Table Of Contents
Table Of Contents


class mxnet.gluon.nn.AvgPool1D(pool_size=2, strides=None, padding=0, layout='NCW', ceil_mode=False, count_include_pad=True, **kwargs)[source]

Average pooling operation for temporal data.

  • 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. 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.
  • data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.
  • 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, count_include_pad=True, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.


__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.


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.