基于Tensorflow里CNN文本分类

使用CNN进行文本分类

卷积神经网络

英文邮件分类

语料

simplistic , silly and tedious . 
it's so laddish and juvenile , only teenage boys could possibly find it funny . 
exploitative and largely devoid of the depth or sophistication that would make watching such a graphic treatment of the crimes bearable . 
[garbus] discards the potential for pathological study , exhuming instead , the skewed melodrama of the circumstantial situation . 
a visually flashy but narratively opaque and emotionally vapid exercise in style and mystification . 
the story is also as unoriginal as they come , already having been recycled more times than i'd care to count . 
about the only thing to give the movie points for is bravado -- to take an entirely stale concept and push it through the audience's meat grinder one more time . 
not so much farcical as sour . 
unfortunately the story and the actors are served with a hack script . 
all the more disquieting for its relatively gore-free allusions to the serial murders , but it falls down in its attempts to humanize its subject . 
a sentimental mess that never rings true . 
while the performances are often engaging , this loose collection of largely improvised numbers would probably have worked better as a one-hour tv documentary . 
interesting , but not compelling . 
on a cutting room floor somewhere lies . . . footage that might have made no such thing a trenchant , ironic cultural satire instead of a frustrating misfire . 
while the ensemble player who gained notice in guy ritchie's lock , stock and two smoking barrels and snatch has the bod , he's unlikely to become a household name on the basis of his first starring vehicle . 
there is a difference between movies with the courage to go over the top and movies that don't care about being stupid
nothing here seems as funny as it did in analyze this , not even joe viterelli as de niro's right-hand goombah . 
such master screenwriting comes courtesy of john pogue , the yale grad who previously gave us " the skulls " and last year's " rollerball . " enough said , except : film overboard ! 
here , common sense flies out the window , along with the hail of bullets , none of which ever seem to hit sascha . 
this 100-minute movie only has about 25 minutes of decent material . 
the execution is so pedestrian that the most positive comment we can make is that rob schneider actually turns in a pretty convincing performance as a prissy teenage girl . 
on its own , it's not very interesting . as a remake , it's a pale imitation . 
it shows that some studios firmly believe that people have lost the ability to think and will forgive any shoddy product as long as there's a little girl-on-girl action . 
a farce of a parody of a comedy of a premise , it isn't a comparison to reality so much as it is a commentary about our knowledge of films . 
as exciting as all this exoticism might sound to the typical pax viewer , the rest of us will be lulled into a coma . 
the party scenes deliver some tawdry kicks . the rest of the film . . . is dudsville . 
our culture is headed down the toilet with the ferocity of a frozen burrito after an all-night tequila bender — and i know this because i've seen 'jackass : the movie . '
the criticism never rises above easy , cynical potshots at morally bankrupt characters . . . 
the movie's something-borrowed construction feels less the product of loving , well integrated homage and more like a mere excuse for the wan , thinly sketched story . killing time , that's all that's going on here . 
stupid , infantile , redundant , sloppy , over-the-top , and amateurish . yep , it's " waking up in reno . " go back to sleep . 
somewhere in the middle , the film compels , as demme experiments he harvests a few movie moment gems , but the field of roughage dominates . 
the action clichés just pile up . 
、、、、、

data_helpers.py

import numpy as np
import re
import itertools
from collections import Counter


def clean_str(string):
    """
    Tokenization/string cleaning for all datasets except for SST.
    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
    """
    string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
    string = re.sub(r"\'s", " \'s", string)
    string = re.sub(r"\'ve", " \'ve", string)
    string = re.sub(r"n\'t", " n\'t", string)
    string = re.sub(r"\'re", " \'re", string)
    string = re.sub(r"\'d", " \'d", string)
    string = re.sub(r"\'ll", " \'ll", string)
    string = re.sub(r",", " , ", string)
    string = re.sub(r"!", " ! ", string)
    string = re.sub(r"\(", " \( ", string)
    string = re.sub(r"\)", " \) ", string)
    string = re.sub(r"\?", " \? ", string)
    string = re.sub(r"\s{2,}", " ", string)
    return string.strip().lower()


def load_data_and_labels(positive_data_file, negative_data_file):
    """
    Loads MR polarity data from files, splits the data into words and generates labels.
    Returns split sentences and labels.
    """
    # Load data from files
    
    
    positive = open(positive_data_file, "rb").read().decode('utf-8')
    negative = open(negative_data_file, "rb").read().decode('utf-8')
    
    positive_examples = positive.split('\n')[:-1]
    negative_examples = negative.split('\n')[:-1]
    
    positive_examples = [s.strip() for s in positive_examples]
    negative_examples = [s.strip() for s in negative_examples]
    
