# 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(" Step 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(" Step 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(" Step 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(" Step 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(" Step 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.")