# 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

from tempfile import gettempdir

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/'

# pylint: disable=redefined-outer-name

def maybe_download(filename, expected_bytes):

return local_filename

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

# Read the data into a list of strings.

def read_data(filename):

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):

return data, count, dictionary, reversed_dictionary

# Filling 4 global variables:

# data - list of codes (integers from 0 to vocabulary_size-1).

# This is the original text but words are replaced by their codes

# count - map of words(strings) to count of occurrences

# dictionary - map of words(strings) to their codes(integers)

# reverse_dictionary - maps codes(integers) to words(strings)

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):

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.

num_sampled = 64 # Number of negative examples to sample.

# 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. These 3 variables are used only for

# displaying model accuracy, they don't affect calculation.

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)

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.

# Explanation of the meaning of NCE loss:

# http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

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.

# pylint: disable=missing-docstring

# Function to draw visualization of distance between embeddings.

def plot_with_labels(low_dim_embs, labels, filename):

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, os.path.join(gettempdir(), 'tsne.png'))

except ImportError as ex:

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

print(ex)