Upload new file

hw11_natural_language_processing/Gabriel_word2vec/word2vec_basic.py

+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import math
+import os
+import random
+import zipfile
+
+import numpy as np
+from six.moves import urllib
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+# 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.request.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 list of strings.
+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
+
+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 += 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 <= 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]])
+
+# Step 4: Build and train a skip-gram model.
+
+batch_size = 128
+embedding_size = 512  # 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(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)
+
+  # Add variable initializer.
+  init = tf.initialize_all_variables()
+
+# Step 5: Begin training.
+num_steps = 1000001
+
+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(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 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.")