MaxPool1D¶

class paddle.nn. MaxPool1D ( kernel_size, stride=None, padding=0, return_mask=False, ceil_mode=False, name=None ) [source]

This operation applies 1D max pooling over input signal composed of several input planes based on the input, and kernel_size, stride, padding parameters. Input(X) and Output(Out) are in NCL format, where N is batch size, C is the number of channels, L is the length of the feature.

The output value of the layer with input size (N, C, L), output (N, C, L_{out}) and kernel_size k can be precisely described as For average pool1d:

\[Output(N_i, C_i, l) = max(Input[N_i, C_i, stride imes l:stride imes l+k])\]

Parameters

kernel_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain an integer.
stride (int|list|tuple, optional) – The pool stride size. If pool stride size is a tuple or list, it must contain an integer. Default None, then stride will be equal to the kernel_size.
padding (str|int|list|tuple, optional) – The padding size. Padding could be in one of the following forms. 1. A string in [‘valid’, ‘same’]. 2. An integer, which means the feature map is zero padded by size of padding on every sides. 3. A list[int] or tuple(int) whose length is 1, which means the feature map is zero padded by the size of padding[0] on every sides. 4. A list[int] or tuple(int) whose length is 2, It has the form [pad_before, pad_after]. 5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], …]. Note that, the batch dimension and channel dimension should be [0,0] or(0,0). The default value is 0.
return_mask (bool, optional) – Whether return the max indices along with the outputs. default is False.
ceil_mode (bool, optional) – Whether to use the ceil function to calculate output height and width. False is the default. If it is set to False, the floor function will be used. Default False.
name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Returns

A callable object of MaxPool1D.

Shape:

x(Tensor): The input tensor of max pool1d operator, which is a 3-D tensor. The data type can be float32, float64.
output(Tensor): The output tensor of max pool1d operator, which is a 3-D tensor. The data type is same as input x.

Examples

           import paddle
import paddle.nn as nn

data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0)
pool_out = MaxPool1D(data)
# pool_out shape: [1, 3, 16]

MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_mask=True)
pool_out, indices = MaxPool1D(data)
# pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]

          

forward ( input ) forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments

extra_repr ( ) extra_repr¶: Extra representation of this layer, you can have custom implementation of your own layer.

MaxPool1D¶

forward¶

extra_repr¶