PyTorch-CNN Model

• 利用卷积神经网络对MINIST数据集进行训练

代码：

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import torch import numpy as np import torchvision #torch的视觉包 import torchvision.datasets as datasets import torchvision.transforms as transforms from torch.utils.data.dataloader import DataLoader from torchvision.transforms import ToTensor import matplotlib.pyplot as plt import PIL.Image as Image import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.datasets as datasets import torchvision.transforms as transforms import torch.optim as optim root='D:\Project_Encyclopedia' mnist=torchvision.datasets.MNIST(root,train=True,transform=ToTensor(),target_transform=None,download=False) bs=8 mnist_loader=torch.utils.data.DataLoader(dataset=mnist,batch_size=bs,shuffle=True) class CNNModel(nn.Module):     def __init__(self):   #初始化         super(CNNModel, self).__init__()  #调用父类         self.conv1 = nn.Conv2d(1, 20,5)   #二维卷积 输入特征的维数是1 1*28*28 输出为20个特征维度 卷积核为5         self.conv2 = nn.Conv2d(20, 12,5)  #输入为20 输出为12  12*8*8         self.fc1 = nn.Linear(12*4*4, 100,bias=True)   #线性层         self.fc2 = nn.Linear(100, 10,bias=True)   #线性层        def forward(self, x):         x=x         x = self.conv1(x) #前向卷积         x = F.relu(x)         x = F.max_pool2d(x,kernel_size=2,stride=2)                   x = self.conv2(x)         x = F.relu(x)         x = F.max_pool2d(x,kernel_size=2,stride=2)#取最大值 12*8*8 变成 12*4*4                   x = x.reshape(-1,12*4*4) #转为1维的数据         x = self.fc1(x)         x = F.relu(x)                   x=self.fc2(x)         return x cnnmodel=CNNModel() print(cnnmodel) for name,param in cnnmodel.named_parameters():     print(name,param.shape) optimizer = torch.optim.SGD(cnnmodel.parameters(), lr=0.1) for epoch in range(5):     total_loss=0     for batch in mnist_loader:         image,labels=batch         optimizer.zero_grad()         out=cnnmodel(image)         loss=F.cross_entropy(out,labels)         total_loss+=loss.item() #把数值取出来         loss.backward()         optimizer.step()     print("epoch:",epoch,"loss:",total_loss) out.argmax(dim=1) labels out.argmax(dim=1).eq(labels).sum().item() #准确度的评估 optimizer = torch.optim.SGD(cnnmodel.parameters(), lr=0.1) for epoch in range(5):     total_loss=0     total_correct=0     for batch in mnist_loader:         image,labels=batch         optimizer.zero_grad()         out=cnnmodel(image)         loss=F.cross_entropy(out,labels)         total_loss+=loss.item() #把数值取出来         loss.backward()         optimizer.step()         total_correct+=out.argmax(dim=1).eq(labels).sum().item()     print("epoch:",epoch,"loss:",total_loss,"acc:",total_correct/60000)

•  训练好的模型怎么保存下来

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1	#http://www.jianshu.com/p/4905bf8e06e5

可以查看上述的网站 有两种方法可以进行保存

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#http://www.jianshu.com/p/4905bf8e06e5 torch.save(cnnmodel,'D:/Project_Encyclopedia/cnnmodel.pkl') #还有别的保存方法 torch.save(cnnmodel.state_dict(),'D:/Project_Encyclopedia/cnnmodel.pt')

• Pooling Layer（池化层

在卷积神经网络中，卷积层之间往往会加上一个池化层。池化层可以非常有效地缩小参数矩阵的尺寸，从而减少最后全连层中的参数数量。使用池化层即可以加快计算速度也有防止过拟合的作用。在图像识别领域，有时图像太大，我们需要减少训练参数的数量，它被要求在随后的卷积层之间周期性地引进池化层。池化的唯一目的是减少图像的空间大小。池化在每一个纵深维度上独自完成，因此图像的纵深保持不变。池化层的最常见形式是最大池化，还有平均池化层。即对一块局部的特征做求最大值的操作或者做求平均值的操作。