    #positive_examples = list(open(positive_data_file, "rb").read().decode('utf-8'))
    #positive_examples = [s.strip() for s in positive_examples]
    #negative_examples = list(open(negative_data_file, "rb").read().decode('utf-8'))
    #negative_examples = [s.strip() for s in negative_examples]
    # Split by words
    x_text = positive_examples + negative_examples
    x_text = [clean_str(sent) for sent in x_text]
    # Generate labels
    positive_labels = [[0, 1] for _ in positive_examples]
    negative_labels = [[1, 0] for _ in negative_examples]
    y = np.concatenate([positive_labels, negative_labels], 0)
    return [x_text, y]


def batch_iter(data, batch_size, num_epochs, shuffle=True):
    """
    Generates a batch iterator for a dataset.
    """
    data = np.array(data)
    data_size = len(data)
    num_batches_per_epoch = int((len(data)-1)/batch_size) + 1
    for epoch in range(num_epochs):
        # Shuffle the data at each epoch
        if shuffle:
            shuffle_indices = np.random.permutation(np.arange(data_size))
            shuffled_data = data[shuffle_indices]
        else:
            shuffled_data = data
        for batch_num in range(num_batches_per_epoch):
            start_index = batch_num * batch_size
            end_index = min((batch_num + 1) * batch_size, data_size)
            yield shuffled_data[start_index:end_index]

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(pooled_outputs, 3)
        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(logits=self.scores, labels=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 tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn

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

# Data loading params
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")

# Model Hyperparameters
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 regularization 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)")
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")
# 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 Preparation
# ==================================================

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

# 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
dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
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=y_train.shape[1],
            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.summary.histogram("{}/grad/hist".format(v.name), g)
                sparsity_summary = tf.summary.scalar("{}/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.summary.merge(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.summary.scalar("loss", cnn.loss)
        acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)

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

        # Dev summaries
        dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
        dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
        dev_summary_writer = tf.summary.FileWriter(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.global_variables(), max_to_keep=FLAGS.num_checkpoints)

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

        # Initialize all variables
        sess.run(tf.global_variables_initializer())

        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, './', global_step=current_step)
                print("Saved model checkpoint to {}\n".format(path))

eval.py

#! /usr/bin/env python

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

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

# Data Parameters
tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the positive data.")

# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "./", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", False, "Evaluate on all training data")

# 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("")

# CHANGE THIS: Load data. Load your own data here
if FLAGS.eval_train:
    x_raw, y_test = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)
    y_test = np.argmax(y_test, axis=1)
else:
    x_raw = ["a masterpiece four years in the making", "everything is off."]
    y_test = [1, 0]

# Map data into vocabulary
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
x_test = np.array(list(vocab_processor.transform(x_raw)))

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
with 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():
        # Load the saved meta graph and restore variables
        saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
        saver.restore(sess, checkpoint_file)

        # Get the placeholders from the graph by name
        input_x = graph.get_operation_by_name("input_x").outputs[0]
        # input_y = graph.get_operation_by_name("input_y").outputs[0]
        dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]

        # Tensors we want to evaluate
        predictions = graph.get_operation_by_name("output/predictions").outputs[0]

        # Generate batches for one epoch
        batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)

        # Collect the predictions here
        all_predictions = []

        for x_test_batch in batches:
            batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
            all_predictions = np.concatenate([all_predictions, batch_predictions])

# Print accuracy if y_test is defined
if y_test is not None:
    correct_predictions = float(sum(all_predictions == y_test))
    print("Total number of test examples: {}".format(len(y_test)))
    print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))

# Save the evaluation to a csv
predictions_human_readable = np.column_stack((np.array(x_raw), all_predictions))
out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
print("Saving evaluation to {0}".format(out_path))
with open(out_path, 'w') as f:
    csv.writer(f).writerows(predictions_human_readable)

