寒假学习Day 5:PyToch学习（二）_sample = next(iter(train_set))

寒假学习Day 5:PyToch学习（二）

CNN

数据准备

import torch
import torchvision
import torchvision.transforms as transforms
train_set=torchvision.datasets.FashionMNIST(
    root='./data/FashionMNIST'
    ,train=True
    ,download=True
    ,transform=transforms.Compose([
        transforms.ToTensor()
    ])
)
train_loader=torch.utils.data.DataLoader(train_set，batch_size=10)#训练加载器，使用的数据加载构造器，设置批处理大小为10，如果不设置则默认为1
import numpy as np
import matplotlib.pyplot as plt
torch.set_printoptions(linewidth=120)
len(train_set)#训练集数
train_set.train_labels#类标签
train_set.train_labels.bincount()#每个类的样本数
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• 查看单个训练数据样例

sample=next(iter(train_set))#将一个训练集对象传递给python内置的内部函数，返回迭代器的下一个项目
len(sample)#2，因为包含数据和标签
type(sample)#tuple
image,label=sample
image.shape
plt.imshow(image.squeeze(),cmap='gray')
print('label:',label)
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• 处理批量和数据加载器

10个一批

batch=next(iter(train_loader))
images,labels=batch
grid=torchvision.utils.make_grid(images,nrow=10)
#创建一个网格，图像作为第一个参数，nrow指定每行的图像数量
plt.figure(figsize=(15,15))
plt.imshow(np.transpose(grid,(1,2,0)))
print('labels:',labels)
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100个一批

train_loader=torch.utils.data.DataLoader(train_set,batch_size=100)
batch=next(iter(train_loader))
images,labels=batch
grid=torchvision.utils.make_grid(images,nrow=10)
plt.figure(figsize=(15,15))
plt.imshow(np.transpose(grid,(1,2,0)))
plt.savefig('clothes')
print('labels:',labels)
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构建

• 简单网络

class Network:
    def __init__(self):
        self.layer=None
    def forward(self,t):
        t=self.layer(t)
        return t
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• pytorch神经网络

import torch.nn as nn
class Network(nn.Module):
    def __init__(self):
        super(Network,self).__init()
        self.layer=None
    def forward(self,t):
        t=self.layer(t)
        return t
            
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• 拓展pytorch nn.moudle类

 class Network(nn.Module):
    def __init__(self):
        super(Network,self).__init__()
        self.conv1=nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5)#复合层
        self.conv2=nn.Conv2d(in_channels=6,out_channels=12,kernel_size=5)
        
        self.fc1=nn.Linear(in_features=12*4*4,out_features=120)#线性/全连接层/dense
        self.fc2=nn.Linear(in_features=120,out_features=60)
        self.out=nn.Linear(in_features=60,out_features=10)
        
    def forward(self,t):
        #implement the forward pass
        return t
network=Network()
network            
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kernel_size 卷积核 卷积滤波器
out_channels 输出通道（与滤波器数量一致）
out_features 输出特征