j314erre/text_cnn.py

## text_cnn.py
import tensorflow as tf
import numpy as np


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(
      self, sequence_length, num_classes, vocab_size,
      embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):

        # Placeholders for input, output and dropout
        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
        self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
        self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

        # Keeping track of l2 regularization loss (optional)
        l2_loss = tf.constant(0.0)

        # Embedding layer
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            self.W = tf.Variable(
                tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                name="W")
            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

        # Create a convolution + maxpool layer for each filter size
        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope("conv-maxpool-%s" % filter_size):
                # Convolution Layer
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                conv = tf.nn.conv2d(
                    self.embedded_chars_expanded,
                    W,
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                # Apply nonlinearity
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                # Maxpooling over the outputs
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, sequence_length - filter_size + 1, 1, 1],
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name="pool")
                pooled_outputs.append(pooled)

        # Combine all the pooled features
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(3, pooled_outputs)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])

        # Add dropout
        with tf.name_scope("dropout"):
            self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)

        # Final (unnormalized) scores and predictions
        with tf.name_scope("output"):
            W = tf.get_variable(
                "W",
                shape=[num_filters_total, num_classes],
                initializer=tf.contrib.layers.xavier_initializer())
            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
            l2_loss += tf.nn.l2_loss(W)
            l2_loss += tf.nn.l2_loss(b)
            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
            self.predictions = tf.argmax(self.scores, 1, name="predictions")

        # CalculateMean cross-entropy loss
        with tf.name_scope("loss"):
            losses = tf.nn.softmax_cross_entropy_with_logits(self.scores, self.input_y)
            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

        # Accuracy
        with tf.name_scope("accuracy"):
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")

## train.py
#! /usr/bin/env python

import numpy as np
import os
import time
import datetime
import data_helpers
import tensorflow as tf
from text_cnn import TextCNN
from tensorflow.contrib import learn

# Parameters
# ==================================================

# Model Hyperparameters
tf.flags.DEFINE_string("word2vec", None, "Word2vec file with pre-trained embeddings (default: None)")
tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularizaion lambda (default: 0.0)")

# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")


# Data Preparatopn
# ==================================================

# Load data
print("Loading data...")
x_text, y = data_helpers.load_data_and_labels()

# Build vocabulary
max_document_length = max([len(x.split(" ")) for x in x_text])
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
x = np.array(list(vocab_processor.fit_transform(x_text)))

# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]

# Split train/test set
# TODO: This is very crude, should use cross-validation
x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))


# Training
# ==================================================

with tf.Graph().as_default():
    session_conf = tf.ConfigProto(
      allow_soft_placement=FLAGS.allow_soft_placement,
      log_device_placement=FLAGS.log_device_placement)
    sess = tf.Session(config=session_conf)
    with sess.as_default():
        cnn = TextCNN(
            sequence_length=x_train.shape[1],
            num_classes=2,
            vocab_size=len(vocab_processor.vocabulary_),
            embedding_size=FLAGS.embedding_dim,
            filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
            num_filters=FLAGS.num_filters,
            l2_reg_lambda=FLAGS.l2_reg_lambda)

        # Define Training procedure
        global_step = tf.Variable(0, name="global_step", trainable=False)
        optimizer = tf.train.AdamOptimizer(1e-3)
        grads_and_vars = optimizer.compute_gradients(cnn.loss)
        train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)

        # Keep track of gradient values and sparsity (optional)
        grad_summaries = []
        for g, v in grads_and_vars:
            if g is not None:
                grad_hist_summary = tf.histogram_summary("{}/grad/hist".format(v.name), g)
                sparsity_summary = tf.scalar_summary("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
                grad_summaries.append(grad_hist_summary)
                grad_summaries.append(sparsity_summary)
        grad_summaries_merged = tf.merge_summary(grad_summaries)

        # Output directory for models and summaries
        timestamp = str(int(time.time()))
        out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
        print("Writing to {}\n".format(out_dir))

        # Summaries for loss and accuracy
        loss_summary = tf.scalar_summary("loss", cnn.loss)
        acc_summary = tf.scalar_summary("accuracy", cnn.accuracy)