中文邮件分类

语料

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、、、、、、、

data_helpers.py

# encoding: UTF-8

import numpy as np
import re
import itertools
from collections import Counter
import os
import word2vec_helpers
import time
import pickle

def load_data_and_labels(input_text_file, input_label_file, num_labels):
    x_text = read_and_clean_zh_file(input_text_file)
    y = None if not os.path.exists(input_label_file) else map(int, list(open(input_label_file, "r").readlines()))
    return (x_text, y)

def load_positive_negative_data_files(positive_data_file, negative_data_file):
    """
    Loads MR polarity data from files, splits the data into words and generates labels.
    Returns split sentences and labels.
    """
    # Load data from files
    positive_examples = read_and_clean_zh_file(positive_data_file)
    negative_examples = read_and_clean_zh_file(negative_data_file)
    # Combine data
    x_text = positive_examples + negative_examples
    # Generate labels
    positive_labels = [[0, 1] for _ in positive_examples]
    negative_labels = [[1, 0] for _ in negative_examples]
    y = np.concatenate([positive_labels, negative_labels], 0)
    return [x_text, y]

def padding_sentences(input_sentences, padding_token, padding_sentence_length = None):
    sentences = [sentence.split(' ') for sentence in input_sentences]
    max_sentence_length = padding_sentence_length if padding_sentence_length is not None else max([len(sentence) for sentence in sentences])
    for sentence in sentences:
        if len(sentence) > max_sentence_length:
            sentence = sentence[:max_sentence_length]
        else:
            sentence.extend([padding_token] * (max_sentence_length - len(sentence)))
    return (sentences, max_sentence_length)

def batch_iter(data, batch_size, num_epochs, shuffle=True):
    '''
    Generate a batch iterator for a dataset
    '''
    data = np.array(data)
    data_size = len(data)
    num_batches_per_epoch = int((data_size - 1) / batch_size) + 1
    for epoch in range(num_epochs):
        if shuffle:
	    # Shuffle the data at each epoch
	        shuffle_indices = np.random.permutation(np.arange(data_size))
	        shuffled_data = data[shuffle_indices]
        else:
	        shuffled_data = data
    for batch_num in range(num_batches_per_epoch):
	    start_idx = batch_num * batch_size
	    end_idx = min((batch_num + 1) * batch_size, data_size)
	    yield shuffled_data[start_idx : end_idx]

def test():
    # Test clean_str
    print("Test")
    #print(clean_str("This's a huge dog! Who're going to the top."))
    # Test load_positive_negative_data_files
    #x_text,y = load_positive_negative_data_files("./tiny_data/rt-polarity.pos", "./tiny_data/rt-polarity.neg")
    #print(x_text)
    #print(y)
    # Test batch_iter
    #batches = batch_iter(x_text, 2, 4)
    #for batch in batches:
    #    print(batch)

def mkdir_if_not_exist(dirpath):
    if not os.path.exists(dirpath):
        os.mkdir(dirpath)

def seperate_line(line):
    return ''.join([word + ' ' for word in line])

def read_and_clean_zh_file(input_file, output_cleaned_file = None):
    lines = list(open(input_file, "rb").readlines())
    lines = [clean_str(seperate_line(line.decode('utf-8'))) for line in lines]
    if output_cleaned_file is not None:
        with open(output_cleaned_file, 'w') as f:
            for line in lines:
                f.write((line + '\n').encode('utf-8'))
    return lines

def clean_str(string):
    
    string = re.sub(r"[^\u4e00-\u9fff]", " ", string)
    #string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
    #string = re.sub(r"\'s", " \'s", string)
    #string = re.sub(r"\'ve", " \'ve", string)
    #string = re.sub(r"n\'t", " n\'t", string)
    #string = re.sub(r"\'re", " \'re", string)
    #string = re.sub(r"\'d", " \'d", string)
    #string = re.sub(r"\'ll", " \'ll", string)
    #string = re.sub(r",", " , ", string)
    #string = re.sub(r"!", " ! ", string)
    #string = re.sub(r"\(", " \( ", string)
    #string = re.sub(r"\)", " \) ", string)
    #string = re.sub(r"\?", " \? ", string)
    string = re.sub(r"\s{2,}", " ", string)
    #return string.strip().lower()
    return string.strip()

def saveDict(input_dict, output_file):
    with open(output_file, 'wb') as f:
        pickle.dump(input_dict, f) 

