# coding: utf-8
'''
Note: Step 3 is missing. That's why I left it.
'''
from __future__ import absolute_import
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import zipfile
# Step 1: Download the data.
# Downloading data. If the file already exists, check that it was received correctly (the file size is the same).
# Return filename after download.
print("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 string.
# file (zipfile) 을 읽어옴
# text8.zip contains only one file. Looking at the code, it seems to be words separated by ''.
def read_data(filename):
f = zipfile.ZipFile(filename)
for name in f.namelist():
return f.read(name).split()
f.close()
words = read_data(filename)
print('Data size', len(words))
print('Sample words: ', words[:10])
# Step 2: Build the dictionary and replace rare words with UNK token.
print("\nStep 2: Build the dictionary and replace rare words with UNK token.")
vocabulary_size = 50000
def build_dataset(words):
#vocabulary_size is the number of frequent words to use.
#all words that do not fit within the top 50000 (vocabulary_size) are treated as UNK.
#Param words: literally a list of words
#Return data: indices of words including UNK. That is, words index list.
#Return count: collections.Counter which counts the frequency of occurrence of each word
#:return dictionary: {"word": "index"}
#:return reverse_dictionary: {"index": "word"}. e.g.) {0: 'UNK', 1: 'the', ...}
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
dictionary = dict()
for word, _ in count:
dictionary[word] = len(dictionary) # insert index to dictionary (len이 계속 증가하므로 결과적으로 index의 효과)
data = list()
unk_count = 0
for word in words:
if word in dictionary:
index = dictionary[word]
else:
index = 0 # dictionary['UNK']
unk_count = 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)
del words # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data: ', data[:10])
print('Sample count: ', count[:10])
print('Sample dict: ', dictionary.items()[:10])
print('Sample reverse dict: ', reverse_dictionary.items()[:10])
data_index = 0
# Step 4: Function to generate a training batch for the skip-gram model.
print("\nStep 4: Function to generate a training batch for the skip-gram model.")
def generate_batch(batch_size, num_skips, skip_window):
#Function to generate minibatch.
#Data_index is declared as global, which acts as static here. That is, the value of data_index is retained even if this function is continually recalled.
#Param batch_size: batch_size.
#Param num_skips: how many (target, context) pairs to generate in the context window.
#Param skip_window: context window size. The skip-gram model predicts the surrounding words from the target word, and skip_window defines the range of the surrounding words.
#Return batch: mini-batch of data.
#Return labels: labels of mini-batch. 2d array of [batch_size] [1].
global data_index
assert batch_size % num_skips == 0 # num_skips의 배수로 batch가 생성되므로.
assert num_skips <= 2 * skip_window # num_skips == 2*skip_window 이면 모든 context window의 context에 대해 pair가 생성된다.
# That is, it should not be larger than that.
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)
# Deques are a generalization of stacks and queues.
# The name is pronounced "deck" and is short for "double-ended queue".
# You can both push (append) & pop both sides.
# buffer = data[data_index:data_index+span] with circling
for _ in range(span):
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
# operator to discard the remainder or decimal point
#Skip-gram is a model for predicting surrounding context words from target words.
#Before we learn skip-gram model, we need to convert words to (target, context) type.
#The code below will do the job with batch_size size.
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:
# Extracting the context from the context window is done randomly.
# However, if skip_window * 2 == num_skips, all contexts are taken out, so random is not meaningful. The order is random only.
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)
return batch, labels
# To see how the batch is configured, output it once:
print("Generating batch ... ")
batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
print("Sample batches: ", batch[:10])
print("Sample labels: ", labels[:10])
for i in range(8):
print(batch[i], '->', labels[i, 0])
print(reverse_dictionary[batch[i]], '->', reverse_dictionary[labels[i, 0]])
# Step 5: Build and train a skip-gram model.
print("\nStep 5: 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.array(random.sample(np.arange(valid_window), valid_size))
# [0 ~ valid_window] And then sampled by valid_size.
# In other words, valid_examples is a random selection of 16 from 0 to 99.
num_sampled = 64 # Number of negative examples to sample.
print("valid_examples: ", valid_examples)
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
# Embedding_lookup This GPU implementation is not implemented, so it must be a CPU.
# Since default is GPU, it explicitly specifies CPU.
with tf.device('/cpu:0'):
# Look up embeddings for inputs.
# embedding matrix (vectors)
embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
# Only the embedding vectors pointed to by train_inputs (mini-batch; indices) in the entire embedding matrix are extracted
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Construct the variables for the NCE loss
# NCE loss is defined using a logistic regression model.
# That is, for logistic regression, weight and bias are needed for each word of vocabulary.
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.
# Calculate the cosine similarity between minibatch (valid_embeddings) and all embeddings.
# This process is to show which words are closest to each valid_example as the learning progresses (ie to show the learning process).
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)
# Step 6: Begin training
print("\nStep 6: Begin training")
num_steps = 100001
with tf.Session(graph=graph) as session:
# We must initialize all variables before we use them.
tf.initialize_all_variables().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()
# Use feed_dict to put data into the placeholder and learn it.
_, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
average_loss += loss_val
if step % 2000 == 0:
if step > 0:
average_loss = 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 7: Visualize the embeddings.
print("\nStep 7: 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:
# If you get an error here, update scikit-learn and matplotlib to the latest version.
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 and matplotlib to visualize embeddings.")