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word2vec训练

word2vec训练

作者: yanghedada | 来源:发表于2018-09-08 10:13 被阅读0次
    # -*- coding: utf-8 -*-
    """
    Created on Thu Apr 26 20:04:12 2018
    
    @author: yanghe
    """
    
    import collections
    import math
    import os
    import random
    import zipfile
    
    import numpy as np
    from six.moves import xrange 
    import tensorflow as tf
    
    
    
    def maybe_download(filename, expected_bytes):
      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)
    
    
    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))
    
    
    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 += 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
    
    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], reverse_dictionary[batch[i]],
            '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
    for i in range(8):
        print("目标单词:",reverse_dictionary[batch[i]],
               "对应编号为:".center(20)+str(batch[i]),
               "   对应的语境单词为: ".ljust(20)+reverse_dictionary[labels[i,0]],
               "    编号为",labels[i,0])
    
    # Step 4: Build and train a skip-gram model.
    
    batch_size = 150
    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.random.choice(valid_window, valid_size, replace=False)
    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)
    
      # 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]))
    
      # 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)
      saver = tf.train.Saver()
      # Add variable initializer.
      init = tf.global_variables_initializer()
    
    # Step 5: Begin training.
    num_steps = 100001
    
    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 > 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
    
        # 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()
      saver.save(session,'E:/python/CNN_test_tensorflow/Word2Vec/svae/moldel.ckpt')
    # Step 6: 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 = [reverse_dictionary[i] for i in xrange(plot_only)]
      plot_with_labels(low_dim_embs, labels)
    
    except ImportError:
      print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
    

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