def loadDict(dict_file):
    output_dict = None
    with open(dict_file, 'rb') as f:
        output_dict = pickle.load(f)
    return output_dict

text_cnn.py

import tensorflow as tf
import numpy as np


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

        # Placeholders for input, output, dropout
        self.input_x = tf.placeholder(tf.float32, [None, sequence_length, embedding_size], 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
            # self.embedded_chars = [None(batch_size), sequence_size, embedding_size]
            # self.embedded_chars = [None(batch_size), sequence_size, embedding_size, 1(num_channels)]
        self.embedded_chars = self.input_x
        self.embedded_chars_expended = 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_expended,
		                            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 (unnomalized) 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")

	    # Calculate Mean cross-entropy loss
        with tf.name_scope("loss"):
            losses = tf.nn.softmax_cross_entropy_with_logits(logits = self.scores, labels = 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")

word2vec_helpers.py

# -*- coding: utf-8 -*-

'''
python word2vec_helpers.py input_file output_model_file output_vector_file
'''

# import modules & set up logging
import os
import sys
import logging
import multiprocessing
import time
import json
 
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence

def output_vocab(vocab):
    for k, v in vocab.items():
        print(k)

def embedding_sentences(sentences, embedding_size = 128, window = 5, min_count = 5, file_to_load = None, file_to_save = None):
    if file_to_load is not None:
        w2vModel = Word2Vec.load(file_to_load)
    else:
        w2vModel = Word2Vec(sentences, size = embedding_size, window = window, min_count = min_count, workers = multiprocessing.cpu_count())
        if file_to_save is not None:
            w2vModel.save(file_to_save)
    all_vectors = []
    embeddingDim = w2vModel.vector_size
    embeddingUnknown = [0 for i in range(embeddingDim)]
    for sentence in sentences:
        this_vector = []
        for word in sentence:
            if word in w2vModel.wv.vocab:
                this_vector.append(w2vModel[word])
            else:
                this_vector.append(embeddingUnknown)
        all_vectors.append(this_vector)
    return all_vectors


def generate_word2vec_files(input_file, output_model_file, output_vector_file, size = 128, window = 5, min_count = 5):
    start_time = time.time()

    # trim unneeded model memory = use(much) less RAM
    # model.init_sims(replace=True)
    model = Word2Vec(LineSentence(input_file), size = size, window = window, min_count = min_count, workers = multiprocessing.cpu_count())
    model.save(output_model_file)
    model.wv.save_word2vec_format(output_vector_file, binary=False)

    end_time = time.time()
    print("used time : %d s" % (end_time - start_time))

def run_main():
    program = os.path.basename(sys.argv[0])
    logger = logging.getLogger(program)
 
    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
    logger.info("running %s" % ' '.join(sys.argv))
 
    # check and process input arguments
    if len(sys.argv) < 4:
        print (globals()['__doc__'] % locals())
        sys.exit(1)
    input_file, output_model_file, output_vector_file = sys.argv[1:4]

    generate_word2vec_files(input_file, output_model_file, output_vector_file) 

def test():
    vectors = embedding_sentences([['first', 'sentence'], ['second', 'sentence']], embedding_size = 4, min_count = 1)
    print(vectors)

train.py

#! /usr/bin/env python
# encoding: utf-8

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
import word2vec_helpers
from text_cnn import TextCNN

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

# Data loading parameters
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
#tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
#tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")
tf.flags.DEFINE_string("positive_data_file", "./data/ham_5000.utf8", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/spam_5000.utf8", "Data source for the negative data.")
tf.flags.DEFINE_integer("num_labels", 2, "Number of labels for data. (default: 2)")

# Model hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-spearated 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 regularization lambda (default: 0.0)")

# Training paramters
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, "Evalue model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (defult: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")

# 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")

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

# Prepare 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))
if not os.path.exists(out_dir):
    os.makedirs(out_dir)

# Data preprocess
# =======================================================

# Load data
print("Loading data...")
x_text, y = data_helpers.load_positive_negative_data_files(FLAGS.positive_data_file, FLAGS.negative_data_file)

