithrix, expectation, rigorously,
Nearest to he: it, she, they, who, there, callithrix, but, ssbn,
Nearest to six: eight, four, five, seven, three, nine, zero, callithrix,
Nearest to s: wct, his, dasyprocta, zero, thz, cinque, callithrix, or,
Average loss at step 72000 : 4.80325884366
Average loss at step 74000 : 4.78602541548
Average loss at step 76000 : 4.89614222682
Average loss at step 78000 : 4.78169331312
Average loss at step 80000 : 4.80316182685
Nearest to a: the, ssbn, wct, upanija, cegep, thighs, dipyramid, agouti,
Nearest to years: months, four, microcebus, amsterdam, hoax, five, reuptake, happens,
Nearest to may: can, will, would, might, should, must, could, to,
Nearest to new: circles, naaman, goo, seam, albury, scientifically, prague, monophosphate,
Nearest to all: microcebus, many, two, tico, some, callithrix, polyhedra, these,
Nearest to used: known, found, handicap, decline, dasyprocta, microcebus, agouti, cebus,
Nearest to called: exchanging, ssbn, hood, champlain, protested, customization, pi, ecc,
Nearest to that: which, what, this, objective, however, cebus, naaman, tamarin,
Nearest to three: four, five, six, two, seven, eight, callithrix, one,
Nearest to the: its, callithrix, their, agouti, wct, his, microsite, this,
Nearest to when: after, clodius, austin, but, primigenius, tamarin, because, five,
Nearest to its: their, his, the, her, callithrix, metis, agouti, tumor,
Nearest to use: polyhedra, ashoka, gland, cegep, unassigned, accept, microcebus, callithrix,
Nearest to he: it, she, they, who, there, callithrix, professions, iit,
Nearest to six: five, four, eight, seven, three, nine, two, zero,
Nearest to s: wct, dasyprocta, zero, his, cinque, callithrix, masterpieces, thz,
Average loss at step 82000 : 4.80276642525
Average loss at step 84000 : 4.78886307824
Average loss at step 86000 : 4.73723094189
Average loss at step 88000 : 4.69320081282
Average loss at step 90000 : 4.75272560072
Nearest to a: the, ssbn, wct, upanija, any, another, cegep, dipyramid,
Nearest to years: months, microcebus, four, amsterdam, hoax, five, reuptake, vannevar,
Nearest to may: can, will, would, might, should, must, could, cannot,
Nearest to new: circles, naaman, goo, one, albury, thaler, gb, seam,
Nearest to all: some, many, microcebus, tico, these, callithrix, various, polyhedra,
Nearest to used: known, found, microcebus, handicap, dasyprocta, seen, agouti, decline,
Nearest to called: exchanging, hood, ssbn, split, customization, thaler, protested, champlain,
Nearest to that: which, what, however, this, but, cebus, dinar, tamarin,
Nearest to three: four, two, five, seven, eight, six, callithrix, cegep,
Nearest to the: its, their, callithrix, agouti, wct, adiabatic, a, cegep,
Nearest to when: after, clodius, but, if, before, tamarin, because, primigenius,
Nearest to its: their, his, the, her, callithrix, agouti, metis, wct,
Nearest to use: polyhedra, ashoka, gland, cegep, callithrix, microcebus, clodius, catalysis,
Nearest to he: she, it, they, there, who, callithrix, iit, zero,
Nearest to six: seven, eight, five, four, nine, three, zero, callithrix,
Nearest to s: wct, dasyprocta, his, thz, chalcedon, cinque, zero, mating,
Average loss at step 92000 : 4.71442663682
Average loss at step 94000 : 4.60804726839
Average loss at step 96000 : 4.72456447947
Average loss at step 98000 : 4.62782734013
Average loss at step 100000 : 4.67684453142
Nearest to a: the, ssbn, any, upanija, wct, cegep, another, expired,
Nearest to years: months, four, microcebus, amsterdam, days, hoax, reuptake, happens,
Nearest to may: can, will, would, might, should, could, must, cannot,
Nearest to new: circles, naaman, goo, seam, prague, albury, scientifically, thaler,
Nearest to all: many, some, microcebus, tico, these, various, callithrix, two,
Nearest to used: known, found, dasyprocta, cebus, microcebus, handicap, seen, agouti,
Nearest to called: exchanging, hood, customization, thaler, split, protested, ssbn, ecc,
Nearest to that: which, what, however, this, but, polyhedra, cebus, cegep,
Nearest to three: four, five, six, two, seven, eight, callithrix, nine,
Nearest to the: its, their, callithrix, nordisk, adiabatic, agouti, wct, his,
Nearest to when: after, if, clodius, before, but, because, tamarin, where,
Nearest to its: their, his, the, her, callithrix, agouti, wct, thz,
Nearest to use: polyhedra, ashoka, cegep, callithrix, microcebus, gland, catalysis, clodius,
Nearest to he: she, it, they, who, there, callithrix, iit, already,
Nearest to six: seven, eight, five, four, nine, three, two, callithrix,
Nearest to s: wct, his, dasyprocta, thz, the, was, chalcedon, callithrix,
/usr/lib64/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
if self._edgecolors == str('face'):
hw11_natural_language_processing/martink_word2vec/word2vec_basic.py 0 → 100644
View file @ 428a8614
# 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.
# ==============================================================================
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
words = read_data(filename)
print('Data size', len(words))
# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 50000
def build_dataset(words):
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(vocabulary_size - 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
reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return data, count, dictionary, reverse_dictionary
data, count, dictionary, reverse_dictionary = build_dataset(words)
del words # 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)
for _ in range(span):
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
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]
buffer.append(data[data_index])
data_index = (data_index + 1) % 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(nce_weights, nce_biases, embed, train_labels,
num_sampled, 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.initialize_all_variables()
# 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:
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
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 visualize embeddings.")
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