Generally `tf.gather`

gives you access to elements in the first dimension of a tensor (e.g. rows 1, 3 and 7 in a 2-dimensional Tensor). If you need access to any other dimension than the first one, or if you don't need the whole slice, but e.g. only the 5th entry in the 1st, 3rd and 7th row, you are better off using `tf.gather_nd`

(see upcoming example for this).

`tf.gather`

arguments:

`params`

: A tensor you want to extract values from.`indices`

: A tensor specifying the indices pointing into`params`

Refer to the tf.gather(params, indices) documentation for detailed information.

We want to extract the 1st and 4th row in a 2-dimensional tensor.

```
# data is [[0, 1, 2, 3, 4, 5],
# [6, 7, 8, 9, 10, 11],
# ...
# [24, 25, 26, 27, 28, 29]]
data = np.reshape(np.arange(30), [5, 6])
params = tf.constant(data)
indices = tf.constant([0, 3])
selected = tf.gather(params, indices)
```

`selected`

has shape `[2, 6]`

and printing its value gives

```
[[ 0 1 2 3 4 5]
[18 19 20 21 22 23]]
```

`indices`

can also just be a scalar (but cannot contain negative indices). E.g. in the above example:

```
tf.gather(params, tf.constant(3))
```

would print

```
[18 19 20 21 22 23]
```

Note that `indices`

can have any shape, but the elements stored in `indices`

always only refer to the *first* dimension of `params`

. E.g. if you want to retrieve both the 1st and 3rd row *and* the 2nd and 4th row at the same time, you can do this:

```
indices = tf.constant([[0, 2], [1, 3]])
selected = tf.gather(params, indices)
```

Now `selected`

will have shape `[2, 2, 6]`

and its content reads:

```
[[[ 0 1 2 3 4 5]
[12 13 14 15 16 17]]
[[ 6 7 8 9 10 11]
[18 19 20 21 22 23]]]
```

You can use `tf.gather`

to compute a permutation. E.g. the following reverses all rows of `params`

:

```
indices = tf.constant(list(range(4, -1, -1)))
selected = tf.gather(params, indices)
```

`selected`

is now

```
[[24 25 26 27 28 29]
[18 19 20 21 22 23]
[12 13 14 15 16 17]
[ 6 7 8 9 10 11]
[ 0 1 2 3 4 5]]
```

If you need access to any other than the first dimension, you could work around that using `tf.transpose`

: E.g. to gather columns instead of rows in our example, you could do this:

```
indices = tf.constant([0, 2])
selected = tf.gather(tf.transpose(params, [1, 0]), indices)
selected_t = tf.transpose(selected, [1, 0])
```

`selected_t`

is of shape `[5, 2]`

and reads:

```
[[ 0 2]
[ 6 8]
[12 14]
[18 20]
[24 26]]
```

However, `tf.transpose`

is rather expensive, so it might be better to use `tf.gather_nd`

for this use case.

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