标签:32 self 实践 MaxPool2d padding PyTorch 64 Conv2d 搭建
PyTorch搭建小实践
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
# self.conv1 = Conv2d(3, 32, 5, padding = 2)
# self.maxpool1 = MaxPool2d(2)
# self.conv2 = Conv2d(32, 32, 5, padding = 2)
# self.maxpool2 = MaxPool2d(2)
# self.conv3 = Conv2d(32, 64, 5, padding = 2)
# self.maxpool3 = MaxPool2d(2)
# self.flatten = Flatten()
# self.linear1 = Linear(1024, 64)
# self.linear2 = Linear(64, 10)
self.model1 = Sequential(
Conv2d(3, 32, 5, padding = 2),
MaxPool2d(2),
Conv2d(32, 32, 5, padding = 2),
MaxPool2d(2),
Conv2d(32, 64, 5, padding = 2),
MaxPool2d(2),
Flatten(),
Linear(1024, 64),
Linear(64, 10)
)
def forward(self, x):
# x = self.conv1(x)
# x = self.maxpool1(x)
# x = self.conv2(x)
# x = self.maxpool2(x)
# x = self.conv3(x)
# x = self.maxpool3(x)
# x = self.flatten(x)
# x = self.linear1(x)
# x = self.linear2(x)
x = self.model1(x)
return x
model = Model()
print(model)
input = torch.ones((64, 3, 32, 32))
output = model(input)
print(output.shape)
writer = SummaryWriter("logs_seq")
writer.add_graph(model, input)
writer.close()
标签:32,self,实践,MaxPool2d,padding,PyTorch,64,Conv2d,搭建 来源: https://blog.csdn.net/weixin_51296032/article/details/122738207
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