Tensorflow 简明教程

import os
import math
import numpy as np
import tensorflow as tf

from tensorflow.contrib.tensorboard.plugins import projector
batch_size = 64
embedding_dimension = 5
negative_samples = 8
LOG_DIR = "logs/word2vec_intro"

digit_to_word_map = {
   1: "One",
   2: "Two",
   3: "Three",
   4: "Four",
   5: "Five",
   6: "Six",
   7: "Seven",
   8: "Eight",
   9: "Nine"}
sentences = []

# Create two kinds of sentences - sequences of odd and even digits.
   for i in range(10000):
   rand_odd_ints = np.random.choice(range(1, 10, 2), 3)
      sentences.append(" ".join([digit_to_word_map[r] for r in rand_odd_ints]))
   rand_even_ints = np.random.choice(range(2, 10, 2), 3)
      sentences.append(" ".join([digit_to_word_map[r] for r in rand_even_ints]))

# Map words to indices
word2index_map = {}
index = 0

for sent in sentences:
   for word in sent.lower().split():

   if word not in word2index_map:
      word2index_map[word] = index
      index += 1
index2word_map = {index: word for word, index in word2index_map.items()}

vocabulary_size = len(index2word_map)

# Generate skip-gram pairs
skip_gram_pairs = []

for sent in sentences:
   tokenized_sent = sent.lower().split()

   for i in range(1, len(tokenized_sent)-1):
      word_context_pair = [[word2index_map[tokenized_sent[i-1]],
         word2index_map[tokenized_sent[i+1]]], word2index_map[tokenized_sent[i]]]

      skip_gram_pairs.append([word_context_pair[1], word_context_pair[0][0]])
      skip_gram_pairs.append([word_context_pair[1], word_context_pair[0][1]])

def get_skipgram_batch(batch_size):
   instance_indices = list(range(len(skip_gram_pairs)))
      np.random.shuffle(instance_indices)
   batch = instance_indices[:batch_size]
   x = [skip_gram_pairs[i][0] for i in batch]
   y = [[skip_gram_pairs[i][1]] for i in batch]
   return x, y

# batch example
x_batch, y_batch = get_skipgram_batch(8)
x_batch
y_batch
[index2word_map[word] for word in x_batch] [index2word_map[word[0]] for word in y_batch]

# Input data, labels train_inputs = tf.placeholder(tf.int32, shape = [batch_size])
   train_labels = tf.placeholder(tf.int32, shape = [batch_size, 1])

# Embedding lookup table currently only implemented in CPU with
   tf.name_scope("embeddings"):
   embeddings = tf.Variable(
      tf.random_uniform([vocabulary_size, embedding_dimension], -1.0, 1.0),
         name = 'embedding')
   # This is essentialy a lookup table
   embed = tf.nn.embedding_lookup(embeddings, train_inputs)

# Create variables for the NCE loss
nce_weights = tf.Variable(
   tf.truncated_normal([vocabulary_size, embedding_dimension], stddev = 1.0 /
      math.sqrt(embedding_dimension)))

nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

loss = tf.reduce_mean(
   tf.nn.nce_loss(weights = nce_weights, biases = nce_biases, inputs = embed,
   labels = train_labels,num_sampled = negative_samples,
   num_classes = vocabulary_size)) tf.summary.scalar("NCE_loss", loss)

# Learning rate decay
global_step = tf.Variable(0, trainable = False)
   learningRate = tf.train.exponential_decay(learning_rate = 0.1,
   global_step = global_step, decay_steps = 1000, decay_rate = 0.95, staircase = True)

train_step = tf.train.GradientDescentOptimizer(learningRate).minimize(loss)
   merged = tf.summary.merge_all()
with tf.Session() as sess:
   train_writer = tf.summary.FileWriter(LOG_DIR,
      graph = tf.get_default_graph())
   saver = tf.train.Saver()

   with open(os.path.join(LOG_DIR, 'metadata.tsv'), "w") as metadata:
      metadata.write('Name\tClass\n') for k, v in index2word_map.items():
      metadata.write('%s\t%d\n' % (v, k))

   config = projector.ProjectorConfig()
   embedding = config.embeddings.add() embedding.tensor_name = embeddings.name

   # Link this tensor to its metadata file (e.g. labels).
   embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')
      projector.visualize_embeddings(train_writer, config)

   tf.global_variables_initializer().run()

   for step in range(1000):
      x_batch, y_batch = get_skipgram_batch(batch_size) summary, _ = sess.run(
         [merged, train_step], feed_dict = {train_inputs: x_batch, train_labels: y_batch})
      train_writer.add_summary(summary, step)

      if step % 100 == 0:
         saver.save(sess, os.path.join(LOG_DIR, "w2v_model.ckpt"), step)
         loss_value = sess.run(loss, feed_dict = {
            train_inputs: x_batch, train_labels: y_batch})
         print("Loss at %d: %.5f" % (step, loss_value))

   # Normalize embeddings before using
   norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims = True))
   normalized_embeddings = embeddings /
      norm normalized_embeddings_matrix = sess.run(normalized_embeddings)

ref_word = normalized_embeddings_matrix[word2index_map["one"]]

cosine_dists = np.dot(normalized_embeddings_matrix, ref_word)
ff = np.argsort(cosine_dists)[::-1][1:10] for f in ff: print(index2word_map[f])
print(cosine_dists[f])