# dropout1d#

ivy.dropout1d(x, prob, /, *, training=True, data_format='NWC', out=None)[source]#

Randomly zero out entire channels with probability prob using samples from a Bernoulli distribution and the remaining channels are scaled by (1/1-prob). In this case, dropout1d performs a channel-wise dropout but assumes a channel is a 1D feature map.

Parameters:
• x (`Union`[`Array`, `NativeArray`]) – a 2D or 3D input array. Should have a floating-point data type.

• prob (`float`) – probability of a channel to be zero-ed.

• training (`bool`, default: `True`) – controls whether dropout1d is performed during training or ignored during testing.

• data_format (`str`, default: `'NWC'`) – “NWC” or “NCW”. Defaults to “NWC”.

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

• ret

an array with some channels zero-ed and the rest of channels are

scaled by (1/1-prob).

• Both the description and the type hints above assumes an array input for simplicity,

• but this function is nestable, and therefore also accepts `ivy.Container`

• instances in place of any of the arguments.

Examples

With `ivy.Array` input:

```>>> x = ivy.array([1, 1, 1]).reshape([1, 1, 3])
>>> y = ivy.dropout1d(x, 0.5)
>>> print(y)
ivy.array([[[2., 0, 2.]]])
```
```>>> x = ivy.array([1, 1, 1]).reshape([1, 1, 3])
>>> y = ivy.dropout1d(x, 1, training=False, data_format="NCW")
>>> print(y)
ivy.array([[[1, 1, 1]]])
```

With one `ivy.Container` input: >>> x = ivy.Container(a=ivy.array([100, 200, 300]).reshape([1, 1, 3]), … b=ivy.array([400, 500, 600]).reshape([1, 1, 3])) >>> y = ivy.dropout1d(x, 0.5) >>> print(y) {

a: ivy.array([[[200., 400., 0.]]]), b: ivy.array([[[0., 0., 0.]]])

}

Array.dropout1d(self, prob, /, *, training=True, data_format='NWC', out=None)[source]#

ivy.Array instance method variant of ivy.dropout1d. This method simply wraps the function, and so the docstring for ivy.dropout1d also applies to this method with minimal changes.

Parameters:
• self (`Array`) – The input array x to perform dropout on.

• prob (`float`) – The probability of zeroing out each array element, float between 0 and 1.

• training (`bool`, default: `True`) – Turn on dropout if training, turn off otherwise. Default is `True`.

• data_format (`str`, default: `'NWC'`) – “NWC” or “NCW”. Default is `"NWC"`.

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

ret – Result array of the output after dropout is performed.

Examples

```>>> x = ivy.array([1, 1, 1]).reshape([1, 1, 3])
>>> y = x.dropout1d(0.5)
>>> print(y)
ivy.array([[[2., 0, 2.]]])
```
Container.dropout1d(self, prob, /, *, training=True, data_format='NWC', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.dropout1d. This method simply wraps the function, and so the docstring for ivy.dropout1d also applies to this method with minimal changes.

Parameters:
• self (`Container`) – The input container to perform dropout on.

• prob (`Union`[`float`, `Container`]) – The probability of zeroing out each array element, float between 0 and 1.

• training (`Union`[`bool`, `Container`], default: `True`) – Turn on dropout if training, turn off otherwise. Default is `True`.

• data_format (`Union`[`str`, `Container`], default: `'NWC'`) – “NWC” or “NCW”. Default is `"NCW"`.

• key_chains (`Optional`[`Union`[`List`[`str`], `Dict`[`str`, `str`], `Container`]], default: `None`) – The key-chains to apply or not apply the method to. Default is `None`.

• to_apply (`Union`[`bool`, `Container`], default: `True`) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is `True`.

• prune_unapplied (`Union`[`bool`, `Container`], default: `False`) – Whether to prune key_chains for which the function was not applied. Default is `False`.

• map_sequences (`Union`[`bool`, `Container`], default: `False`) – Whether to also map method to sequences (lists, tuples). Default is `False`.

• out (`Optional`[`Container`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Container`

Returns:

ret – Result container of the output after dropout is performed.

Examples

```>>> x = ivy.Container(a=ivy.array([1, 2, 3]).reshape([1, 1, 3]),
...                   b=ivy.array([4, 5, 6]).reshape([1, 1, 3]))
>>> y = x.dropout1d(x, 0.5)
>>> print(y)
{
a: ivy.array([[[0., 4., 0.]]]),
b: ivy.array([[[0., 0., 12.]]])
}
```