深度学习pytorch(四)

  1. 神经网络的基本骨架-nn.Module的使用

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    import torch
    from torch import nn

    class Xy(nn.Module):
        def __init__(self):
            super().__init__()
        def forward(self,input):
            output = input + 1
            return output

    xy = Xy()
    x = torch.tensor(1.0)
    output = xy(x)
    print(output)

  2. 卷积操作

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import torch
import torch.nn.functional as F
input = torch.tensor([
    [1,2,0,3,1],
    [0,1,2,3,1],
    [1,2,1,0,0],
    [5,2,3,1,1],
    [2,1,0,1,1]
])
kernel = torch.tensor([
    [1,2,1],
    [0,1,0],
    [2,1,0]
])

input = torch.reshape(input,(1,1,5,5))
kernel = torch.reshape(kernel,(1,1,3,3))

print(input.shape)
print(kernel.shape)

output = F.conv2d(input,kernel,stride=1)    #stride为步长
print(output)


output2 = F.conv2d(input,kernel,stride=2)
print(output2)

output3 = F.conv2d(input,kernel,stride=1,padding=1)  #padding为填充
print(output3)
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import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset,batch_size=64)

class Xy(nn.Module):
    def __init__(self):
        super(Xy, self).__init__()
        self.conv1 = Conv2d(in_channels=3,out_channels=6,kernel_size=3,stride=1,padding=0)

    def forward(self,x):
        x = self.conv1(x)
        return x

xy = Xy()

writer = SummaryWriter("logs")
step = 0
for data in dataloader:
    imgs,targets = data
    output = xy(imgs)
    print(imgs.shape)
    print(output.shape)
    #torch.Size([64, 3, 32, 32])
    writer.add_images("input",imgs,step)
    #torch.Size([64, 6, 30, 30])->[xxx,3,30,30]
    output = torch.reshape(output,(-1,3,30,30))
    writer.add_images("output",output,step)

    step = step + 1

  1. 池化操作

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    import  torch
    import torchvision
    from torch import nn
    from torch.nn import MaxPool2d
    from torch.utils.data import DataLoader

    input = torch.tensor([
        [1,2,0,3,1],
        [0,1,2,3,1],
        [1,2,1,0,0],
        [5,2,3,1,1],
        [2,1,0,1,1]
    ],dtype=torch.float32)
    input = torch.reshape(input,(-1,1,5,5))  #-1表示让它自己计算
    print(input.shape)


    class Xy(nn.Module):
        def __init__(self):
            super(Xy, self).__init__()
            self.maxpool1 = MaxPool2d(kernel_size=3,ceil_mode=False)

        def forward(self,input):
            output = self.maxpool1(input)
            return output

    xy = Xy()
    output = xy(input)
    print(output)


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    import  torch
    import torchvision
    from torch import nn
    from torch.nn import MaxPool2d
    from torch.utils.data import DataLoader
    from torch.utils.tensorboard import SummaryWriter

    dataset = torchvision.datasets.CIFAR10("dataset",train=False,download=True,transform=torchvision.transforms.ToTensor())
    dataloader = DataLoader(dataset,batch_size=64)

    class Xy(nn.Module):
        def __init__(self):
            super(Xy, self).__init__()
            self.maxpool1 = MaxPool2d(kernel_size=3,ceil_mode=False)

        def forward(self,input):
            output = self.maxpool1(input)
            return output

    xy = Xy()

    writer = SummaryWriter("logs_maxpool")
    step = 0
    for data in dataloader:
        imgs,targets = data
        writer.add_images("input",imgs,step)
        output = xy(imgs)
        writer.add_images("output",output,step)
        step = step + 1

    writer.close()