Python ops.Graph方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def __init__(self, target='', graph=None, config=None):
    """Creates a new TensorFlow session.

    If no `graph` argument is specified when constructing the session,
    the default graph will be launched in the session. If you are
    using more than one graph (created with `tf.Graph()` in the same
    process, you will have to use different sessions for each graph,
    but each graph can be used in multiple sessions. In this case, it
    is often clearer to pass the graph to be launched explicitly to
    the session constructor.

    Args:
      target: (Optional.) The execution engine to connect to.
        Defaults to using an in-process engine. See
        @{$distributed$Distributed TensorFlow}
        for more examples.
      graph: (Optional.) The `Graph` to be launched (described above).
      config: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
        protocol buffer with configuration options for the session.

    """
    super(Session, self).__init__(target, graph, config=config)
    # NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.
    self._default_graph_context_manager = None
    self._default_session_context_manager = None 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def get_tensors(graph):
  """get all the tensors which are input or output of an op in the graph.

  Args:
    graph: a `tf.Graph`.
  Returns:
    A list of `tf.Tensor`.
  Raises:
    TypeError: if graph is not a `tf.Graph`.
  """
  if not isinstance(graph, tf_ops.Graph):
    raise TypeError("Expected a graph, got: {}".format(type(graph)))
  ts = []
  for op in graph.get_operations():
    ts += op.outputs
  return ts 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def __init__(self, graph):
    """Create a dictionary of control-output dependencies.

    Args:
      graph: a `tf.Graph`.
    Returns:
      A dictionary where a key is a `tf.Operation` instance and the
         corresponding value is a list of all the ops which have the key
         as one of their control-input dependencies.
    Raises:
      TypeError: graph is not a `tf.Graph`.
    """
    if not isinstance(graph, tf_ops.Graph):
      raise TypeError("Expected a tf.Graph, got: {}".format(type(graph)))
    self._control_outputs = {}
    self._graph = graph
    self._version = None
    self._build() 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def make_placeholder_from_tensor(t, scope=None):
  """Create a `tf.placeholder` for the Graph Editor.

  Note that the correct graph scope must be set by the calling function.

  Args:
    t: a `tf.Tensor` whose name will be used to create the placeholder
      (see function placeholder_name).
    scope: absolute scope within which to create the placeholder. None
      means that the scope of `t` is preserved. `""` means the root scope.
  Returns:
    A newly created `tf.placeholder`.
  Raises:
    TypeError: if `t` is not `None` or a `tf.Tensor`.
  """
  return tf_array_ops.placeholder(
      dtype=t.dtype, shape=t.get_shape(), name=placeholder_name(
          t, scope=scope)) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def __copy__(self):
    """Create a copy of this subgraph.

    Note that this class is a "view", copying it only create another view and
    does not copy the underlying part of the `tf.Graph`.

    Returns:
      A new identical instance of the original subgraph view.
    """
    cls = self.__class__
    result = cls.__new__(cls)
    for k, v in iteritems(self.__dict__):
      if k == "_graph":
        setattr(result, k, v)
      else:
        setattr(result, k, list(v))  # copy the list
    return result 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def remap_inputs(self, new_input_indices):
    """Remap the inputs of the subgraph.

    If the inputs of the original subgraph are [t0, t1, t2], remapping to [2,0]
    will create a new instance whose inputs is [t2, t0].

    Note that this is only modifying the view: the underlying `tf.Graph` is not
    affected.

    Args:
      new_input_indices: an iterable of integers or tf.Tensors
        representing a mapping between the old inputs and the new ones.
        Integers must be positive and smaller than the number of old inputs.
        tf.Tensors must belong to the old list of inputs.
        This mapping can be under-complete and must be without repetitions.
    Returns:
      A new modified instance of the original subgraph view with remapped
        inputs.
    """
    res = self.copy()
    res._remap_inputs(new_input_indices)  # pylint: disable=protected-access
    return res 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def remap_outputs(self, new_output_indices):
    """Remap the output of the subgraph.

    If the output of the original subgraph are [t0, t1, t2], remapping to
    [1,1,0] will create a new instance whose outputs is [t1, t1, t0].

    Note that this is only modifying the view: the underlying tf.Graph is not
    affected.

    Args:
      new_output_indices: an iterable of integers or tf.Tensors
        representing a mapping between the old outputs and the new ones.
        Integers must be positive and smaller than the number of old outputs.
        tf.Tensors must belong to the old list of outputs.
        This mapping can be under-complete and can have repetitions.
    Returns:
      A new modified instance of the original subgraph view with remapped
        outputs.
    """
    res = copy.copy(self)
    res._remap_outputs(new_output_indices)  # pylint: disable=protected-access
    return res 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def _check_graph(sgv, graph):
  """Check if sgv belongs to the given graph.

  Args:
    sgv: a SubGraphView.
    graph: a graph or None.
  Returns:
    The SubGraphView sgv.
  Raises:
    TypeError: if sgv is not a SubGraphView or if graph is not None and not
      a tf.Graph.
    ValueError: if the graph of sgv and the given graph are not None and
      different.
  """
  if not isinstance(sgv, SubGraphView):
    raise TypeError("Expected a SubGraphView, got: {}".format(type(graph)))
  if graph is None or not sgv.graph:
    return sgv
  if not isinstance(graph, tf_ops.Graph):
    raise TypeError("Expected a tf.Graph, got: {}".format(type(graph)))
  if sgv.graph is not graph:
    raise ValueError("Graph mismatch.")
  return sgv 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def infer_real_valued_columns_from_input_fn(input_fn):
  """Creates `FeatureColumn` objects for inputs defined by `input_fn`.

