train.py

#encoding = utf-8
#训练脚本
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helper
from TextCNN import TextCNN

from tensorflow.contrib import learn

# Parameters参数设置
# ==================================================

# Data loading params 数据加载参数
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-polarity.pos",
                       "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polarity.neg",
                       "Data source for the negative data.")

# Model Hyperparameters  超参数设置

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 训练参数
tf.flags.DEFINE_integer("batch_size", 16, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 10,
                        "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer(
    "evaluate_every", 100,
    "Evaluate model on dev set after this many steps (default: 100)")
#每一百轮便保存模型
tf.flags.DEFINE_integer("checkpoint_every", 100,
                        "Save model after this many steps (default: 100)")
#仅保存最近五次模型
tf.flags.DEFINE_integer("num_checkpoints", 5,
                        "Number of checkpoints to store (default: 5)")
# Misc Parameters 设备参数
#当指定的设备不存在时，自动分配（默认为TRUE）
tf.flags.DEFINE_boolean("allow_soft_placement", True,
                        "Allow device soft device placement")
#打印日志
tf.flags.DEFINE_boolean("log_device_placement", False,
                        "Log placement of ops on devices")

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()


# 定义一个训练步骤
# ==================================================
def train_step(x_batch, y_batch, train_op):
    """
    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)


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],  #我们的句子的长度。请记住，我们填充了所有句子的长度（我们的数据集为59）
            num_classes=y_train.shape[1],  #分类数，在我们的例子中是两个（正数和负数）。
            vocab_size=len(vocab_processor.vocabulary_),  #邮件字典不重复单词数目
            embedding_size=FLAGS.embedding_dim,  #中间层维度
            filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),  #过滤器大小
            num_filters=FLAGS.num_filters,  #过滤器数目
            l2_reg_lambda=FLAGS.l2_reg_lambda)  #l2惩罚项大小

        # Define Training procedure 定义训练过程
        global_step = tf.Variable(0, name="global_step", trainable=False)
        #指定优化器
        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
        #全局初始化
        sess.run(tf.global_variables_initializer())

        # Generate batches
        batches = data_helper.batch_iter(list(zip(x_train, y_train)),
                                         FLAGS.batch_size, FLAGS.num_epochs)
        # Training loop. For each batch...开始训练了
        for batch in batches:
            x_batch, y_batch = zip(*batch)
            train_step(x_batch, y_batch, train_op)
            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))


def main(argv=None):

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

    # Data Preparation
    # ==================================================
    # Load data加载数据，返回数据集和标签
    print("Loading data...")
    x_text, y = data_helper.load_data_and_labels(FLAGS.positive_data_file,
                                                 FLAGS.negative_data_file)

    # Build vocabulary生成单词字典
    #得到最大邮件长度（单词个数），不足的用0填充
    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打乱数据集
    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
    #负数：从后往前取
    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_)))  #Vocabulary Size: 18758
    print("Train/Dev split: {:d}/{:d}".format(
        len(y_train), len(y_dev)))  #Train/Dev split: 9596/1066


if __name__ == '__main__':
    tf.app.run()