Pytorch基础_torch.size([64, 1, 30])-优快云博客

常引用的模块

import torch as t
import torch.nn as nn
from torch.autograd import Variable as V
import torch.utils.data as Data
import torch.optim as optim
import torch.nn.functional as F
import torchvision

快速构建网络

法一：

class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)
        self.predict = torch.nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

net1 = Net(1, 10, 1)

法二：

net2 = torch.nn.Sequential(
    torch.nn.Linear(1, 10),
    torch.nn.ReLU(),
    torch.nn.Linear(10, 1)
)

法三（一和二结合）：

class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1=nn.Sequential(
            nn.Conv2d(1,16,5,1,2), # 参数分别是输入channel,输出channel,kernel_size,strides,padding 当stride为1时，padding=(kernel_size-1)/2时图像大小不变
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        self.conv2=nn.Sequential(
            nn.Conv2d(16,32,5,1,2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        self.linear=nn.Linear(32*7*7,10)
        
    def forward(self,x):
        x=self.conv1(x)
        x=self.conv2(x)
        x=x.view(x.size(0),-1)
        x=self.linear(x)
        return x

注意用的激活函数relu所在的模块一样，一个是torch.nn.Relu，一个是torch.nn.functional.relu

导入数据集

导入MNIST数据集：

train_data=torchvision.datasets.MNIST(
    root='./MNIST', #存放的目录下
    download=True, #是否要现下载
    train=True, #是否是训练集
    transform=torchvision.transform.ToTensor() #将PIL.Image 或 numpy.array转化成            Tensor格式
)

此时就导入了Tensor类型的数据集了，可以手动输出显示：

print(train_data.train_data.size()) # 输出torch.Size([60000, 28, 28])
print(train_data.train_labels.size()) # 输出torch.Size([60000]) 
plt.imshow(train_data.train_data[0].numpy(),cmap='gray') # plt不能显示tensor类型数据，要转化成numpy
plt.title('%i' % train_data.train_labels[0])
plt.show()

发现输出的图片格式不是标准的pytorch训练格式，所以要进行变换：

train_loader=Data.DataLoader(dataset=train_data, batch_size=64, shuffle=True, num_workers=2)


train_iter=iter(train_loader)
batch_x1,batch_y1=train_iter.next()
print(batch_x1.size()) # 输出torch.Size([64, 1, 28, 28])
print(t.max(batch_x1)) # 输出1.0 ，此时已将图片从0-255.0转化成0-1.0

# 同理
for (batch_x,batch_y) in train_loader:
    ...
# 也会加入channel维度和正则化

Tensor数据	DataLoader迭代的数据
size:[64,28,28]	增加channel维度：[64,1,28,28]
pixel范围：0-255	pixel范围：0-1.0
数据类型：ByteTensor	数据类型：FloatTensor

常用torch函数：

torch.cat()

a=t.ones(2,2)
b=t.zeros(2,2)

c=t.cat([a,b],1) 

# 输出
# 1  1  0  0
# 1  1  0  0
# [torch.FloatTensor of size 2x4]

c=t.cat([a,b],0)

# 输出
# 1  1  
# 1  1
# 0  0
# 0  0
# [torch.FloatTensor of size 4x2]

torch.randn(2，3)

均值为0，方差为1 的正态分布

torch.rand(3,2)

[0,1)的均匀分布

torch.nn.functional.softmax() 和 .sum() 和 torch.nn.functional.log_softmax()

data = V(t.randn(5,2))
print(data)

print(F.softmax(data,dim=1))
print(F.softmax(data,dim=1).sum(dim=1))

'''
Variable containing:
 1.0147 -0.1819
 0.6182  0.0393
 0.9262 -0.9596
 0.3518 -0.4039
 1.7584 -0.4145
[torch.FloatTensor of size 5x2]

Variable containing:
 0.7679  0.2321
 0.6408  0.3592
 0.8683  0.1317
 0.6804  0.3196
 0.8978  0.1022
[torch.FloatTensor of size 5x2]

Variable containing:
 1
 1
 1
 1
 1
[torch.FloatTensor of size 5]
'''

torch.nn.functional.log_softmax()等价于log(softmax())