title: how to normalize input data for models in tensorflow
body: <p>My training data are saved in 3 files, each file is too large and cannot fit into memory.For each training example, the data are two dimensionality (2805 rows and 222 columns, the 222nd column is for label) and are numerical values. I would like to normalize the data before feeding into models for training. Below is my code for input_pipeline, and 
  the data has not been normalized before creating dataset. Is there some functions in tensorflow that can do normalization for my case? </p>
  
  <pre><code>dataset = tf.data.TextLineDataset([file1, file2, file3])
  # combine 2805 lines into a single example
  dataset = dataset.batch(2805)
  
  def parse_example(line_batch):
      record_defaults = [[1.0] for col in range(0, 221)]
      record_defaults.append([1])
      content = tf.decode_csv(line_batch, record_defaults = record_defaults, field_delim = '\t')
      features = tf.stack(content[0:221])
      features = tf.transpose(features)
      label = content[-1][-1]
      label = tf.one_hot(indices = tf.cast(label, tf.int32), depth = 2)
      return features, label
  
  dataset = dataset.map(parse_example)
  dataset = dataset.shuffle(1000)
  # batch multiple examples
  dataset = dataset.batch(batch_size)
  dataset = dataset.repeat(num_epochs)
  iterator = dataset.make_one_shot_iterator()
  data_batch, label_batch = iterator.get_next() 
  </code></pre>