class torch.nn.AvgPool1d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)对信号的输入通道,提供1维平均池化(average pooling )输入信号的大小(N,C,L),输出大小(N,C,L_out)和池化窗口大小k的关系是: $$out(N_i,Cj,l)=1/k*\sum^{k}{m=0}input(N{i},C{j}...
>>> m = nn.LPPool1d(2, 3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input) LPPool2d class torch.nn.LPPool2d(norm_type, kernel_size, stride=None, ceil_mode=False)[source] Applies a 2D power-average pooling over an input signal composed of several input...
Applies a 2D power-average pooling over an input signal composed of several input planes. If the sum of all inputs to the power of p is zero, the gradient is set to zero as well. SeeLPPool2dfor details. adaptive_max_pool1d torch.nn.functional.adaptive_max_pool1d(*args, **kwargs) A...
Pooling functions¶ avg_pool1d¶ torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)→ Tensor¶ Applies a 1D average pooling over an input signal composed of several input planes. See AvgPool1d for details and output ...
11) torch.nn.LPPool1dIt is used to apply a 1D power-average pooling over an input signal composed of several input planes. 12) torch.nn.LPPool2dIt is used to apply a 2D power-average pooling over an input signal composed of several input planes. ...
Pooling 函数 torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) 对由几个输入平面组成的输入信号进行一维平均池化。 有关详细信息和输出形状,请参阅AvgPool1d。 参数:-kernel_size– 窗口的大小 -stride– 窗口的步长。默认值为kernel_si...
Pooling是PyTorch含有的一种池化层,在PyTorch的中有六种形式: 自适应最大池化Adaptive Max Pooling: torch.nn.AdaptiveMaxPool1d(output_size) torch.nn.AdaptiveMaxPool2d(output_size) torch.nn.AdaptiveMaxPool3d(output_size) 自适应平均池化Adaptive Average Pooling: torch.nn.AdaptiveAvgPool1d(output_size) ...
Applies a 3D adaptive average pooling over an input signal composed of several input planes. See :class:`~torch.nn.AdaptiveAvgPool3d` for details and output shape. Args: output_size: the target output size (single integer or triple-integer tuple) ...
Pooling layers MaxPool1d MaxPool2d MaxPool3d MaxUnpool1d MaxUnpool2d MaxUnpool3d AvgPool1d AvgPool2d AvgPool3d FractionalMaxPool2d LPPool1d LPPool2d AdaptiveMaxPool1d AdaptiveMaxPool2d AdaptiveMaxPool3d AdaptiveAvgPool1d AdaptiveAvgPool2d ...
class torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True) 一维卷积层,输入的尺度是(N, C_in,L),输出尺度( N,C_out,L_out)的计算方式: $$ out(N_i, C_{out_j})=bias(C{out_j})+\sum^{C{in}-1}{k=0}weight(C{out_...