Python deprecation.deprecated方法代码示例

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def create_global_step(graph=None):
  """Create global step tensor in graph.

  This API is deprecated. Use core framework training version instead.

  Args:
    graph: The graph in which to create the global step tensor. If missing, use
      default graph.

  Returns:
    Global step tensor.

  Raises:
    ValueError: if global step tensor is already defined.
  """
  return training_util.create_global_step(graph) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def test_static_fn_with_one_line_doc(self, mock_warning):
    date = "2016-07-04"
    instructions = "This is how you update..."

    @deprecation.deprecated(date, instructions)
    def _fn(arg0, arg1):
      """fn doc."""
      return arg0 + arg1

    # Assert function docs are properly updated.
    self.assertEqual("_fn", _fn.__name__)
    self.assertEqual(
        "fn doc. (deprecated)"
        "\n"
        "\nTHIS FUNCTION IS DEPRECATED. It will be removed after %s."
        "\nInstructions for updating:\n%s" % (date, instructions),
        _fn.__doc__)

    # Assert calling new fn issues log warning.
    self.assertEqual(3, _fn(1, 2))
    self.assertEqual(1, mock_warning.call_count)
    (args, _) = mock_warning.call_args
    self.assertRegexpMatches(args[0], r"deprecated and will be removed after")
    self._assert_subset(set([date, instructions]), set(args[1:])) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def test_static_fn_no_doc(self, mock_warning):
    date = "2016-07-04"
    instructions = "This is how you update..."

    @deprecation.deprecated(date, instructions)
    def _fn(arg0, arg1):
      return arg0 + arg1

    # Assert function docs are properly updated.
    self.assertEqual("_fn", _fn.__name__)
    self.assertEqual(
        "DEPRECATED FUNCTION"
        "\n"
        "\nTHIS FUNCTION IS DEPRECATED. It will be removed after %s."
        "\nInstructions for updating:"
        "\n%s" % (date, instructions),
        _fn.__doc__)

    # Assert calling new fn issues log warning.
    self.assertEqual(3, _fn(1, 2))
    self.assertEqual(1, mock_warning.call_count)
    (args, _) = mock_warning.call_args
    self.assertRegexpMatches(args[0], r"deprecated and will be removed after")
    self._assert_subset(set([date, instructions]), set(args[1:])) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def test_varargs(self, mock_warning):
    date = "2016-07-04"
    instructions = "This is how you update..."

    @deprecation.deprecated_args(date, instructions, "deprecated")
    def _fn(arg0, arg1, *deprecated):
      return arg0 + arg1 if deprecated else arg1 + arg0

    # Assert calls without the deprecated argument log nothing.
    self.assertEqual(3, _fn(1, 2))
    self.assertEqual(0, mock_warning.call_count)

    # Assert calls with the deprecated argument log a warning.
    self.assertEqual(3, _fn(1, 2, True, False))
    self.assertEqual(1, mock_warning.call_count)
    (args, _) = mock_warning.call_args
    self.assertRegexpMatches(args[0], r"deprecated and will be removed after")
    self._assert_subset(set([date, instructions]), set(args[1:])) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def test_kwargs(self, mock_warning):
    date = "2016-07-04"
    instructions = "This is how you update..."

    @deprecation.deprecated_args(date, instructions, "deprecated")
    def _fn(arg0, arg1, **deprecated):
      return arg0 + arg1 if deprecated else arg1 + arg0

    # Assert calls without the deprecated argument log nothing.
    self.assertEqual(3, _fn(1, 2))
    self.assertEqual(0, mock_warning.call_count)

    # Assert calls with the deprecated argument log a warning.
    self.assertEqual(3, _fn(1, 2, a=True, b=False))
    self.assertEqual(1, mock_warning.call_count)
    (args, _) = mock_warning.call_args
    self.assertRegexpMatches(args[0], r"deprecated and will be removed after")
    self._assert_subset(set([date, instructions]), set(args[1:])) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def test_deprecated_illegal_args(self):
    instructions = "This is how you update..."
    with self.assertRaisesRegexp(ValueError, "date"):
      deprecation.deprecated_arg_values(
          None, instructions, deprecated=True)
    with self.assertRaisesRegexp(ValueError, "date"):
      deprecation.deprecated_arg_values(
          "", instructions, deprecated=True)
    with self.assertRaisesRegexp(ValueError, "YYYY-MM-DD"):
      deprecation.deprecated_arg_values(
          "07-04-2016", instructions, deprecated=True)
    date = "2016-07-04"
    with self.assertRaisesRegexp(ValueError, "instructions"):
      deprecation.deprecated_arg_values(
          date, None, deprecated=True)
    with self.assertRaisesRegexp(ValueError, "instructions"):
      deprecation.deprecated_arg_values(
          date, "", deprecated=True)
    with self.assertRaisesRegexp(ValueError, "argument", deprecated=True):
      deprecation.deprecated_arg_values(
          date, instructions) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def merge_all_summaries(key=ops.GraphKeys.SUMMARIES):
  """Merges all summaries collected in the default graph.

  This op is deprecated. Please switch to tf.summary.merge_all, which has
  identical behavior.

  Args:
    key: `GraphKey` used to collect the summaries.  Defaults to
      `GraphKeys.SUMMARIES`.

  Returns:
    If no summaries were collected, returns None.  Otherwise returns a scalar
    `Tensor` of type `string` containing the serialized `Summary` protocol
    buffer resulting from the merging.
  """
  summary_ops = ops.get_collection(key)
  if not summary_ops:
    return None
  else:
    return merge_summary(summary_ops) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def histogram_summary(tag, values, collections=None, name=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with a histogram.