• Batch Normalization

1）加快训练速度，这样我们就可以使用较大的学习率来训练网络。

2）提高网络的泛化能力。

3）BN层本质上是一个归一化网络层，可以替代局部响应归一化层（LRN层）。

4）可以打乱样本训练顺序（这样就不可能出现同一张照片被多次选择用来训练）。

1.计算样本均值。

2.计算样本方差。

3.样本数据标准化处理。

• 卷积神经网络和卷积核的运转

•  将batch_size改为32 并对网络进行调整

代码：

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import torch import numpy as np import torchvision #torch的视觉包 import torchvision.datasets as datasets import torchvision.transforms as transforms from torch.utils.data.dataloader import DataLoader from torchvision.transforms import ToTensor import matplotlib.pyplot as plt import PIL.Image as Image import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.datasets as datasets import torchvision.transforms as transforms import torch.optim as optim root='D:\Project_Encyclopedia' mnist=torchvision.datasets.MNIST(root,train=True,transform=ToTensor(),target_transform=None,download=False) mnist data=[d[0].data.cpu().numpy() for d in mnist] np.mean(data) #整个数据集的均值 np.std(data) #整个数据集的标准差 bs=32 train_dataloader=torch.utils.data.DataLoader(     dataset=torchvision.datasets.MNIST(root,train=True,                     transform=transforms.Compose([                         transforms.ToTensor(),                         transforms.Normalize((0.1307,),(0.3081,))]),                     target_transform=None,download=False),     batch_size=bs,     shuffle=True,     pin_memory=True ) test_dataloader=torch.utils.data.DataLoader(     dataset=torchvision.datasets.MNIST(root,train=False,                     transform=transforms.Compose([                         transforms.ToTensor(),                         transforms.Normalize((0.1307,),(0.3081,))]),                     target_transform=None,download=False),     batch_size=bs,     shuffle=True,     pin_memory=True ) class CNNModel(nn.Module):     def __init__(self):   #初始化         super(CNNModel, self).__init__()  #调用父类         self.conv1 = nn.Conv2d(1, 20,5,1)   # 1维->20维 卷积核大小为5         self.conv2 = nn.Conv2d(20, 50,5,1)  # 20维->50维 卷积核大小为5         self.fc1 = nn.Linear(4*4*50, 500)   #线性层         self.fc2 = nn.Linear(500, 10)   #线性层        def forward(self, x):         #x:1*28*28  1代表通道         x = F.relu(self.conv1(x)) #前向卷积 28*28 -> (28+1-5)=24 24*24         x = F.max_pool2d(x,kernel_size=2,stride=2) #12*12         x = F.relu(self.conv2(x)) #8*8         x = F.max_pool2d(x,kernel_size=2,stride=2)#4*4         x = x.reshape(-1,4*4*50)         #或者用 x = x.view(-1,4*4*50)         x = F.relu(self.fc1(x))         x=self.fc2(x)         return F.log_softmax(x,dim=1) cnnmodel=CNNModel() print(cnnmodel) for name,param in cnnmodel.named_parameters():     print(name,param.shape) optimizer = torch.optim.SGD(cnnmodel.parameters(), lr=0.1,momentum=0.5) for epoch in range(5):     total_loss=0     for batch in train_dataloader:         image,labels=batch         optimizer.zero_grad()         out=cnnmodel(image)         loss=F.cross_entropy(out,labels)         total_loss+=loss.item() #把数值取出来         loss.backward()         optimizer.step()     print("epoch:",epoch,"loss:",total_loss) out.argmax(dim=1) labels out.argmax(dim=1).eq(labels).sum().item() #准确度的评估 optimizer = torch.optim.SGD(cnnmodel.parameters(), lr=0.1,momentum=0.5) for epoch in range(5):     total_loss=0     total_correct=0     for batch in train_dataloader:         image,labels=batch         optimizer.zero_grad()         out=cnnmodel(image)         loss=F.cross_entropy(out,labels)         total_loss+=loss.item() #把数值取出来         loss.backward()         optimizer.step()         total_correct+=out.argmax(dim=1).eq(labels).sum().item()     print("epoch:",epoch,"loss:",total_loss,"acc:",total_correct/60000) #http://www.jianshu.com/p/4905bf8e06e5 torch.save(cnnmodel,'D:/Project_Encyclopedia/cnnmodel.pkl') #还有别的保存方法 torch.save(cnnmodel.state_dict(),'D:/Project_Encyclopedia/cnnmodel.pt')