text: #! /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
  
  # Parameters
  # ==================================================
  
  # 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 regularizaion lambda (default: 0.0)")
  
  # Training parameters
  tf.flags.DEFINE_integer("batch_size", 8, "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.batch_size
  print("\nParameters:")
  for attr, value in sorted(FLAGS.__flags.iteritems()):
      print("{}={}".format(attr.upper(), value))
  print("")
  
  
  # Data Preparatopn
  # ==================================================
  
  # Load data
  print("Loading data...")
  x, y, vocabulary, vocabulary_inv = data_helpers.load_data()
  # 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(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(vocabulary),
              embedding_size=FLAGS.embedding_dim,
              filter_sizes=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-4)
          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_def)
  
          # 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_def)
  
          # 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())
  
          # Initialize all variables
          sess.run(tf.initialize_all_variables())
  
          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(
              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))
  