title: How to save self-trained word2vec to a txt file with format like 'word2vec-google-news' or 'glove.6b.50d'
body: <p>I wonder that how can I save a self-trained word2vec to txt file with the format like 'word2vec-google-news' or 'glove.6b.50d' which has the tokens followed by matched vectors.<a href="https://i.stack.imgur.com/YdCbv.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/YdCbv.png" alt="."></a></p>
  
  <p>I export my self-trained vectors to txt file which only has vectors but no tokens in the front of those vectors.
  <a href="https://i.stack.imgur.com/gu2yr.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/gu2yr.png" alt="enter image description here"></a></p>
  
  <p>My code for training my own word2vec:</p>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import math
  import random
  import numpy as np
  from six.moves import xrange
  import zipfile
  import tensorflow as tf
  import pandas as pd
  
  filename = ('data/data.zip')
  
  # Step 1: Read the data into a list of strings.
  def read_data(filename):
    with zipfile.ZipFile(filename) as f:
      data = tf.compat.as_str(f.read(f.namelist()[0])).split()
      return data
  
  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))
      #print("count",len(count))
      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
              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], [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 &lt;= 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], reverse_dictionary[batch[i]],'-&gt;', labels[i, 0], reverse_dictionary[labels[i, 0]])
  
  # Step 4: Build and train a skip-gram model.
  batch_size = 128
  embedding_size = 128
  skip_window = 2
  num_skips = 2
  valid_size = 9
  valid_window = 100
  num_sampled = 64    # Number of negative examples to sample.
  valid_examples = np.random.choice(valid_window, valid_size, replace=False)
  
  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
      with tf.device('/cpu:0'):
          # Look up embeddings for inputs.
          embeddings = tf.Variable(
              tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
          embed = tf.nn.embedding_lookup(embeddings, train_inputs)
  
          # Construct the variables for the NCE loss
          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]),dtype=tf.float32)
  
      # 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, inputs=embed, labels=train_labels,
                   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()
  
  # Step 5: Begin training.
  num_steps = 20000
  
  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 &gt; 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
  
      final_embeddings = normalized_embeddings.eval()
  
  
  np.savetxt('data/w2v.txt', final_embeddings)
  </code></pre>