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Update Gather-7 specification #5441

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merged 6 commits into from
May 6, 2021
Merged

Update Gather-7 specification #5441

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6fc30b5
Allow nagative values for batch_dims
Apr 28, 2021
1a80cb0
Merge remote-tracking branch 'upstream/master' into doc/achetver/upda…
Apr 28, 2021
ac04baa
Update formula
Apr 28, 2021
700b8c1
Update spec according to comments
Apr 29, 2021
b2e4a0a
Merge remote-tracking branch 'upstream/master' into doc/achetver/upda…
Apr 29, 2021
00a3c80
clarified cases when batch_dims and axis less than zero and enhanced …
pavel-esir May 4, 2021
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31 changes: 25 additions & 6 deletions docs/ops/movement/Gather_7.md
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Expand Up @@ -10,18 +10,19 @@ TensorFlow\* [Gather](https://www.tensorflow.org/api_docs/python/tf/gather) oper

**Detailed description**

output[p_0, p_1, ..., p_{axis-1}, p_axis, ..., p_{axis + k}, ...] =
data[p_0, p_1, ..., p_{axis-1}, indices[p_0, p_1, ..., p_{b-1}, p_b, ..., p_{axis}, j], ...]
output[p_0, p_1, ..., p_{axis-1}, i_b, ..., i_{M-1}, p_{axis+1}, ..., p_{N-1}] =
data[p_0, p_1, ..., p_{axis-1}, indices[p_0, p_1, ..., p_{b-1}, i_b, ..., i_{M-1}], p_{axis+1}, ..., p_{N-1}]

Where `data`, `indices` and `axis` are tensors from first, second and third inputs correspondingly, and `b` is
the number of batch dimensions.
Where `data`, `indices` and `axis` are tensors from first, second and third inputs correspondingly, `b` is
the number of batch dimensions. `N` and `M` are numbers of dimensions of `data` and `indices` tensors, respectively.

**Attributes**:
* *batch_dims*
* **Description**: *batch_dims* (also denoted as `b`) is a leading number of dimensions of `data` tensor and `indices`
representing the batches, and *Gather* starts to gather from the `b` dimension. It requires the first `b`
dimensions in `data` and `indices` tensors to be equal.
* **Range of values**: `[0; min(data.rank, indices.rank))` and `batch_dims <= axis`
dimensions in `data` and `indices` tensors to be equal. If *batch_dims* is less than zero used normalized value
`batch_dims = indices.rank + batch_dims`.
* **Range of values**: `[-min(data.rank, indices.rank); min(data.rank, indices.rank))` and `batch_dims <= axis`
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* **Type**: *T_AXIS*
* **Default value**: 0
* **Required**: *no*
Expand Down Expand Up @@ -112,6 +113,24 @@ output = [[[[ 5, 6, 7, 8],
output_shape = (2, 1, 3, 4)
```

Example 5 with negative *batch_dims* value:
```
batch_dims = -1 <-- normalized value will be indices.rank + batch_dims = 2 - 1 = 1
axis = 1

indices = [[0, 0, 4], <-- this is applied to the first batch
[4, 0, 0]] <-- this is applied to the second batch
indices_shape = (2, 3)

data = [[1, 2, 3, 4, 5], <-- the first batch
[6, 7, 8, 9, 10]] <-- the second batch
data_shape = (2, 5)

output = [[ 1, 1, 5],
[10, 6, 6]]
output_shape = (2, 3)
```

**Inputs**

* **1**: `data` tensor of type *T* with arbitrary data. **Required**.
Expand Down