  This ops is deprecated. Please switch to tf.summary.histogram.

  For an explanation of why this op was deprecated, and information on how to
  migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py)

  The generated
  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
  has one summary value containing a histogram for `values`.

  This op reports an `InvalidArgument` error if any value is not finite.

  Args:
    tag: A `string` `Tensor`. 0-D.  Tag to use for the summary value.
    values: A real numeric `Tensor`. Any shape. Values to use to
      build the histogram.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "HistogramSummary", [tag, values]) as scope:
    val = gen_logging_ops._histogram_summary(
        tag=tag, values=values, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def merge_summary(inputs, collections=None, name=None):
  # pylint: disable=line-too-long
  """Merges summaries.

  This op is deprecated. Please switch to tf.summary.merge, which has identical
  behavior.

  This op creates a
  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
  protocol buffer that contains the union of all the values in the input
  summaries.

  When the Op is run, it reports an `InvalidArgument` error if multiple values
  in the summaries to merge use the same tag.

  Args:
    inputs: A list of `string` `Tensor` objects containing serialized `Summary`
      protocol buffers.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer resulting from the merging.
  """
  with ops.name_scope(name, "MergeSummary", inputs):
    val = gen_logging_ops._merge_summary(inputs=inputs, name=name)
    _Collect(val, collections, [])
  return val 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def scalar_summary(tags, values, collections=None, name=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with scalar values.

  This ops is deprecated. Please switch to tf.summary.scalar.
  For an explanation of why this op was deprecated, and information on how to
  migrate, look ['here'](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/deprecated/__init__.py)

  The input `tags` and `values` must have the same shape.  The generated
  summary has a summary value for each tag-value pair in `tags` and `values`.

  Args:
    tags: A `string` `Tensor`.  Tags for the summaries.
    values: A real numeric Tensor.  Values for the summaries.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "ScalarSummary", [tags, values]) as scope:
    val = gen_logging_ops._scalar_summary(tags=tags, values=values, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def reduce_prod(input_tensor,
                axis=None,
                keep_dims=False,
                name=None,
                reduction_indices=None):
  """Computes the product of elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor.

  @compatibility(numpy)
  Equivalent to np.prod
  @end_compatibility
  """
  return gen_math_ops._prod(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      name=name) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def reduce_max(input_tensor,
               axis=None,
               keep_dims=False,
               name=None,
               reduction_indices=None):
  """Computes the maximum of elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor.

  @compatibility(numpy)
  Equivalent to np.max
  @end_compatibility
  """
  return gen_math_ops._max(
      input_tensor,
      _ReductionDims(input_tensor, axis, reduction_indices),
      keep_dims,
      name=name) 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def histogram_summary(tag, values, collections=None, name=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with a histogram.

  This ops is deprecated. Please switch to tf.summary.histogram.

  For an explanation of why this op was deprecated, and information on how to
  migrate, look ['here'](https://www.tensorflow.org/code/tensorflow/contrib/deprecated/__init__.py)

  The generated
  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
  has one summary value containing a histogram for `values`.

  This op reports an `InvalidArgument` error if any value is not finite.

  Args:
    tag: A `string` `Tensor`. 0-D.  Tag to use for the summary value.
    values: A real numeric `Tensor`. Any shape. Values to use to
      build the histogram.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "HistogramSummary", [tag, values]) as scope:
    val = gen_logging_ops._histogram_summary(
        tag=tag, values=values, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def scalar_summary(tags, values, collections=None, name=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with scalar values.

  This ops is deprecated. Please switch to tf.summary.scalar.
  For an explanation of why this op was deprecated, and information on how to
  migrate, look ['here'](https://www.tensorflow.org/code/tensorflow/contrib/deprecated/__init__.py)

  The input `tags` and `values` must have the same shape.  The generated
  summary has a summary value for each tag-value pair in `tags` and `values`.

  Args:
    tags: A `string` `Tensor`.  Tags for the summaries.
    values: A real numeric Tensor.  Values for the summaries.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    name: A name for the operation (optional).

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  with ops.name_scope(name, "ScalarSummary", [tags, values]) as scope:
    val = gen_logging_ops._scalar_summary(tags=tags, values=values, name=scope)
    _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
  return val 

# 需要导入模块: from tensorflow.python.util import deprecation [as 别名]
# 或者: from tensorflow.python.util.deprecation import deprecated [as 别名]
def test_static_fn_with_doc(self, mock_warning):
    date = "2016-07-04"
    instructions = "This is how you update..."

    @deprecation.deprecated(date, instructions)
    def _fn(arg0, arg1):
      """fn doc.

      Args:
        arg0: Arg 0.
        arg1: Arg 1.

      Returns:
        Sum of args.
      """
      return arg0 + arg1

    # Assert function docs are properly updated.
    self.assertEqual("_fn", _fn.__name__)
    self.assertEqual(
        "fn doc. (deprecated)"
        "\n"
        "\nTHIS FUNCTION IS DEPRECATED. It will be removed after %s."
        "\nInstructions for updating:\n%s"
        "\n"
        "\n      Args:"
        "\n        arg0: Arg 0."
        "\n        arg1: Arg 1."
        "\n"
        "\n      Returns:"
        "\n        Sum of args."
        "\n      " % (date, instructions),
        _fn.__doc__)

    # Assert calling new fn issues log warning.
    self.assertEqual(3, _fn(1, 2))
    self.assertEqual(1, mock_warning.call_count)
    (args, _) = mock_warning.call_args
    self.assertRegexpMatches(args[0], r"deprecated and will be removed after")
    self._assert_subset(set([date, instructions]), set(args[1:]))