# Get embedding vector
sentences, max_document_length = data_helpers.padding_sentences(x_text, '')
x = np.array(word2vec_helpers.embedding_sentences(sentences, embedding_size = FLAGS.embedding_dim, file_to_save = os.path.join(out_dir, 'trained_word2vec.model')))
print("x.shape = {}".format(x.shape))
print("y.shape = {}".format(y.shape))

# Save params
training_params_file = os.path.join(out_dir, 'training_params.pickle')
params = {'num_labels' : FLAGS.num_labels, 'max_document_length' : max_document_length}
data_helpers.saveDict(params, training_params_file)

# Shuffle data randomly
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
dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
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 = y_train.shape[1],
	    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.summary.histogram("{}/grad/hist".format(v.name), g)
                sparsity_summary = tf.summary.scalar("{}/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.summary.merge(grad_summaries)

        # Output directory for models and summaries
        print("Writing to {}\n".format(out_dir))

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

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

        # Dev summaries
        dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
        dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
        dev_summary_writer = tf.summary.FileWriter(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.global_variables(), max_to_keep=FLAGS.num_checkpoints)

        # Initialize all variables
        sess.run(tf.global_variables_initializer())

        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))

eval.py

#! /usr/bin/env python

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
import word2vec_helpers
from text_cnn import TextCNN
import csv

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

# Data Parameters
tf.flags.DEFINE_string("input_text_file", "./data/spam_100.utf8", "Test text data source to evaluate.")
tf.flags.DEFINE_string("input_label_file", "", "Label file for test text data source.")

# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", True, "Evaluate on all training data")

# 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("")

# validate
# ==================================================

# validate checkout point file
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if checkpoint_file is None:
    print("Cannot find a valid checkpoint file!")
    exit(0)
print("Using checkpoint file : {}".format(checkpoint_file))

# validate word2vec model file
trained_word2vec_model_file = os.path.join(FLAGS.checkpoint_dir, "..", "trained_word2vec.model")
if not os.path.exists(trained_word2vec_model_file):
    print("Word2vec model file \'{}\' doesn't exist!".format(trained_word2vec_model_file))
print("Using word2vec model file : {}".format(trained_word2vec_model_file))

# validate training params file
training_params_file = os.path.join(FLAGS.checkpoint_dir, "..", "training_params.pickle")
if not os.path.exists(training_params_file):
    print("Training params file \'{}\' is missing!".format(training_params_file))
print("Using training params file : {}".format(training_params_file))

# Load params
params = data_helpers.loadDict(training_params_file)
num_labels = int(params['num_labels'])
max_document_length = int(params['max_document_length'])

# Load data
if FLAGS.eval_train:
    x_raw, y_test = data_helpers.load_data_and_labels(FLAGS.input_text_file, FLAGS.input_label_file, num_labels)
else:
    x_raw = ["a masterpiece four years in the making", "everything is off."]
    y_test = [1, 0]

# Get Embedding vector x_test
sentences, max_document_length = data_helpers.padding_sentences(x_raw, '', padding_sentence_length = max_document_length)
x_test = np.array(word2vec_helpers.embedding_sentences(sentences, file_to_load = trained_word2vec_model_file))
print("x_test.shape = {}".format(x_test.shape))


# Evaluation
# ==================================================
print("\nEvaluating...\n")
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
with 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():
        # Load the saved meta graph and restore variables
        saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
        saver.restore(sess, checkpoint_file)

        # Get the placeholders from the graph by name
        input_x = graph.get_operation_by_name("input_x").outputs[0]
        # input_y = graph.get_operation_by_name("input_y").outputs[0]
        dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]

        # Tensors we want to evaluate
        predictions = graph.get_operation_by_name("output/predictions").outputs[0]

        # Generate batches for one epoch
        batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)

        # Collect the predictions here
        all_predictions = []

        for x_test_batch in batches:
            batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
            all_predictions = np.concatenate([all_predictions, batch_predictions])

# Print accuracy if y_test is defined
if y_test is not None:
    correct_predictions = float(sum(all_predictions == y_test))
    print("Total number of test examples: {}".format(len(y_test)))
    print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))

# Save the evaluation to a csv
predictions_human_readable = np.column_stack((np.array([text.encode('utf-8') for text in x_raw]), all_predictions))
out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
print("Saving evaluation to {0}".format(out_path))
with open(out_path, 'w') as f:
    csv.writer(f).writerows(predictions_human_readable)