  This interprets all inputs as dense, fixed-length float values. This creates
  a local graph in which it calls `input_fn` to build the tensors, then discards
  it.

  Args:
    input_fn: Input function returning a tuple of:
        features - Dictionary of string feature name to `Tensor` or `Tensor`.
        labels - `Tensor` of label values.

  Returns:
    List of `FeatureColumn` objects.
  """
  with ops.Graph().as_default():
    features, _ = input_fn()
    return layers.infer_real_valued_columns(features) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def test_det(self):
    self._skip_if_tests_to_skip_contains("det")
    for use_placeholder in False, True:
      for shape in self._shapes_to_test:
        for dtype in self._dtypes_to_test:
          if dtype.is_complex:
            self.skipTest(
                "tf.matrix_determinant does not work with complex, so this "
                "test is being skipped.")
          with self.test_session(graph=ops.Graph()) as sess:
            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
                shape, dtype, use_placeholder=use_placeholder)
            op_det = operator.determinant()
            if not use_placeholder:
              self.assertAllEqual(shape[:-2], op_det.get_shape())
            op_det_v, mat_det_v = sess.run(
                [op_det, linalg_ops.matrix_determinant(mat)],
                feed_dict=feed_dict)
            self.assertAC(op_det_v, mat_det_v) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def test_log_abs_det(self):
    self._skip_if_tests_to_skip_contains("log_abs_det")
    for use_placeholder in False, True:
      for shape in self._shapes_to_test:
        for dtype in self._dtypes_to_test:
          if dtype.is_complex:
            self.skipTest(
                "tf.matrix_determinant does not work with complex, so this "
                "test is being skipped.")
          with self.test_session(graph=ops.Graph()) as sess:
            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
                shape, dtype, use_placeholder=use_placeholder)
            op_log_abs_det = operator.log_abs_determinant()
            mat_log_abs_det = math_ops.log(
                math_ops.abs(linalg_ops.matrix_determinant(mat)))
            if not use_placeholder:
              self.assertAllEqual(shape[:-2], op_log_abs_det.get_shape())
            op_log_abs_det_v, mat_log_abs_det_v = sess.run(
                [op_log_abs_det, mat_log_abs_det],
                feed_dict=feed_dict)
            self.assertAC(op_log_abs_det_v, mat_log_abs_det_v) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def test_add_to_tensor(self):
    self._skip_if_tests_to_skip_contains("add_to_tensor")
    for use_placeholder in False, True:
      for shape in self._shapes_to_test:
        for dtype in self._dtypes_to_test:
          with self.test_session(graph=ops.Graph()) as sess:
            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
                shape, dtype, use_placeholder=use_placeholder)
            op_plus_2mat = operator.add_to_tensor(2 * mat)

            if not use_placeholder:
              self.assertAllEqual(shape, op_plus_2mat.get_shape())

            op_plus_2mat_v, mat_v = sess.run([op_plus_2mat, mat],
                                             feed_dict=feed_dict)

            self.assertAC(op_plus_2mat_v, 3 * mat_v) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def test_diag_part(self):
    self._skip_if_tests_to_skip_contains("diag_part")
    for use_placeholder in False, True:
      for shape in self._shapes_to_test:
        for dtype in self._dtypes_to_test:
          with self.test_session(graph=ops.Graph()) as sess:
            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
                shape, dtype, use_placeholder=use_placeholder)
            op_diag_part = operator.diag_part()
            mat_diag_part = array_ops.matrix_diag_part(mat)

            if not use_placeholder:
              self.assertAllEqual(
                  mat_diag_part.get_shape(), op_diag_part.get_shape())

            op_diag_part_, mat_diag_part_ = sess.run(
                [op_diag_part, mat_diag_part], feed_dict=feed_dict)

            self.assertAC(op_diag_part_, mat_diag_part_) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def testSequence(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_sequence")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Expect an assertion error since add_meta_graph_and_variables() should be
    # invoked before any add_meta_graph() calls.
    with self.test_session(graph=ops.Graph()) as sess:
      self.assertRaises(AssertionError, builder.add_meta_graph, ["foo"])

    # Expect an assertion error for multiple calls of
    # add_meta_graph_and_variables() since weights should be saved exactly once.
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["bar"])
      self.assertRaises(AssertionError, builder.add_meta_graph_and_variables,
                        sess, ["baz"]) 

# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Graph [as 別名]
def testNoOverwrite(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_no_overwrite")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Graph with a single variable. SavedModel invoked to:
    # - add with weights.
    with self.test_session(graph=ops.Graph()) as sess:
      self._init_and_validate_variable(sess, "v", 42)
      builder.add_meta_graph_and_variables(sess, ["foo"])

    # Save the SavedModel to disk in text format.
    builder.save(as_text=True)

    # Restore the graph with tag "foo", whose variables were saved.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, ["foo"], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())

    # An attempt to create another builder with the same export directory should
    # result in an assertion error.
    self.assertRaises(AssertionError, saved_model_builder.SavedModelBuilder,
                      export_dir)