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.
    count_include_pad : bool, default True
        When 'False', will exclude padding elements when computing the average value.


    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.
    count_include_pad : bool, default True
        When 'False', will exclude padding elements when computing the average value.


    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.
    count_include_pad : bool, default True
        When 'False', will exclude padding elements when computing the average value.


    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.

NAME

    AI::MXNet::Gluon::NN::ReflectionPad2D

DESCRIPTION

    Pads the input tensor using the reflection of the input boundary.

    Parameters
    ----------
    padding: int
        An integer padding size

    Examples
    --------
    >>> $m = nn->ReflectionPad2D(3);
    >>> $input = mx->nd->random->normal(shape=>[16, 3, 224, 224]);
    >>> $output = $m->($input);