avg_pool1d#
- ivy.avg_pool1d(x, kernel, strides, padding, /, *, data_format='NWC', count_include_pad=False, ceil_mode=False, division_override=None, out=None)[source]#
Compute a 1-D avg pool given 3-D input x.
- Parameters:
x (
Union
[Array
,NativeArray
]) – Input image [batch_size, w, d_in].kernel (
Union
[int
,Tuple
[int
]]) – Size of the kernel i.e., the sliding window for each dimension of input. [w].strides (
Union
[int
,Tuple
[int
]]) – The stride of the sliding window for each dimension of input.padding (
str
) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.data_format (
str
) – NWC” or “NCW”. Defaults to “NWC”. (default:'NWC'
)count_include_pad (
bool
) – Whether to include padding in the averaging calculation. (default:False
)ceil_mode (
bool
) – Whether to use ceil or floor for creating the output shape. (default:False
)division_override (
Optional
[int
]) – If specified, it will be used as the divisor, (default:None
) otherwise kernel_size will be used.out (
Optional
[Array
]) – optional output array, for writing the result to. (default:None
)
- Return type:
- Returns:
ret – The result of the pooling operation.
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 ofthe arguments.
Examples
>>> x = ivy.arange(0, 24.).reshape((2, 3, 4)) >>> print(ivy.avg_pool1d(x, 2, 2, 'SAME')) ivy.array([[[ 2., 3., 4., 5.], [ 8., 9., 10., 11.]],
- [[14., 15., 16., 17.],
[20., 21., 22., 23.]]])
>>> x = ivy.arange(0, 24.).reshape((2, 3, 4)) >>> print(ivy.avg_pool1d(x, 2, 2, 'VALID')) ivy.array([[[ 2., 3., 4., 5.]],
[[14., 15., 16., 17.]]])
- Array.avg_pool1d(self, kernel, strides, padding, /, *, data_format='NWC', count_include_pad=False, ceil_mode=False, out=None)#
ivy.Array instance method variant of ivy.avg_pool1d. This method simply wraps the function, and so the docstring for ivy.avg_pool1d also applies to this method with minimal changes.
- Parameters:
self (
Array
) – Input image [batch_size,w,d_in].kernel (
Union
[int
,Tuple
[int
]]) – The size of the window for each dimension of the input tensor.strides (
Union
[int
,Tuple
[int
]]) – The stride of the sliding window for each dimension of input.padding (
str
) – “SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.data_format (
str
) – “NWC” or “NCW”. Defaults to “NWC”. (default:'NWC'
)count_include_pad (
bool
) – Whether to include padding in the averaging calculation. (default:False
)ceil_mode (
bool
) – Whether to use ceil or floor for creating the output shape. (default:False
)out (
Optional
[Array
]) – optional output array, for writing the result to. It must have a shape that (default:None
) the inputs broadcast to.
- Return type:
Array
- Returns:
ret – The result of the max pooling operation.
Examples
>>> x = ivy.arange(0, 24.).reshape((2, 3, 4)) >>> print(x.avg_pool1d(2, 2, 'SAME')) ivy.array([[[ 2., 3., 4., 5.], [ 8., 9., 10., 11.]], [[14., 15., 16., 17.], [20., 21., 22., 23.]]])
>>> x = ivy.arange(0, 24.).reshape((2, 3, 4)) >>> print(x.avg_pool1d(2, 2, 'VALID')) ivy.array([[[ 2., 3., 4., 5.]], [[14., 15., 16., 17.]]])
- Container.avg_pool1d(self, kernel, strides, padding, /, *, data_format='NWC', count_include_pad=False, ceil_mode=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#
ivy.Container instance method variant of ivy.avg_pool1d. This method simply wraps the function, and so the docstring for ivy.avg_pool1d also applies to this method with minimal changes.
- Parameters:
self (
Container
) – Container of input images [batch_size, w, d_in].kernel (
Union
[int
,Tuple
[int
]]) – Size of the kernel i.e., the sliding window for each dimension of input. [w].strides (
Union
[int
,Tuple
[int
]]) – The stride of the sliding window for each dimension of input.padding (
str
) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.data_format (
str
) – NWC” or “NCW”. Defaults to “NWC”. (default:'NWC'
)count_include_pad (
bool
) – Whether to include padding in the averaging calculation. (default:False
)ceil_mode (
bool
) – Whether to use ceil or floor for creating the output shape. (default:False
)out (
Optional
[Container
]) – optional output array, for writing the result to. (default:None
)
- Return type:
Container
- Returns:
ret – The result of the pooling operation.
Examples
>>> a = ivy.arange(12.).reshape((2,2,3)) >>> b = ivy.arange(24.).reshape((2,3,4)) >>> x = ivy.Container({'a': a, 'b': b}) >>> print(x.avg_pool1d(2, 2, "VALID")) { a: ivy.array([[[1.5, 2.5, 3.5]], [[7.5, 8.5, 9.5]]]), b: ivy.array([[[2., 3., 4., 5.]], [[14., 15., 16., 17.]]]) }