TF impl, Mikolov

word2VecTF.py

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+# Word2Vec
+# May 30, 2015, 1455 hrs
+# Vijay Prakash Dwivedi (mail@vijaydwivedi.com.np)
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import math
+import os
+import random
+
+import numpy as np
+from six.moves import xrange
+
+import tensorflow as tf
+
+os.system('clear')
+print("#----------------- wor2vec implementation in TensorFlow ----------------#")
+
+# -------------------------------------------- #
+# STEP 1: Read the data into a list of strings
+def read_data():
+	# Read the data file as a list of words
+	with open ("/home/vijay321/Desktop/IASNLP/text8", "r") as myfile:
+		read_data = myfile.readlines()
+
+	return read_data[0].split()
+
+words = read_data()
+print('Data size', len(words))
+
+
+# ------------------------------------------------------------------ #
+# STEP 2: Build the dictionary and replace rare words with UNK token
+vocabulary_size = 50000
+
+def build_dataset(words, vocabulary_size):
+	count = [['UNK', -1]]
+	count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
+	dictionary = dict()
+
+	for word, _ in count:
+		dictionary[word] = len(dictionary)	# here ranking (index) is done... Eg. dictionary['the'] = 1
+
+	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, vocabulary_size)
+del words	# 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)
+
+	# 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)
+# print(valid_examples)
+num_sampled = 64
+
+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)
+
+	# CPU Implementation
+	with tf.device('/cpu:0'):
+		# Look up embedding for inputs
+		embeddings = tf.Variable(tf.random_normal([vocabulary_size, embedding_size], -1.0, 1.0))
+		embed = tf.nn.embedding_lookup(embeddings, train_inputs)
+
+		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()
+	init = tf.initialize_all_variables()
+
+
+# ------------------------------------------ #
+# STEP 5: Begin training
+num_steps = 10001
+
+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]]
+					print(valid_word)
+					top_k = 8		# number of nearest neighbours
+					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()
+
+	print(len(embeddings.eval()))
+	print(valid_embeddings)
+
+# ------------------------------------------ #
+# 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.")