Python tensorflow.serialize_sparse() Examples

def testSerializeDeserializeMany(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorValue_5x6(np.arange(6))
      sp_input1 = self._SparseTensorValue_3x4(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      combined_indices, combined_values, combined_shape = sess.run(
          sp_deserialized)

      self.assertAllEqual(combined_indices[:6, 0], [0] * 6)  # minibatch 0
      self.assertAllEqual(combined_indices[:6, 1:], sp_input0[0])
      self.assertAllEqual(combined_indices[6:, 0], [1] * 6)  # minibatch 1
      self.assertAllEqual(combined_indices[6:, 1:], sp_input1[0])
      self.assertAllEqual(combined_values[:6], sp_input0[1])
      self.assertAllEqual(combined_values[6:], sp_input1[1])
      self.assertAllEqual(combined_shape, [2, 5, 6]) 

def testFeedSerializeDeserializeMany(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      sp_input1 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      input1_val = self._SparseTensorValue_3x4(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      combined_indices, combined_values, combined_shape = sess.run(
          sp_deserialized, {sp_input0: input0_val, sp_input1: input1_val})

      self.assertAllEqual(combined_indices[:6, 0], [0] * 6)  # minibatch 0
      self.assertAllEqual(combined_indices[:6, 1:], input0_val[0])
      self.assertAllEqual(combined_indices[6:, 0], [1] * 6)  # minibatch 1
      self.assertAllEqual(combined_indices[6:, 1:], input1_val[0])
      self.assertAllEqual(combined_values[:6], input0_val[1])
      self.assertAllEqual(combined_values[6:], input1_val[1])
      self.assertAllEqual(combined_shape, [2, 5, 6]) 

def testDeserializeFailsWrongType(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      sp_input1 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      input1_val = self._SparseTensorValue_3x4(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int64)

      with self.assertRaisesOpError(
          r"Requested SparseTensor of type int64 but "
          r"SparseTensor\[0\].values.dtype\(\) == int32"):
        sess.run(
            sp_deserialized, {sp_input0: input0_val, sp_input1: input1_val}) 

def testDeserializeFailsInconsistentRank(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      sp_input1 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      input1_val = self._SparseTensorValue_1x1x1()
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      with self.assertRaisesOpError(
          r"Inconsistent rank across SparseTensors: rank prior to "
          r"SparseTensor\[1\] was: 3 but rank of SparseTensor\[1\] is: 4"):
        sess.run(
            sp_deserialized, {sp_input0: input0_val, sp_input1: input1_val}) 

def testDeserializeFailsInvalidProto(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = ["a", "b", "c"]
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      with self.assertRaisesOpError(
          r"Could not parse serialized_sparse\[1, 0\]"):
        sess.run(sp_deserialized, {sp_input0: input0_val}) 

def _read_word_record(data_queue):

    reader = tf.TFRecordReader() # Construct a general reader
    key, example_serialized = reader.read(data_queue) 

    feature_map = {
        'image/encoded':  tf.FixedLenFeature( [], dtype=tf.string, 
                                              default_value='' ),
        'image/labels':   tf.VarLenFeature( dtype=tf.int64 ), 
        'image/width':    tf.FixedLenFeature( [1], dtype=tf.int64,
                                              default_value=1 ),
        'image/filename': tf.FixedLenFeature([], dtype=tf.string,
                                             default_value='' ),
        'text/string':     tf.FixedLenFeature([], dtype=tf.string,
                                             default_value='' ),
        'text/length':    tf.FixedLenFeature( [1], dtype=tf.int64,
                                              default_value=1 )
    }
    features = tf.parse_single_example( example_serialized, feature_map )

    image = tf.image.decode_jpeg( features['image/encoded'], channels=1 ) #gray
    width = tf.cast( features['image/width'], tf.int32) # for ctc_loss
    label = tf.serialize_sparse( features['image/labels'] ) # for batching
    length = features['text/length']
    text = features['text/string']
    filename = features['image/filename']
    return image,width,label,length,text,filename 

def preprocess_fn( data ):
    """Parse the elements of the dataset"""

    feature_map = {
        'image/encoded'  :   tf.FixedLenFeature( [], dtype=tf.string, 
                                                 default_value='' ),
        'image/labels'   :   tf.VarLenFeature( dtype=tf.int64 ), 
        'image/width'    :   tf.FixedLenFeature( [1], dtype=tf.int64,
                                                 default_value=1 ),
        'image/filename' :   tf.FixedLenFeature( [], dtype=tf.string,
                                                 default_value='' ),
        'text/string'    :   tf.FixedLenFeature( [], dtype=tf.string,
                                                 default_value='' ),
        'text/length'    :   tf.FixedLenFeature( [1], dtype=tf.int64,
                                                 default_value=1 )
    }
    
    features = tf.parse_single_example( data, feature_map )
    
    # Initialize fields according to feature map

    # Convert to grayscale
    image = tf.image.decode_jpeg( features['image/encoded'], channels=1 ) 

    width = tf.cast( features['image/width'], tf.int32 ) # for ctc_loss
    label = tf.serialize_sparse( features['image/labels'] ) # for batching
    length = features['text/length']
    text = features['text/string']

    image = preprocess_image( image )

    return image, width, label, length, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text 

def _ReadExamples(filename_queue, shape, using_ctc, reader=None):
  """Builds network input tensor ops for TF Example.

  Args:
    filename_queue: Queue of filenames, from tf.train.string_input_producer
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    image:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    height:  Tensor int64 containing the height of the image.
    width:   Tensor int64 containing the width of the image.
    labels:  Serialized SparseTensor containing the int64 labels.
    text:    Tensor string of the utf8 truth text.
  """
  if reader:
    reader = reader()
  else:
    reader = tf.TFRecordReader()
  _, example_serialized = reader.read(filename_queue)
  example_serialized = tf.reshape(example_serialized, shape=[])
  features = tf.parse_single_example(
      example_serialized,
      {'image/encoded': parsing_ops.FixedLenFeature(
          [1], dtype=tf.string, default_value=''),
       'image/text': parsing_ops.FixedLenFeature(
           [1], dtype=tf.string, default_value=''),
       'image/class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/unpadded_class': parsing_ops.VarLenFeature(dtype=tf.int64),
       'image/height': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1),
       'image/width': parsing_ops.FixedLenFeature(
           [1], dtype=tf.int64, default_value=1)})
  if using_ctc:
    labels = features['image/unpadded_class']
  else:
    labels = features['image/class']
  labels = tf.serialize_sparse(labels)
  image = tf.reshape(features['image/encoded'], shape=[], name='encoded')
  image = _ImageProcessing(image, shape)
  height = tf.reshape(features['image/height'], [-1])
  width = tf.reshape(features['image/width'], [-1])
  text = tf.reshape(features['image/text'], shape=[])

  return image, height, width, labels, text