diff options
Diffstat (limited to 'tensorflow/g3doc/tutorials/word2vec/word2vec_basic.py')
| -rw-r--r-- | tensorflow/g3doc/tutorials/word2vec/word2vec_basic.py | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/tensorflow/g3doc/tutorials/word2vec/word2vec_basic.py b/tensorflow/g3doc/tutorials/word2vec/word2vec_basic.py new file mode 100644 index 0000000000..0a981570fa --- /dev/null +++ b/tensorflow/g3doc/tutorials/word2vec/word2vec_basic.py @@ -0,0 +1,219 @@ +import collections +import math +import numpy as np +import os +import random +import tensorflow as tf +import urllib +import zipfile + +# 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.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. +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) + +# Step 2: Build the dictionary and replace rare words with UNK token. +vocabulary_size = 50000 + +def build_dataset(words): + count = [['UNK', -1]] + count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) + dictionary = dict() + for word, _ in count: + dictionary[word] = len(dictionary) + 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] + +data_index = 0 + +# Step 4: 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) + return batch, labels + +batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1) +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. + +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(xrange(valid_window), valid_size)) +num_sampled = 64 # Number of negative examples to sample. + +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) + + # Construct the variables. + embeddings = tf.Variable( + tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) + 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])) + + # Look up embeddings for inputs. + embed = tf.nn.embedding_lookup(embeddings, train_inputs) + + # 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) + +# 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() + _, 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. + +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 = dictionary.keys()[:plot_only] + plot_with_labels(low_dim_embs, labels) + +except ImportError: + print "Please install sklearn and matplotlib to visualize embeddings." + |