NAME

AI::MXNet::Gluon::NN::Conv

DESCRIPTION

Abstract nD convolution layer (private, used as implementation base).

This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of outputs.
If `use_bias` is `True`, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.

Parameters
----------
channels : int
    The dimensionality of the output space
    i.e. the number of output channels in the convolution.
kernel_size : int or tuple/list of n ints
    Specifies the dimensions of the convolution window.
strides: int or tuple/list of n ints,
    Specifies the strides of the convolution.
padding : int or tuple/list of n ints,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation: int or tuple/list of n ints,
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two convolution
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout : str,
    Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW',
    'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for
    batch, channel, height, width and depth dimensions respectively.
    Convolution is performed over 'D', 'H', and 'W' dimensions.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias: bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer: str or `Initializer`
    Initializer for the bias vector.

NAME

AI::MXNet::Gluon::NN::Conv1D

DESCRIPTION

1D convolution layer (e.g. temporal convolution).

This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If `use_bias` is True, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.

If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.


Parameters
----------
channels : int
    The dimensionality of the output space, i.e. the number of output
    channels (filters) in the convolution.
kernel_size :int or tuple/list of 1 int
    Specifies the dimensions of the convolution window.
strides : int or tuple/list of 1 int,
    Specify the strides of the convolution.
padding : int or a tuple/list of 1 int,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation : int or tuple/list of 1 int
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two conv
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout: str, default 'NCW'
    Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
    'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
    respectively. Convolution is applied on the 'W' dimension.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias : bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
    Initializer for the bias vector.


Input shape:
    This depends on the `layout` parameter. Input is 3D array of shape
    (batch_size, in_channels, width) if `layout` is `NCW`.

Output shape:
    This depends on the `layout` parameter. Output is 3D array of shape
    (batch_size, channels, out_width) if `layout` is `NCW`.
    out_width is calculated as::

        out_width = floor((width+2*padding-dilation*(kernel_size-1)-1)/stride)+1

NAME

AI::MXNet::Gluon::NN::Conv2D

DESCRIPTION

2D convolution layer (e.g. spatial convolution over images).

This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is True,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.

If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.

Parameters
----------
channels : int
    The dimensionality of the output space, i.e. the number of output
    channels (filters) in the convolution.
kernel_size :int or tuple/list of 2 int
    Specifies the dimensions of the convolution window.
strides : int or tuple/list of 2 int,
    Specify the strides of the convolution.
padding : int or a tuple/list of 2 int,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation : int or tuple/list of 2 int
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two conv
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout : str, default 'NCHW'
    Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
    'N', 'C', 'H', 'W' stands for batch, channel, height, and width
    dimensions respectively. Convolution is applied on the 'H' and
    'W' dimensions.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias : bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
    Initializer for the bias vector.


Input shape:
    This depends on the `layout` parameter. Input is 4D array of shape
    (batch_size, in_channels, height, width) if `layout` is `NCHW`.

Output shape:
    This depends on the `layout` parameter. Output is 4D array of shape
    (batch_size, channels, out_height, out_width) if `layout` is `NCHW`.

    out_height and out_width are calculated as::

        out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
        out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1

NAME

AI::MXNet::Gluon::NN::Conv3D

DESCRIPTION

3D convolution layer (e.g. spatial convolution over volumes).

This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is `True`,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.

If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.

Parameters
----------
channels : int
    The dimensionality of the output space, i.e. the number of output
    channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
    Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
    Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation : int or tuple/list of 3 int
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two conv
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout : str, default 'NCDHW'
    Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
    'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
    depth dimensions respectively. Convolution is applied on the 'D',
    'H' and 'W' dimensions.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias : bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
    Initializer for the bias vector.


Input shape:
    This depends on the `layout` parameter. Input is 5D array of shape
    (batch_size, in_channels, depth, height, width) if `layout` is `NCDHW`.

Output shape:
    This depends on the `layout` parameter. Output is 5D array of shape
    (batch_size, channels, out_depth, out_height, out_width) if `layout` is
    `NCDHW`.

    out_depth, out_height and out_width are calculated as::

        out_depth = floor((depth+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
        out_height = floor((height+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
        out_width = floor((width+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2])+1

NAME

AI::MXNet::Gluon::NN::Conv1DTranspose

DESCRIPTION

Transposed 1D convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.

