diff --git a/hw11_natural_language_processing/Gabriel_word2vec/word2vec_basic.py b/hw11_natural_language_processing/Gabriel_word2vec/word2vec_basic.py
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+# 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.")