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/TensorFlow Python

# tf.IndexedSlices

## Class `IndexedSlices`

Defined in `tensorflow/python/framework/ops.py`.

See the guide: Variables > Sparse Variable Updates

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

```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.

## Properties

### `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.

### `values`

A `Tensor` containing the values of the slices.

## Methods

### `__init__`

```__init__(
values,
indices,
dense_shape=None
)
```

Creates an `IndexedSlices`.

### `__neg__`

```__neg__()
```