AvgPool1D¶

class paddle.nn. AvgPool1D ( kernel_size, stride=None, padding=0, exclusive=True, ceil_mode=False, name=None ) [source]

This operation applies a 1D average pooling over an input signal composed of several input planes, based on the input, output_size, return_mask 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 tensor shape will be [N, C, output_size].

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

\[Output(N_i, C_i, l) = \frac{Input[N_i, C_i, stride \times l:stride \times l+k]}{ksize}\]

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 int, 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.
exclusive (bool, optional) – Whether to exclude padding points in average pooling mode, default is True.
ceil_mode (bool, optional) – ${ceil_mode_comment}Whether to use the ceil function to calculate output height and width. If it is set to False, the floor function will be used. The default value is False.
name (str, optional) – For eed to detailed information, please refer to Name. Usually name is no nset and None by default.

Shape:

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

Returns: A callable object of AvgPool1D.

Examples

           import paddle
import paddle.nn as nn

data = paddle.uniform([1, 3, 32], dtype="float32", min=-1, max=1)
AvgPool1D = nn.AvgPool1D(kernel_size=2, stride=2, padding=0)
pool_out = AvgPool1D(data)
# pool_out shape: [1, 3, 16]

          

forward ( x ) 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.

AvgPool1D¶

forward¶

extra_repr¶