text: import zipfile
  import tensorflow as tf
  import numpy as np
  import random
  import math
  import collections
  
  from matplotlib import pylab
  from sklearn.manifold import TSNE
  
  from not_mnist.img_pickle import save_obj
  from not_mnist.load_data import maybe_download
  
  
  def read_data(filename):
      """Extract the first file enclosed in a zip file as a list of words"""
      with zipfile.ZipFile(filename) as f:
          data = tf.compat.as_str(f.read(f.namelist()[0])).split()
      return data
  
  
  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
  
  
  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
  
  
  # load data
  url = 'http://mattmahoney.net/dc/'
  filename = maybe_download('text8.zip', 31344016, url=url)
  
  # read data
  words = read_data(filename)
  print('Data size %d' % len(words))
  
  vocabulary_size = 50000
  context_size = 1
  
  data, count, dictionary, reverse_dictionary = build_dataset(words)
  print('Most common words (+UNK)', count[:5])
  print('Sample data', data[:10])
  del words  # Hint to reduce memory.
  
  # split data
  data_index = 0
  
  print('data:', [reverse_dictionary[di] for di in data[:8]])
  
  for num_skips, skip_window in [(2, 1), (4, 2)]:
      batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)
      print('\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))
      print('    batch:', [reverse_dictionary[bi] for bi in batch])
      print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])
  
  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(range(valid_window), valid_size))
  num_sampled = 64  # Number of negative examples to sample.
  
  # tensor: Train a skip-gram model, word2vec
  graph = tf.Graph()
  
  with graph.as_default(), tf.device('/cpu:0'):
      # Input data.
      train_dataset = 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)
  
      # Variables.
      embeddings = tf.Variable(
          tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
      softmax_weights = tf.Variable(
          tf.truncated_normal([vocabulary_size, embedding_size],
                              stddev=1.0 / math.sqrt(embedding_size)))
      softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))
  
      # Model.
      # Look up embeddings for inputs.
      embed = tf.nn.embedding_lookup(embeddings, train_dataset)
      # Compute the softmax loss, using a sample of the negative labels each time.
      loss = tf.reduce_mean(
          tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,
                                     train_labels, num_sampled, vocabulary_size))
  
      # Optimizer.
      optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)
  
      # Compute the similarity between minibatch examples and all embeddings.
      # We use the cosine distance:
      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, tf.transpose(normalized_embeddings))
  
  # flow
  num_steps = 100001
  
  with tf.Session(graph=graph) as session:
      tf.initialize_all_variables().run()
      print('Initialized')
      average_loss = 0
      for step in range(num_steps):
          batch_data, batch_labels = generate_batch(
              batch_size, num_skips, skip_window)
          feed_dict = {train_dataset: batch_data, train_labels: batch_labels}
          _, l = session.run([optimizer, loss], feed_dict=feed_dict)
          average_loss += l
          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 %d: %f' % (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 range(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 = 'Nearest to %s:' % valid_word
                  for k in range(top_k):
                      close_word = reverse_dictionary[nearest[k]]
                      log = '%s %s,' % (log, close_word)
                  print(log)
      final_embeddings = normalized_embeddings.eval()
      save_obj('text8_embed.pickle', final_embeddings)
  
  num_points = 400
  
  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
  two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points + 1, :])
  
  
  def plot(embeddings, labels):
      assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
      pylab.figure(figsize=(15, 15))  # in inches
      for i, label in enumerate(labels):
          x, y = embeddings[i, :]
          pylab.scatter(x, y)
          pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
                         ha='right', va='bottom')
      pylab.show()
  
  
  words = [reverse_dictionary[i] for i in range(1, num_points + 1)]
  plot(two_d_embeddings, words)
  