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
  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("")
  
  x_raw = ["Venezuela seizes 4.8 million toys", "Trump says he's turning away 'billions", "New Zealand elects new leader"]
  y_test = [0, 0, 1]
  
  # 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:
      print(all_predictions, y_test)
      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)
  