Python dtypes.DType方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def parameterInfo(self,
                      shape: Tuple[int, ...] = (1,),
                      latentShape: Tuple[int, ...] = ()) -> ParameterInfo:
        """Initializers of the parameters of the distribution.

        Draw random initialization values for each parameter matching the
        provided `shape`, `lantentShape`, and `dtype`. This method has to
        be implemented by concrete distributions to provide reasonable
        random initalizations used during `Distribution.random`.

        Arguments:
            shape: `Tuple[int, ...]` the shape of the distribution.
            latentShape: `Tuple[int, ...]` the latent shape of the
                distribution.
            dtype: `DType` the data type of the distribution.

        Returns:
            `Dict[str, Tensor]` map from parameter names to `Tensor` s
            containing the random initial values for the parameters.
        """
        ... 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def __init__(self, iterator_resource, initializer, output_types,
               output_shapes):
    """Creates a new iterator from the given iterator resource.

    NOTE(mrry): Most users will not call this initializer directly, and will
    instead use `Iterator.from_dataset()` or `Dataset.make_one_shot_iterator()`.

    Args:
      iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
        iterator.
      initializer: A `tf.Operation` that should be run to initialize this
        iterator.
      output_types: A nested structure of `tf.DType` objects corresponding to
        each component of an element of this iterator.
      output_shapes: A nested structure of `tf.TensorShape` objects
        corresponding to each component of an element of this dataset.
    """
    self._iterator_resource = iterator_resource
    self._initializer = initializer
    self._output_types = output_types
    self._output_shapes = output_shapes 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def __init__(self,
                 name: str = "NA",
                 dtype: tf.DType = tf.float32,
                 drawType: DrawType = DrawType.SAMPLE,
                 updateType: UpdateType = UpdateType.ALL,
                 persistent: bool = True) -> None:
        self.__name = name
        self.__dtype = dtype
        self.__drawType = drawType
        self.__updateType = updateType
        self.__persistent = persistent 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def dtype(self) -> DType:
        """The dtype of the distribution."""
        return(self.__dtype) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def __init__(self, *input_types, **kwargs):
    """Create a `Defun` decorator.

    Args:
      *input_types: A list of `tf.DType`
      **kwargs: Optional keyword arguments, including
         func_name - (optional).  A python string, the name to use to
           declare this `Function` in the graph.

         grad_func - (optional).  A function implementing the gradient
           of the function-to-register.  This is either a
           `_DefinedFunction` or a `Declare` object. The gradient
           function must satisify the criterion defined in
           function.proto:GradientDef.

         python_grad_func - (optional).  A function implementing the
           gradient of the function python-side. This function must
           take the current op and the gradients w.r.t. its outputs,
           and return the gradients w.r.t. the inputs. That is it must
           implement the interface expected by `tf.RegisterGradient`).
           This will be called by tf.gradients to add the gradient ops
           to the graph. At most one of grad_func and python_grad_func
           can be specified.

         out_names = (optional). A list of strings, one per output
           tensor.

         shape_func - (optional). A function taking the op and returning a list
           of static shapes to set for the function's outputs.
    """
    self._input_types = input_types
    self._func_name = kwargs.pop("func_name", None)
    self._grad_func = kwargs.pop("grad_func", None)
    self._python_grad_func = kwargs.pop("python_grad_func", None)
    self._out_names = kwargs.pop("out_names", None)
    self._extra_kwargs = kwargs 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def output_types(self):
    """Returns the type of each component of an element of this iterator.

    Returns:
      A nested structure of `tf.DType` objects corresponding to each component
      of an element of this iterator.
    """
    return self._output_types 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def instantiate(self, input_types):
    """Instantiate this function given input argument types.

    Args:
      input_types: A list of data types for the inputs.

    Returns:
      _DefinedFunction for the given input types.

    """
    # Stringify the type list.
    key = _type_list_to_str(input_types)
    defined = self._overload.get(key)
    if not defined:
      # If not defined yet, define the function given the input types.
      name = self._func_name
      if name is not None:
        name = "_".join([name, key])
      defined = _DefinedFunction(self._func, self._argnames, input_types, name,
                                 None, self._python_grad_func,
                                 out_names=self._out_names,
                                 **self._extra_kwargs)
      _ = defined.name  # Fully instantiate the function definition.
      if self._grad_func:
        # If _grad_func is given, it is another
        # _OverloadedFunction. We need to instantiate it with the
        # right input types.
        output_types = [
            dtypes.DType(_.type)
            for _ in defined.definition.signature.output_arg
        ]
        # pylint: disable=protected-access
        defined._grad_func = self._grad_func.instantiate(input_types +
                                                         output_types)
        # pylint: enable=protected-access
      self._overload[key] = defined
    return defined 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def __init__(self, *input_types, **kwargs):
    """Create a `Defun` decorator.