If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.

Parameters
----------
channels : int
    The dimensionality of the output space, i.e. the number of output
    channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
    Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
    Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation : int or tuple/list of 3 int
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two conv
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout : str, default 'NCW'
    Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
    'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
    respectively. Convolution is applied on the 'W' dimension.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias : bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
    Initializer for the bias vector.


Input shape:
    This depends on the `layout` parameter. Input is 3D array of shape
    (batch_size, in_channels, width) if `layout` is `NCW`.

Output shape:
    This depends on the `layout` parameter. Output is 3D array of shape
    (batch_size, channels, out_width) if `layout` is `NCW`.

    out_width is calculated as::

        out_width = (width-1)*strides-2*padding+kernel_size+output_padding

NAME

AI::MXNet::Gluon::NN::Conv2DTranspose

DESCRIPTION

Transposed 2D convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.

If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.


Parameters
----------
channels : int
    The dimensionality of the output space, i.e. the number of output
    channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
    Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
    Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation : int or tuple/list of 3 int
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two conv
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout : str, default 'NCHW'
    Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
    'N', 'C', 'H', 'W' stands for batch, channel, height, and width
    dimensions respectively. Convolution is applied on the 'H' and
    'W' dimensions.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias : bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
    Initializer for the bias vector.


Input shape:
    This depends on the `layout` parameter. Input is 4D array of shape
    (batch_size, in_channels, height, width) if `layout` is `NCHW`.

Output shape:
    This depends on the `layout` parameter. Output is 4D array of shape
    (batch_size, channels, out_height, out_width) if `layout` is `NCHW`.

    out_height and out_width are calculated as::

        out_height = (height-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]
        out_width = (width-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]

NAME

AI::MXNet::Gluon::NN::Conv3DTranspose

DESCRIPTION

Transposed 3D convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.

If `in_channels` is not specified, `Parameter` initialization will be
deferred to the first time `forward` is called and `in_channels` will be
inferred from the shape of input data.


Parameters
----------
channels : int
    The dimensionality of the output space, i.e. the number of output
    channels (filters) in the convolution.
kernel_size :int or tuple/list of 3 int
    Specifies the dimensions of the convolution window.
strides : int or tuple/list of 3 int,
    Specify the strides of the convolution.
padding : int or a tuple/list of 3 int,
    If padding is non-zero, then the input is implicitly zero-padded
    on both sides for padding number of points
dilation : int or tuple/list of 3 int
    Specifies the dilation rate to use for dilated convolution.
groups : int
    Controls the connections between inputs and outputs.
    At groups=1, all inputs are convolved to all outputs.
    At groups=2, the operation becomes equivalent to having two conv
    layers side by side, each seeing half the input channels, and producing
    half the output channels, and both subsequently concatenated.
layout : str, default 'NCDHW'
    Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
    'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
    depth dimensions respectively. Convolution is applied on the 'D',
    'H', and 'W' dimensions.
in_channels : int, default 0
    The number of input channels to this layer. If not specified,
    initialization will be deferred to the first time `forward` is called
    and `in_channels` will be inferred from the shape of input data.
activation : str
    Activation function to use. See :func:`~mxnet.ndarray.Activation`.
    If you don't specify anything, no activation is applied
    (ie. "linear" activation: `a(x) = x`).
use_bias : bool
    Whether the layer uses a bias vector.
weight_initializer : str or `Initializer`
    Initializer for the `weight` weights matrix.
bias_initializer : str or `Initializer`
    Initializer for the bias vector.


Input shape:
    This depends on the `layout` parameter. Input is 5D array of shape
    (batch_size, in_channels, depth, height, width) if `layout` is `NCDHW`.

Output shape:
    This depends on the `layout` parameter. Output is 5D array of shape
    (batch_size, channels, out_depth, out_height, out_width) if `layout` is `NCDHW`.
    out_depth, out_height and out_width are calculated as::

        out_depth = (depth-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]
        out_height = (height-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]
        out_width = (width-1)*strides[2]-2*padding[2]+kernel_size[2]+output_padding[2]

NAME

AI::MXNet::Gluon::NN::MaxPool1D

DESCRIPTION

Max pooling operation for one dimensional data.


