ssh://[email protected]: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
https://raw.githubusercontent.com/tensorflow/tensorflow/r1.3/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
# 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.‘)
时间: 2024-08-05 12:46:10