    Args:
      *input_types: A list of `tf.DType`
      **kwargs: Optional keyword arguments, including
         func_name - (optional).  A python string, the name to use to
           declare this `Function` in the graph.

         grad_func - (optional).  A function implementing the gradient
           of the function-to-register.  This is either a
           `_DefinedFunction` or a `Declare` object. The gradient
           function must satisify the criterion defined in
           function.proto:GradientDef.

         python_grad_func - (optional).  A function implementing the
           gradient of the function python-side. This function must
           take the current op and the gradients w.r.t. its outputs,
           and return the gradients w.r.t. the inputs. That is it must
           implement the interface expected by `tf.RegisterGradient`).
           This will be called by tf.gradients to add the gradient ops
           to the graph. At most one of grad_func and python_grad_func
           can be specified.

         out_names = (optional). A list of strings, one per output
           tensor.
    """
    self._input_types = input_types
    self._func_name = kwargs.pop("func_name", None)
    self._grad_func = kwargs.pop("grad_func", None)
    self._python_grad_func = kwargs.pop("python_grad_func", None)
    self._out_names = kwargs.pop("out_names", None)
    self._extra_kwargs = kwargs 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def instantiate(self, input_types):
    """Instantiate this function given input argument types.

    Args:
      input_types: A list of data types for the inputs.

    Returns:
      _DefinedFunction for the given input types.

    """
    # Stringify the type list.
    key = _type_list_to_str(input_types)
    defined = self._overload.get(key)
    if not defined:
      # If not defined yet, define the function given the input types.
      name = self._func_name
      if name is not None:
        name = "_".join([name, key])
      defined = _DefinedFunction(self._func, self._argnames, input_types, name,
                                 None, self._python_grad_func,
                                 **self._extra_kwargs)
      _ = defined.name  # Fully instantiate the function definition.
      if self._grad_func:
        # If _grad_func is given, it is another
        # _OverloadedFunction. We need to instantiate it with the
        # right input types.
        output_types = [
            dtypes.DType(_.type)
            for _ in defined.definition.signature.output_arg
        ]
        # pylint: disable=protected-access
        defined._grad_func = self._grad_func.instantiate(input_types +
                                                         output_types)
        # pylint: enable=protected-access
      self._overload[key] = defined
    return defined 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def __init__(self, *input_types, **kwargs):
    """Create a `Defun` decorator.

    Args:
      *input_types: A list of `tf.DType`
      **kwargs: Optional keyword arguments, including
         func_name - (optional).  A python string, the name to use to
           declare this `Function` in the graph.

         grad_func - (optional).  A function implementing the gradient
           of the function-to-register.  This is either a
           `_DefinedFunction` or a `Declare` object. The gradient
           function must satisify the criterion defined in
           function.proto:GradientDef.

         python_grad_func - (optional).  A function implementing the
           gradient of the function python-side. This function must
           take the current op and the gradients w.r.t. its outputs,
           and return the gradients w.r.t. the inputs. That is it must
           implement the interface expected by `tf.RegisterGradient`).
           This will be called by tf.gradients to add the gradient ops
           to the graph. At most one of grad_func and python_grad_func
           can be specified.
    """
    self._input_types = input_types
    self._func_name = kwargs.pop("func_name", None)
    self._grad_func = kwargs.pop("grad_func", None)
    self._python_grad_func = kwargs.pop("python_grad_func", None)
    self._extra_kwargs = kwargs 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def make_tf_graph(input_types):
    """
    Decorator to help construct TensorFlow 1.x model.

    Parameters
    ----------
    input_types: list of tuple
        List of input types. E.g. [(3, 224, 224, tf.int32)] represent 1 input,
        with shape (3, 224, 224), and the expected data type is tf.int32. The
        dtype is optional, in case it's missing, tf.float32 will be used.

    Returns
    -------
    tf.Graph, list of str, list of str
    """

    def wrapper(ops):
        with tf.Graph().as_default() as model:
            inputs = []
            for input_type in input_types:
                input_type = tuple(input_type)
                if len(input_type) > 0 and isinstance(input_type[-1], dtypes.DType):
                    shape, dtype = input_type[:-1], input_type[-1]
                else:
                    shape, dtype = input_type, tf.float32
                inputs.append(tf.placeholder(shape=shape, dtype=dtype))

            outputs = ops(*inputs)
        return model, inputs, outputs

    return wrapper 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def _get_types_from_tensor_info_dict(tensor_info_dict):
  """Returns a map of keys to DType objects.

  Args:
    tensor_info_dict: map with TensorInfo proto as values.

  Returns:
    Map with corresponding DType objects as values.
  """
  return {
      key: dtypes.DType(tensor_info.dtype)
      for key, tensor_info in tensor_info_dict.items()
  } 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def get_signature_def_input_types(signature):
  """Returns map of output names to their types.

  Args:
    signature: SignatureDef proto.

  Returns:
    Map from string to DType objects.
  """
  return _get_types_from_tensor_info_dict(signature.inputs) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def get_signature_def_output_types(signature):
  """Returns map of output names to their types.

  Args:
    signature: SignatureDef proto.

  Returns:
    Map from string to DType objects.
  """
  return _get_types_from_tensor_info_dict(signature.outputs) 

# 需要導入模塊: from tensorflow.python.framework import dtypes [as 別名]
# 或者: from tensorflow.python.framework.dtypes import DType [as 別名]
def get_variable_to_dtype_map(self):
      from tensorflow.python.framework import dtypes
      return {name: dtypes.DType(type_enum)
              for name, type_enum in self._GetVariableToDataTypeMap().items()}