Chapter_14_NLP.py

# ------------------------------------------------------------------------------
#                     Chapter 14 (Part 3): RNNs in NLP
# ------------------------------------------------------------------------------
from __future__ import division, print_function, unicode_literals
import numpy as np
import os
import tensorflow as tf
import sys

from six.moves import urllib

import errno
import os
import zipfile

from collections import Counter
import random
from collections import deque

# Fetch data
WORDS_PATH = "datasets/words"
WORDS_URL = 'http://mattmahoney.net/dc/text8.zip'

def mkdir_p(path):
    """Create directories, ok if they already exist.

    This is for python 2 support. In python >=3.2, simply use:
    >>> os.makedirs(path, exist_ok=True)
    """
    try:
        os.makedirs(path)
    except OSError as exc:
        if exc.errno == errno.EEXIST and os.path.isdir(path):
            pass
        else:
            raise

def fetch_words_data(words_url=WORDS_URL, words_path=WORDS_PATH):
    os.makedirs(words_path, exist_ok=True)
    zip_path = os.path.join(words_path, "words.zip")
    if not os.path.exists(zip_path):
        urllib.request.urlretrieve(words_url, zip_path)
    with zipfile.ZipFile(zip_path) as f:
        data = f.read(f.namelist()[0])
    return data.decode("ascii").split()

words = fetch_words_data()

# Build the dictionary
vocabulary_size = 50000

vocabulary = [("UNK", None)] + Counter(words).most_common(vocabulary_size - 1)
vocabulary = np.array([word for word, _ in vocabulary])
dictionary = {word: code for code, word in enumerate(vocabulary)}
data = np.array([dictionary.get(word, 0) for word in words])

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 = 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

# Build the model
batch_size = 128
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.

learning_rate = 0.01

# Input data
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

#
vocabulary_size = 50000
embedding_size = 150

init_embeddings = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)
embeddings = tf.Variable(init_embeds)

train_inputs = tf.placeholder(tf.int32, shape=[None])
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# NB it's possible to download pre-trained word embeddings

# Construct the variables for the NCE loss
nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / np.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, train_labels, embed, num_sampled, vocabulary_size))

# Construct the Adam optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(loss)

# Compute the cosine similarity between minibatch examples and all embeddings.
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=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()

# Train the model
num_steps = 10001

with tf.Session() as session:
    init.run()

    average_loss = 0
    for step in range(num_steps):
        print("\rIteration: {}".format(step), end="\t")
        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 training op (including it
        # in the list of returned values for session.run()
        _, loss_val = session.run([training_op, 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 range(valid_size):
                valid_word = vocabulary[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 range(top_k):
                    close_word = vocabulary[nearest[k]]
                    log_str = "%s %s," % (log_str, close_word)
                print(log_str)

    final_embeddings = normalized_embeddings.eval()

# saver = tf.train.Saver()
# saver..save("./my_final_embeddings.npy", final_embeddings)

print(final_embeddings)