torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) 对由几个输入平面组成的输入信号应用一维卷积。 详细信息和输出形状,查看Conv1d 参数: input– 输入张量的形状 (minibatch x in_channels x iW) ...
torch.nn.functional.max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)source对由几个输入平面组成的输入进行1D最大池化。 有关详细信息和输出形状,参考MaxPool1dtorch.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, ...
torch.nn.functional.max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) torch.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) torch.nn.functional.max_pool3d(input...
torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) 对由几个输入平面组成的输入信号应用一维卷积。 详细信息和输出形状,查看Conv1d 参数: input– 输入张量的形状 (minibatch x in_channels x iW) ...
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 ...
torch.nn.functionaltorch.nn.functional.adaptive_avg_pool1d()torch.nn.functional.adaptive_avg_pool2d()torch.nn.functional.adaptive_avg_pool3d()torch.nn.functional.adaptive_max_pool1d()torch.nn.functional.adaptive_max_pool2d()torch.nn.functional.adaptive_max_pool3d()torch.nn.functional.affine_grid...
torch.nn.functional pairwise_distance Convolution functions conv1d torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor Applies a 1D convolution over an input signal composed of several input planes....
max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [(N, Co), ...]*len(Ks) x = torch.cat(x, 1) ''' x1 = self.conv_and_pool(x,self.conv13) #(N,Co) x2 = self.conv_and_pool(x,self.conv14) #(N,Co) x3 = self.conv_and_pool(x,self.conv15) #(N,Co) x =...
关于MaxPool1d、MaxPool2d的区别:https://www.jianshu.com/p/c5b8e02bedbe 3、 class torch.nn.ReLU(inplace=False) #max(0,x) class torch.nn.ReLU6(inplace=False) #min(max(0,x), 6) class torch.nn.ELU(alpha=1.0, inplace=False) #max(0,x) + min(0, alpha * (e^x - 1))带泄露...
max_pool1d, 2: F.max_pool2d, 3: F.max_pool3d}[spatial_dims](input=pad, kernel_size=size[2:], stride=stride[2:], padding=0, dilation=dilation[2:], return_indices=with_index) return result Example #4Source File: deep_lift.py From captum with BSD 3-Clause "New" or "Revised" ...