transposedConv2dLayer使用示例
时间: 2023-06-27 17:06:10 浏览: 42
下面是一个使用transposedConv2dLayer的示例代码:
```
import torch.nn as nn
class TransposedConvNet(nn.Module):
def __init__(self):
super(TransposedConvNet, self).__init__()
# 定义一个转置卷积层
self.transposed_conv_layer = nn.ConvTranspose2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=1)
def forward(self, x):
x = self.transposed_conv_layer(x)
return x
# 创建一个输入张量
input_tensor = torch.randn(1, 3, 16, 16)
# 创建一个转置卷积神经网络
transposed_conv_net = TransposedConvNet()
# 进行前向传播
output_tensor = transposed_conv_net(input_tensor)
# 输出结果的形状
print(output_tensor.shape)
```
在这个例子中,我们定义了一个TransposedConvNet类,它包含一个转置卷积层,输入通道数为3,输出通道数为6,卷积核大小为3x3,步长为1,填充为1。在前向传播过程中,我们传入一个随机生成的输入张量,然后调用transposed_conv_layer层,最后输出转置卷积层的输出张量的形状。