问题：如何经过convTransposed1d输出指定大小的特征？

import torchfrom torch import nnimport torch.nn.functional as Fconv1 = nn.Conv1d(1, 2, 3, padding=1)conv2 = nn.Conv1d(in_channels=2, out_channels=4, kernel_size=3, padding=1)#转置卷积dconv1 = nn.ConvTranspose1d(4, 1, kernel_size=3, stride=2, padding=1, output_padding=1)x = torch.randn(16, 1, 8)print(x.size())x1 = conv1(x)x2 = conv2(x1)print(x2.size())x3 = dconv1(x2)print(x3.size())'''torch.Size([16, 1, 8])torch.Size([16, 4, 8]) #conv2输出特征图大小torch.Size([16, 1, 16]) #转置卷积输出特征图大小'''#转置卷积dconv1 = nn.ConvTranspose1d(1, 1, kernel_size=3, stride=3, padding=1, output_padding=1)x = torch.randn(16, 1, 8)print(x.size()) #torch.Size([16, 1, 23])x3 = dconv1(x)print(x3.size()) #torch.Size([16, 1, 23])

下面两图为演示conv1d，在padding和不padding下的输出特征图大小

不带padding

带padding

补充知识：判断pytorch是否支持GPU加速

如下所示：

print torch.cuda.is_available()

以上这篇pytorch 计算ConvTranspose1d输出特征大小方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。