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word2vec.py
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word2vec.py
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# 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.
# ==============================================================================
# !/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import sys
import numpy as np
import tensorflow as tf
from GAN.config import *
from word2vec.word2vec_helpers import build_dataset, generate_batch, plot_with_labels, save_model, load_model, \
plot_with_labels_selected
# Step 2: Build the dictionary and replace rare words with UNK token.
VOCABULARY_SIZE = config[Conf.VOCAB_SIZE]
EMBEDDING_SIZE = config[Conf.EMBEDDING_SIZE] # Dimension of the embedding vector.
NUM_STEPS = config[Conf.WORD2VEC_NUM_STEPS]
DATASET = "flowers" # flowers/flickr
if "plot" in sys.argv:
reverse_dictionary, final_embeddings, _ = load_model(EMBEDDING_SIZE, VOCABULARY_SIZE, NUM_STEPS, DATASET)
plot_with_labels(reverse_dictionary, final_embeddings,
filename="word2vec_%sd%svoc%ssteps_plot_%s" % (EMBEDDING_SIZE, VOCABULARY_SIZE, NUM_STEPS, DATASET), plot_only=500)
elif "plot_selection" in sys.argv:
reverse_dictionary, final_embeddings, dictionary = load_model(EMBEDDING_SIZE, VOCABULARY_SIZE, NUM_STEPS, DATASET)
selected_word_list = ["man", "woman", "boy", "girl", "blue", "yellow", "green", "red", "one", "two", "three", "chair", "table", "sweater", "dress", "suit"]
plot_with_labels_selected(reverse_dictionary, final_embeddings, selected_word_list,
filename="word2vec_selection_%sd%svoc%ssteps_plot_%s" % (EMBEDDING_SIZE, VOCABULARY_SIZE, NUM_STEPS, DATASET))
else:
# num_steps = 1
data, count, dictionary, reverse_dictionary = build_dataset(VOCABULARY_SIZE, DATASET)
print('Most common words (+UNK)', count[:5])
print('Sample datasets', 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.
batch, labels, data_index = generate_batch(data, batch_size=8, num_skips=2, skip_window=1, data_index=data_index)
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
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 datasets.
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.
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(graph=graph, config=tf_config) as session:
# We must initialize all variables before we use them.
init.run()
print("Initialized")
average_loss = 0
for step in range(NUM_STEPS):
batch_inputs, batch_labels, data_index = generate_batch(data, batch_size, num_skips, skip_window,
data_index)
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 range(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 range(top_k):
close_word = reverse_dictionary[nearest[k]]
log_str = "%s %s," % (log_str, close_word)
print(log_str)
final_embeddings = normalized_embeddings.eval()
plotting = False
saving = True
if saving:
save_model(reverse_dictionary, final_embeddings, EMBEDDING_SIZE, VOCABULARY_SIZE, NUM_STEPS, DATASET)
if plotting:
plot_with_labels(reverse_dictionary, final_embeddings)