        # Train Summaries
        train_summary_op = tf.merge_summary([loss_summary, acc_summary, grad_summaries_merged])
        train_summary_dir = os.path.join(out_dir, "summaries", "train")
        train_summary_writer = tf.train.SummaryWriter(train_summary_dir, sess.graph)

        # Dev summaries
        dev_summary_op = tf.merge_summary([loss_summary, acc_summary])
        dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
        dev_summary_writer = tf.train.SummaryWriter(dev_summary_dir, sess.graph)

        # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
        checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
        checkpoint_prefix = os.path.join(checkpoint_dir, "model")
        if not os.path.exists(checkpoint_dir):
            os.makedirs(checkpoint_dir)
        saver = tf.train.Saver(tf.all_variables())

        # Write vocabulary
        vocab_processor.save(os.path.join(out_dir, "vocab"))

        # Initialize all variables
        sess.run(tf.initialize_all_variables())
        # Initialize all variables
        sess.run(tf.initialize_all_variables())
        if FLAGS.word2vec:
            # initial matrix with random uniform
            initW = np.random.uniform(-0.25,0.25,(len(vocab_processor.vocabulary_), FLAGS.embedding_dim))
            # load any vectors from the word2vec
            print("Load word2vec file {}\n".format(FLAGS.word2vec))
            with open(FLAGS.word2vec, "rb") as f:
                header = f.readline()
                vocab_size, layer1_size = map(int, header.split())
                binary_len = np.dtype('float32').itemsize * layer1_size
                for line in xrange(vocab_size):
                    word = []
                    while True:
                        ch = f.read(1)
                        if ch == ' ':
                            word = ''.join(word)
                            break
                        if ch != '\n':
                            word.append(ch)
                    idx = vocab_processor.vocabulary_.get(word)
                    if idx != 0:
                        initW[idx] = np.fromstring(f.read(binary_len), dtype='float32')
                    else:
                        f.read(binary_len)

            sess.run(cnn.W.assign(initW))

        def train_step(x_batch, y_batch):
            """
            A single training step
            """
            feed_dict = {
              cnn.input_x: x_batch,
              cnn.input_y: y_batch,
              cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
            }
            _, step, summaries, loss, accuracy = sess.run(
                [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
                feed_dict)
            time_str = datetime.datetime.now().isoformat()
            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
            train_summary_writer.add_summary(summaries, step)

        def dev_step(x_batch, y_batch, writer=None):
            """
            Evaluates model on a dev set
            """
            feed_dict = {
              cnn.input_x: x_batch,
              cnn.input_y: y_batch,
              cnn.dropout_keep_prob: 1.0
            }
            step, summaries, loss, accuracy = sess.run(
                [global_step, dev_summary_op, cnn.loss, cnn.accuracy],
                feed_dict)
            time_str = datetime.datetime.now().isoformat()
            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
            if writer:
                writer.add_summary(summaries, step)

        # Generate batches
        batches = data_helpers.batch_iter(
            list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
        # Training loop. For each batch...
        for batch in batches:
            x_batch, y_batch = zip(*batch)
            train_step(x_batch, y_batch)
            current_step = tf.train.global_step(sess, global_step)
            if current_step % FLAGS.evaluate_every == 0:
                print("\nEvaluation:")
                dev_step(x_dev, y_dev, writer=dev_summary_writer)
                print("")
            if current_step % FLAGS.checkpoint_every == 0:
                path = saver.save(sess, checkpoint_prefix, global_step=current_step)
                print("Saved model checkpoint to {}\n".format(path))
	import tensorflow as tf
	import numpy as np


	class TextCNN(object):
	"""
	A CNN for text classification.
	Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
	"""
	def __init__(
	self, sequence_length, num_classes, vocab_size,
	embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):

	# Placeholders for input, output and dropout
	self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
	self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
	self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

	# Keeping track of l2 regularization loss (optional)
	l2_loss = tf.constant(0.0)

	# Embedding layer
	with tf.device('/cpu:0'), tf.name_scope("embedding"):
	self.W = tf.Variable(
	tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
	name="W")
	self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
	self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

