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Update Word2Vec.py #74

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31 changes: 16 additions & 15 deletions Word2Vec.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
import numpy as np
import re
from collections import Counter
Expand All @@ -25,7 +26,7 @@
# into one huge string, and then uses a Counter to identify words
# and the number of occurences
def processDataset(filename):
openedFile = open(filename, 'r')
openedFile = open(filename, 'r', encoding='utf-8')
allLines = openedFile.readlines()
myStr = ""
for line in allLines:
Expand All @@ -41,7 +42,7 @@ def createTrainingMatrices(dictionary, corpus):
yTrain=[]
for i in range(numTotalWords):
if i % 100000 == 0:
print 'Finished %d/%d total words' % (i, numTotalWords)
print ('Finished %d/%d total words' % (i, numTotalWords))
wordsAfter = allWords[i + 1:i + windowSize + 1]
wordsBefore = allWords[max(0, i - windowSize):i]
wordsAdded = wordsAfter + wordsBefore
Expand All @@ -61,19 +62,19 @@ def getTrainingBatch():
if (os.path.isfile('Word2VecXTrain.npy') and os.path.isfile('Word2VecYTrain.npy') and os.path.isfile('wordList.txt')):
xTrain = np.load('Word2VecXTrain.npy')
yTrain = np.load('Word2VecYTrain.npy')
print 'Finished loading training matrices'
print ('Finished loading training matrices')
with open("wordList.txt", "rb") as fp:
wordList = pickle.load(fp)
print 'Finished loading word list'
print ('Finished loading word list')

else:
fullCorpus, datasetDictionary = processDataset('conversationData.txt')
print 'Finished parsing and cleaning dataset'
print ('Finished parsing and cleaning dataset')
wordList = list(datasetDictionary.keys())
createOwnVectors = raw_input('Do you want to create your own vectors through Word2Vec (y/n)?')
createOwnVectors = input('Do you want to create your own vectors through Word2Vec (y/n)?')
if (createOwnVectors == 'y'):
xTrain, yTrain = createTrainingMatrices(datasetDictionary, fullCorpus)
print 'Finished creating training matrices'
print ('Finished creating training matrices')
np.save('Word2VecXTrain.npy', xTrain)
np.save('Word2VecYTrain.npy', yTrain)
else:
Expand All @@ -90,13 +91,13 @@ def getTrainingBatch():
numTrainingExamples = len(xTrain)
vocabSize = len(wordList)

sess = tf.Session()
embeddingMatrix = tf.Variable(tf.random_uniform([vocabSize, wordVecDimensions], -1.0, 1.0))
nceWeights = tf.Variable(tf.truncated_normal([vocabSize, wordVecDimensions], stddev=1.0 / math.sqrt(wordVecDimensions)))
sess = tf.compat.v1.Session()
embeddingMatrix = tf.Variable(tf.random.uniform([vocabSize, wordVecDimensions], -1.0, 1.0))
nceWeights = tf.Variable(tf.random.truncated_normal([vocabSize, wordVecDimensions], stddev=1.0 / math.sqrt(wordVecDimensions)))
nceBiases = tf.Variable(tf.zeros([vocabSize]))

inputs = tf.placeholder(tf.int32, shape=[batchSize])
outputs = tf.placeholder(tf.int32, shape=[batchSize, 1])
inputs = tf.compat.v1.placeholder(tf.int32, shape=[batchSize])
outputs = tf.compat.v1.placeholder(tf.int32, shape=[batchSize, 1])

embed = tf.nn.embedding_lookup(embeddingMatrix, inputs)

Expand All @@ -108,14 +109,14 @@ def getTrainingBatch():
num_sampled=numNegativeSample,
num_classes=vocabSize))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0).minimize(loss)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=1.0).minimize(loss)

sess.run(tf.global_variables_initializer())
sess.run(tf.compat.v1.global_variables_initializer())
for i in range(numIterations):
trainInputs, trainLabels = getTrainingBatch()
_, curLoss = sess.run([optimizer, loss], feed_dict={inputs: trainInputs, outputs: trainLabels})
if (i % 10000 == 0):
print ('Current loss is:', curLoss)
print 'Saving the word embedding matrix'
print ('Saving the word embedding matrix')
embedMatrix = embeddingMatrix.eval(session=sess)
np.save('embeddingMatrix.npy', embedMatrix)