tf.IndexedSlices学习

@tf_export("IndexedSlices")
class IndexedSlices(_TensorLike):
  """A sparse representation of a set of tensor slices at given indices.

  This class is a simple wrapper for a pair of `Tensor` objects:

  * `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.
  * `indices`: A 1-D integer `Tensor` with shape `[D0]`.

  An `IndexedSlices` is typically used to represent a subset of a larger
  tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.
  The values in `indices` are the indices in the first dimension of
  the slices that have been extracted from the larger tensor.

  The dense tensor `dense` represented by an `IndexedSlices` `slices` has

  ```python
  dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]
  ```

  The `IndexedSlices` class is used principally in the definition of
  gradients for operations that have sparse gradients
  (e.g. @{tf.gather}).

  Contrast this representation with
  @{tf.SparseTensor},
  which uses multi-dimensional indices and scalar values.
  """

  def __init__(self, values, indices, dense_shape=None):
    """Creates an `IndexedSlices`."""
    _get_graph_from_inputs([values, indices, dense_shape])
    self._values = values
    self._indices = indices
    self._dense_shape = dense_shape

  @property
  def values(self):
    """A `Tensor` containing the values of the slices."""
    return self._values

  @property
  def indices(self):
    """A 1-D `Tensor` containing the indices of the slices."""
    return self._indices

  @property
  def dense_shape(self):
    """A 1-D `Tensor` containing the shape of the corresponding dense tensor."""
    return self._dense_shape

  @property
  def name(self):
    """The name of this `IndexedSlices`."""
    return self.values.name

  @property
  def device(self):
    """The name of the device on which `values` will be produced, or `None`."""
    return self.values.device

  @property
  def op(self):
    """The `Operation` that produces `values` as an output."""
    return self.values.op

  @property
  def dtype(self):
    """The `DType` of elements in this tensor."""
    return self.values.dtype

  @property
  def graph(self):
    """The `Graph` that contains the values, indices, and shape tensors."""
    return self._values.graph

  def __str__(self):
    return "IndexedSlices(indices=%s, values=%s%s)" % (
        self._indices, self._values, (", dense_shape=%s" % self._dense_shape)
        if self._dense_shape is not None else "")

  def __neg__(self):
    return IndexedSlices(-self.values, self.indices, self.dense_shape)