	# Create a convolution + maxpool layer for each filter size
	pooled_outputs = []
	for i, filter_size in enumerate(filter_sizes):
	with tf.name_scope("conv-maxpool-%s" % filter_size):
	# Convolution Layer
	filter_shape = [filter_size, embedding_size, 1, num_filters]
	W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
	b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
	conv = tf.nn.conv2d(
	self.embedded_chars_expanded,
	W,
	strides=[1, 1, 1, 1],
	padding="VALID",
	name="conv")
	# Apply nonlinearity
	h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
	# Maxpooling over the outputs
	pooled = tf.nn.max_pool(
	h,
	ksize=[1, sequence_length - filter_size + 1, 1, 1],
	strides=[1, 1, 1, 1],
	padding='VALID',
	name="pool")
	pooled_outputs.append(pooled)

	# Combine all the pooled features
	num_filters_total = num_filters * len(filter_sizes)
	self.h_pool = tf.concat(3, pooled_outputs)
	self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])

	# Add dropout
	with tf.name_scope("dropout"):
	self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)

	# Final (unnormalized) scores and predictions
	with tf.name_scope("output"):
	W = tf.get_variable(
	"W",
	shape=[num_filters_total, num_classes],
	initializer=tf.contrib.layers.xavier_initializer())
	b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
	l2_loss += tf.nn.l2_loss(W)
	l2_loss += tf.nn.l2_loss(b)
	self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
	self.predictions = tf.argmax(self.scores, 1, name="predictions")

	# CalculateMean cross-entropy loss
	with tf.name_scope("loss"):
	losses = tf.nn.softmax_cross_entropy_with_logits(self.scores, self.input_y)
	self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

	# Accuracy
	with tf.name_scope("accuracy"):
	correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
	self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
	#! /usr/bin/env python

	import numpy as np
	import os
	import time
	import datetime
	import data_helpers
	import tensorflow as tf
	from text_cnn import TextCNN
	from tensorflow.contrib import learn

	# Parameters
	# ==================================================

	# Model Hyperparameters
	tf.flags.DEFINE_string("word2vec", None, "Word2vec file with pre-trained embeddings (default: None)")
	tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
	tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
	tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
	tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
	tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularizaion lambda (default: 0.0)")

	# Training parameters
	tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
	tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
	tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)")
	tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
	# Misc Parameters
	tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
	tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")

	FLAGS = tf.flags.FLAGS
	FLAGS._parse_flags()
	print("\nParameters:")
	for attr, value in sorted(FLAGS.__flags.items()):
	print("{}={}".format(attr.upper(), value))
	print("")


	# Data Preparatopn
	# ==================================================

	# Load data
	print("Loading data...")
	x_text, y = data_helpers.load_data_and_labels()

	# Build vocabulary
	max_document_length = max([len(x.split(" ")) for x in x_text])
	vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
	x = np.array(list(vocab_processor.fit_transform(x_text)))

	# Randomly shuffle data
	np.random.seed(10)
	shuffle_indices = np.random.permutation(np.arange(len(y)))
	x_shuffled = x[shuffle_indices]
	y_shuffled = y[shuffle_indices]

	# Split train/test set
	# TODO: This is very crude, should use cross-validation
	x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
	y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
	print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
	print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))


	# Training
	# ==================================================

	with tf.Graph().as_default():
	session_conf = tf.ConfigProto(
	allow_soft_placement=FLAGS.allow_soft_placement,
	log_device_placement=FLAGS.log_device_placement)
	sess = tf.Session(config=session_conf)
	with sess.as_default():
	cnn = TextCNN(
	sequence_length=x_train.shape[1],
	num_classes=2,
	vocab_size=len(vocab_processor.vocabulary_),
	embedding_size=FLAGS.embedding_dim,
	filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
	num_filters=FLAGS.num_filters,
	l2_reg_lambda=FLAGS.l2_reg_lambda)

	# Define Training procedure
	global_step = tf.Variable(0, name="global_step", trainable=False)
	optimizer = tf.train.AdamOptimizer(1e-3)
	grads_and_vars = optimizer.compute_gradients(cnn.loss)
	train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)

