mxnet.gluon.nn.MaxPool1D¶

class mxnet.gluon.nn.MaxPool1D(pool_size=2, strides=None, padding=0, layout='NCW', ceil_mode=False, **kwargs)[source]¶

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. Only supports ‘NCW’ layout for now. ‘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.

Inputs:

data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:

out: 3D output tensor with shape (batch_size, channels, out_width) when 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.

__init__(pool_size=2, strides=None, padding=0, layout='NCW', ceil_mode=False, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([pool_size, strides, padding, …])	Initialize self.
`apply`(fn)	Applies `fn` recursively to every child block as well as self.
`cast`(dtype)	Cast this Block to use another data type.
`collect_params`([select])	Returns a `ParameterDict` containing this `Block` and all of its children’s Parameters(default), also can returns the select `ParameterDict` which match some given regular expressions.
`export`(path[, epoch])	Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
`forward`(x, *args)	Defines the forward computation.
`hybrid_forward`(F, x)	Overrides to construct symbolic graph for this Block.
`hybridize`([active])	Activates or deactivates `HybridBlock` s recursively.
`infer_shape`(*args)	Infers shape of Parameters from inputs.
`infer_type`(*args)	Infers data type of Parameters from inputs.
`initialize`([init, ctx, verbose, force_reinit])	Initializes `Parameter` s of this `Block` and its children.
`load_parameters`(filename[, ctx, …])	Load parameters from file previously saved by save_parameters.
`load_params`(filename[, ctx, allow_missing, …])	[Deprecated] Please use load_parameters.
`name_scope`()	Returns a name space object managing a child `Block` and parameter names.
`register_child`(block[, name])	Registers block as a child of self.
`register_forward_hook`(hook)	Registers a forward hook on the block.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the block.
`save_parameters`(filename)	Save parameters to file.
`save_params`(filename)	[Deprecated] Please use save_parameters.
`summary`(*inputs)	Print the summary of the model’s output and parameters.

Attributes

`name`	Name of this `Block`, without ‘_’ in the end.
`params`	Returns this `Block`’s parameter dictionary (does not include its children’s parameters).
`prefix`	Prefix of this `Block`.