Parameters
----------
pool_size: int
    Size of the max pooling windows.
strides: int, or None
    Factor by which to downscale. E.g. 2 will halve the input size.
    If `None`, it will default to `pool_size`.
padding: int
    If padding is non-zero, then the input is implicitly
    zero-padded on both sides for padding number of points.
layout : str, default 'NCW'
    Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
    'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
    respectively. Pooling is applied on the W dimension.
ceil_mode : bool, default False
    When `True`, will use ceil instead of floor to compute the output shape.


Input shape:
    This depends on the `layout` parameter. Input is 3D array of shape
    (batch_size, channels, width) if `layout` is `NCW`.

Output shape:
    This depends on the `layout` parameter. Output is 3D array of shape
    (batch_size, channels, out_width) if `layout` is `NCW`.

    out_width is calculated as::

        out_width = floor((width+2*padding-pool_size)/strides)+1

    When `ceil_mode` is `True`, ceil will be used instead of floor in this
    equation.

NAME

AI::MXNet::Gluon::NN::MaxPool2D

DESCRIPTION

Max pooling operation for two dimensional (spatial) data.


Parameters
----------
pool_size: int or list/tuple of 2 ints,
    Size of the max pooling windows.
strides: int, list/tuple of 2 ints, or None.
    Factor by which to downscale. E.g. 2 will halve the input size.
    If `None`, it will default to `pool_size`.
padding: int or list/tuple of 2 ints,
    If padding is non-zero, then the input is implicitly
    zero-padded on both sides for padding number of points.
layout : str, default 'NCHW'
    Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
    'N', 'C', 'H', 'W' stands for batch, channel, height, and width
    dimensions respectively. padding is applied on 'H' and 'W' dimension.
ceil_mode : bool, default False
    When `True`, will use ceil instead of floor to compute the output shape.


Input shape:
    This depends on the `layout` parameter. Input is 4D array of shape
    (batch_size, channels, height, width) if `layout` is `NCHW`.

Output shape:
    This depends on the `layout` parameter. Output is 4D array of shape
    (batch_size, channels, out_height, out_width)  if `layout` is `NCHW`.

    out_height and out_width are calculated as::

        out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1
        out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1

    When `ceil_mode` is `True`, ceil will be used instead of floor in this
    equation.

NAME

AI::MXNet::Gluon::NN::MaxPool3D

DESCRIPTION

Max pooling operation for 3D data (spatial or spatio-temporal).


Parameters
----------
pool_size: int or list/tuple of 3 ints,
    Size of the max pooling windows.
strides: int, list/tuple of 3 ints, or None.
    Factor by which to downscale. E.g. 2 will halve the input size.
    If `None`, it will default to `pool_size`.
padding: int or list/tuple of 3 ints,
    If padding is non-zero, then the input is implicitly
    zero-padded on both sides for padding number of points.
layout : str, default 'NCDHW'
    Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
    'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
    depth dimensions respectively. padding is applied on 'D', 'H' and 'W'
    dimension.
ceil_mode : bool, default False
    When `True`, will use ceil instead of floor to compute the output shape.


Input shape:
    This depends on the `layout` parameter. Input is 5D array of shape
    (batch_size, channels, depth, height, width) if `layout` is `NCDHW`.

Output shape:
    This depends on the `layout` parameter. Output is 5D array of shape
    (batch_size, channels, out_depth, out_height, out_width) if `layout`
    is `NCDHW`.

    out_depth, out_height and out_width are calculated as ::

        out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
        out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
        out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1

    When `ceil_mode` is `True`, ceil will be used instead of floor in this
    equation.

NAME

AI::MXNet::Gluon::NN::AvgPool1D

DESCRIPTION

Average pooling operation for temporal data.

Parameters
----------
pool_size: int
    Size of the max pooling windows.
strides: int, or None
    Factor by which to downscale. E.g. 2 will halve the input size.
    If `None`, it will default to `pool_size`.
padding: int
    If padding is non-zero, then the input is implicitly
    zero-padded on both sides for padding number of points.
layout : str, default 'NCW'
    Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc.
    'N', 'C', 'W' stands for batch, channel, and width (time) dimensions
    respectively. padding is applied on 'W' dimension.
ceil_mode : bool, default False
    When `True`, will use ceil instead of floor to compute the output shape.