	# Keep track of gradient values and sparsity (optional)
	grad_summaries = []
	for g, v in grads_and_vars:
	if g is not None:
	grad_hist_summary = tf.histogram_summary("{}/grad/hist".format(v.name), g)
	sparsity_summary = tf.scalar_summary("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
	grad_summaries.append(grad_hist_summary)
	grad_summaries.append(sparsity_summary)
	grad_summaries_merged = tf.merge_summary(grad_summaries)

	# Output directory for models and summaries
	timestamp = str(int(time.time()))
	out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
	print("Writing to {}\n".format(out_dir))

	# Summaries for loss and accuracy
	loss_summary = tf.scalar_summary("loss", cnn.loss)
	acc_summary = tf.scalar_summary("accuracy", cnn.accuracy)

	# Train Summaries
	train_summary_op = tf.merge_summary([loss_summary, acc_summary, grad_summaries_merged])
	train_summary_dir = os.path.join(out_dir, "summaries", "train")
	train_summary_writer = tf.train.SummaryWriter(train_summary_dir, sess.graph)

	# Dev summaries
	dev_summary_op = tf.merge_summary([loss_summary, acc_summary])
	dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
	dev_summary_writer = tf.train.SummaryWriter(dev_summary_dir, sess.graph)

	# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
	checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
	checkpoint_prefix = os.path.join(checkpoint_dir, "model")
	if not os.path.exists(checkpoint_dir):
	os.makedirs(checkpoint_dir)
	saver = tf.train.Saver(tf.all_variables())

	# Write vocabulary
	vocab_processor.save(os.path.join(out_dir, "vocab"))

	# Initialize all variables
	sess.run(tf.initialize_all_variables())
	# Initialize all variables
	sess.run(tf.initialize_all_variables())
	if FLAGS.word2vec:
	# initial matrix with random uniform
	initW = np.random.uniform(-0.25,0.25,(len(vocab_processor.vocabulary_), FLAGS.embedding_dim))
	# load any vectors from the word2vec
	print("Load word2vec file {}\n".format(FLAGS.word2vec))
	with open(FLAGS.word2vec, "rb") as f:
	header = f.readline()
	vocab_size, layer1_size = map(int, header.split())
	binary_len = np.dtype('float32').itemsize * layer1_size
	for line in xrange(vocab_size):
	word = []
	while True:
	ch = f.read(1)
	if ch == ' ':
	word = ''.join(word)
	break
	if ch != '\n':
	word.append(ch)
	idx = vocab_processor.vocabulary_.get(word)
	if idx != 0:
	initW[idx] = np.fromstring(f.read(binary_len), dtype='float32')
	else:
	f.read(binary_len)

	sess.run(cnn.W.assign(initW))

	def train_step(x_batch, y_batch):
	"""
	A single training step
	"""
	feed_dict = {
	cnn.input_x: x_batch,
	cnn.input_y: y_batch,
	cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
	}
	_, step, summaries, loss, accuracy = sess.run(
	[train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
	feed_dict)
	time_str = datetime.datetime.now().isoformat()
	print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
	train_summary_writer.add_summary(summaries, step)

	def dev_step(x_batch, y_batch, writer=None):
	"""
	Evaluates model on a dev set
	"""
	feed_dict = {
	cnn.input_x: x_batch,
	cnn.input_y: y_batch,
	cnn.dropout_keep_prob: 1.0
	}
	step, summaries, loss, accuracy = sess.run(
	[global_step, dev_summary_op, cnn.loss, cnn.accuracy],
	feed_dict)
	time_str = datetime.datetime.now().isoformat()
	print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
	if writer:
	writer.add_summary(summaries, step)

	# Generate batches
	batches = data_helpers.batch_iter(
	list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
	# Training loop. For each batch...
	for batch in batches:
	x_batch, y_batch = zip(*batch)
	train_step(x_batch, y_batch)
	current_step = tf.train.global_step(sess, global_step)
	if current_step % FLAGS.evaluate_every == 0:
	print("\nEvaluation:")
	dev_step(x_dev, y_dev, writer=dev_summary_writer)
	print("")
	if current_step % FLAGS.checkpoint_every == 0:
	path = saver.save(sess, checkpoint_prefix, global_step=current_step)
	print("Saved model checkpoint to {}\n".format(path))