tf.IndexedSlices

A sparse representation of a set of tensor slices at given indices.

View aliases

Compat aliases for migration

tf.compat.v1.IndexedSlices

tf.IndexedSlices(
    values, indices, dense_shape=None
)

Used in the notebooks

Used in the tutorials
Client-efficient large-model federated learning via `federated_select` and sparse aggregation

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

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).

v = tf.Variable([[0.,1, 2], [2, 3, 4], [4, 5, 6], [6, 7, 8]])
with tf.GradientTape() as tape:
  r = tf.gather(v, [1,3])
index_slices = tape.gradient(r,v)
index_slices
<...IndexedSlices object ...>
index_slices.indices.numpy()
array([1, 3], dtype=int32)
index_slices.values.numpy()
array([[1., 1., 1.],
       [1., 1., 1.]], dtype=float32)

Contrast this representation with tf.sparse.SparseTensor, which uses multi-dimensional indices and scalar values.

Attributes
`dense_shape`	A 1-D `Tensor` containing the shape of the corresponding dense tensor.
`device`	The name of the device on which `values` will be produced, or `None`.
`dtype`	The `DType` of elements in this tensor.
`graph`	The `Graph` that contains the values, indices, and shape tensors.
`indices`	A 1-D `Tensor` containing the indices of the slices.
`name`	The name of this `IndexedSlices`.
`op`	The `Operation` that produces `values` as an output.
`shape`	Gets the `tf.TensorShape` representing the shape of the dense tensor.
`values`	A `Tensor` containing the values of the slices.

Methods

`consumers`

View source

consumers()

`neg`