Input shape:
    This depends on the `layout` parameter. Input is 3D array of shape
    (batch_size, channels, width) if `layout` is `NCW`.

Output shape:
    This depends on the `layout` parameter. Output is 3D array of shape
    (batch_size, channels, out_width) if `layout` is `NCW`.

    out_width is calculated as::

        out_width = floor((width+2*padding-pool_size)/strides)+1

    When `ceil_mode` is `True`, ceil will be used instead of floor in this
    equation.

NAME

AI::MXNet::Gluon::NN::AvgPool2D

DESCRIPTION

Average pooling operation for spatial data.

Parameters
----------
pool_size: int or list/tuple of 2 ints,
    Size of the max pooling windows.
strides: int, list/tuple of 2 ints, or None.
    Factor by which to downscale. E.g. 2 will halve the input size.
    If `None`, it will default to `pool_size`.
padding: int or list/tuple of 2 ints,
    If padding is non-zero, then the input is implicitly
    zero-padded on both sides for padding number of points.
layout : str, default 'NCHW'
    Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc.
    'N', 'C', 'H', 'W' stands for batch, channel, height, and width
    dimensions respectively. padding is applied on 'H' and 'W' dimension.
ceil_mode : bool, default False
    When True, will use ceil instead of floor to compute the output shape.


Input shape:
    This depends on the `layout` parameter. Input is 4D array of shape
    (batch_size, channels, height, width) if `layout` is `NCHW`.

Output shape:
    This depends on the `layout` parameter. Output is 4D array of shape
    (batch_size, channels, out_height, out_width)  if `layout` is `NCHW`.

    out_height and out_width are calculated as::

        out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1
        out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1

    When `ceil_mode` is `True`, ceil will be used instead of floor in this
    equation.

NAME

AI::MXNet::Gluon::NN::AvgPool3D

DESCRIPTION

Average pooling operation for 3D data (spatial or spatio-temporal).

Parameters
----------
pool_size: int or list/tuple of 3 ints,
    Size of the max pooling windows.
strides: int, list/tuple of 3 ints, or None.
    Factor by which to downscale. E.g. 2 will halve the input size.
    If `None`, it will default to `pool_size`.
padding: int or list/tuple of 3 ints,
    If padding is non-zero, then the input is implicitly
    zero-padded on both sides for padding number of points.
layout : str, default 'NCDHW'
    Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc.
    'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and
    depth dimensions respectively. padding is applied on 'D', 'H' and 'W'
    dimension.
ceil_mode : bool, default False
    When True, will use ceil instead of floor to compute the output shape.


Input shape:
    This depends on the `layout` parameter. Input is 5D array of shape
    (batch_size, channels, depth, height, width) if `layout` is `NCDHW`.

Output shape:
    This depends on the `layout` parameter. Output is 5D array of shape
    (batch_size, channels, out_depth, out_height, out_width) if `layout`
    is `NCDHW`.

    out_depth, out_height and out_width are calculated as ::

        out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
        out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
        out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1

    When `ceil_mode` is `True,` ceil will be used instead of floor in this
    equation.

NAME

AI::MXNet::Gluon::NN::GlobalMaxPool1D

DESCRIPTION

Global max pooling operation for temporal data.

NAME

AI::MXNet::Gluon::NN::GlobalMaxPool2D

DESCRIPTION

Global max pooling operation for spatial data.

NAME

AI::MXNet::Gluon::NN::GlobalMaxPool3D

DESCRIPTION

Global max pooling operation for 3D data.

NAME

AI::MXNet::Gluon::NN::GlobalAvgPool1D

DESCRIPTION

Global average pooling operation for temporal data.

NAME

AI::MXNet::Gluon::NN::GlobalAvgPool2D

DESCRIPTION

Global average pooling operation for spatial data.

NAME

AI::MXNet::Gluon::NN::GlobalAvgPool2D

DESCRIPTION

Global average pooling operation for 3D data.