Python tensorflow.python.util.compat.integral_types() Examples

The following are 12 code examples of tensorflow.python.util.compat.integral_types(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.util.compat , or try the search function .
Example #1
Source File: lookup_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __new__(cls, key):
    if len(key) != 2:
      raise ValueError("key must have size 2, got %s." % len(key))

    if not isinstance(key[0], compat.integral_types) or not isinstance(
        key[1], compat.integral_types):
      raise TypeError("Invalid key %s. Must be unsigned integer values." % key)

    return super(cls, StrongHashSpec).__new__(cls, "stronghash", key)
Example #2
Source File: tensor_util.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _FilterInt(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterInt(x) for x in v])
  return None if isinstance(v, compat.integral_types) else _NotNone(v)
Example #3
Source File: sparse_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def sparse_row_envelope(sparse_input, row_axis=0, col_axis=1, name=None):
  """Returns the length of each 'row' in a `SparseTensor`.

  For example, if `sparse_input` has indices `[[0,0], [2, 0], [2, 1], [2, 2]]`
  and shape `[3, 3]`, this function will return `[1, 0, 3]`.

  Args:
    sparse_input: a `SparseTensor` of rank at least 2.
    row_axis: An integer. The axis for the row of the envelope matrix. Default
      is 0.
    col_axis: An integer. The axis for the col of the envelope matrix. Default
      is 1.
    name: A name for the operation (optional).

  Returns:
    A one-dimensional `Tensor` whose entries correspond to the length of each
    row of `SparseTensor`.

  Raises:
    ValueError: If row_axis and col_axis are the same axis or they are not
      integers.
  """
  if not (isinstance(row_axis, compat.integral_types) and
          isinstance(col_axis, compat.integral_types)):
    raise ValueError("`row_axis` and `col_axis` must be integers.")

  if row_axis == col_axis:
    raise ValueError("Row and column can not be the same axis.")

  with ops.name_scope(name, "sparse_row_envelope", [sparse_input]):
    indices = sparse_input.indices
    row_indices = indices[:, row_axis]
    col_indices = indices[:, col_axis]
    num_rows = math_ops.cast(sparse_input.dense_shape[row_axis], dtypes.int32)
    row_envelope = math_ops.unsorted_segment_max(
        col_indices + 1, row_indices, num_rows, name=name)
    zeros = array_ops.zeros_like(row_envelope)
    return array_ops.where(row_envelope > zeros, row_envelope, zeros)
Example #4
Source File: lookup_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __new__(cls, key):
    if len(key) != 2:
      raise ValueError("key must have size 2, got %s." % len(key))

    if not isinstance(key[0], compat.integral_types) or not isinstance(
        key[1], compat.integral_types):
      raise TypeError("Invalid key %s. Must be unsigned integer values." % key)

    return super(cls, StrongHashSpec).__new__(cls, "stronghash", key)
Example #5
Source File: tensor_util.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _FilterInt(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterInt(x) for x in v])
  return None if isinstance(v, compat.integral_types) else _NotNone(v)
Example #6
Source File: lookup_ops.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def __new__(cls, key):
    if len(key) != 2:
      raise ValueError("key must have size 2, got %s." % len(key))

    if not isinstance(key[0], compat.integral_types) or not isinstance(
        key[1], compat.integral_types):
      raise TypeError("Invalid key %s. Must be unsigned integer values." % key)

    return super(cls, StrongHashSpec).__new__(cls, "stronghash", key)
Example #7
Source File: sparse_ops.py    From tf-slim with Apache License 2.0 5 votes vote down vote up 
def sparse_row_envelope(sparse_input, row_axis=0, col_axis=1, name=None):
  """Returns the length of each 'row' in a `SparseTensor`.

  For example, if `sparse_input` has indices `[[0,0], [2, 0], [2, 1], [2, 2]]`
  and shape `[3, 3]`, this function will return `[1, 0, 3]`.

  Args:
    sparse_input: a `SparseTensor` of rank at least 2.
    row_axis: An integer. The axis for the row of the envelope matrix. Default
      is 0.
    col_axis: An integer. The axis for the col of the envelope matrix. Default
      is 1.
    name: A name for the operation (optional).

  Returns:
    A one-dimensional `Tensor` whose entries correspond to the length of each
    row of `SparseTensor`.

  Raises:
    ValueError: If row_axis and col_axis are the same axis or they are not
      integers.
  """
  if not (isinstance(row_axis, compat.integral_types) and
          isinstance(col_axis, compat.integral_types)):
    raise ValueError("`row_axis` and `col_axis` must be integers.")

  if row_axis == col_axis:
    raise ValueError("Row and column can not be the same axis.")

  with ops.name_scope(name, "sparse_row_envelope", [sparse_input]):
    indices = sparse_input.indices
    row_indices = indices[:, row_axis]
    col_indices = indices[:, col_axis]
    num_rows = math_ops.cast(sparse_input.dense_shape[row_axis], dtypes.int32)
    row_envelope = math_ops.unsorted_segment_max(
        col_indices + 1, row_indices, num_rows, name=name)
    zeros = array_ops.zeros_like(row_envelope)
    return array_ops.where(row_envelope > zeros, row_envelope, zeros)
Example #8
Source File: tensor_util.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _FilterInt(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterInt(x) for x in v])
  return None if isinstance(v, compat.integral_types) else _NotNone(v)
Example #9
Source File: lookup_ops.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def __new__(cls, key):
    if len(key) != 2:
      raise ValueError("key must have size 2, got %s." % len(key))

    if not isinstance(key[0], compat.integral_types) or not isinstance(
        key[1], compat.integral_types):
      raise TypeError("Invalid key %s. Must be unsigned integer values." % key)

    return super(cls, StrongHashSpec).__new__(cls, "stronghash", key)
Example #10
Source File: tensor_util.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _FilterInt(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterInt(x) for x in v])
  return None if isinstance(v, (compat.integral_types,
                                tensor_shape.Dimension)) else _NotNone(v)
Example #11
Source File: tensor_util.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _FilterInt(v):
  if isinstance(v, (list, tuple)):
    return _FirstNotNone([_FilterInt(x) for x in v])
  return None if isinstance(v, compat.integral_types) else _NotNone(v)
Example #12
Source File: lookup_ops.py    From keras-lambda with MIT License 5 votes vote down vote up 
def __new__(cls, key):
    if len(key) != 2:
      raise ValueError("key must have size 2, got %s." % len(key))

    if not isinstance(key[0], compat.integral_types) or not isinstance(
        key[1], compat.integral_types):
      raise TypeError("Invalid key %s. Must be unsigned integer values." % key)

    return super(cls, StrongHashSpec).__new__(cls, "stronghash", key)