Python实验十四

Pytorch 之神经网络

第1关 加载数据——Data Loader

import torch
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import os
import sys
path = os.path.split(os.path.abspath(os.path.realpath(sys.argv[0])))[0] + os.path.sep

path = path[:-10] + '/data/'

#/********** Begin *********/
# 下载训练集 MNIST 训练集,设置 root = path,train=False ,download=False,赋值给变量train_dataset
train_dataset = dsets.MNIST(root=path, train=False, transform=transforms.ToTensor(),download=False)

# 创建dataset=train_dataset,batch_size=100, shuffle=True的DataLoader变量data_loader
data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=100, shuffle=True)

# 获取数据集中的第四个元素的图片信息和类别
print('Number of samples: ',len(train_dataset))
img, target = train_dataset[3]
#按照格式“Image Size: ...”输出该图片大小
print("Image Size: ",img.size())

#按照格式“Image Target: ...”输出该图片的类别
print("Image Target: ",target)

print("\nCongratulation!")
#/********** End *********/

第2关 建立模型，定义损失和优化函数

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable

# CNN Model (2 conv layer)
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(2))
        #/********** Begin *********/
        self.layer2 = nn.Sequential(
        #定义卷积层Conv2d：输入16张特征图，输出32张特征图，卷积核5x5,padding为2
            nn.Conv2d(16,32,kernel_size=5,padding=2),
        #定义BatchNorm2d层，参数为32
            nn.BatchNorm2d(32),
        #定义非线性层ReLU
            nn.ReLU(),
            nn.MaxPool2d(2))

        # 定义全连接层：线性连接(y = Wx + b)，7*7*32个节点连接到10个节点上
        self.fc = nn.Linear(7*7*32,10)
    def forward(self, x):
        out = self.layer1(x)
        # 输入out->layer2->更新到out
        out = self.layer2(out)
        #view函数将张量x变形成一维向量形式，总特征数不变，为全连接层做准备
        out = out.view(out.size(0),-1)
        # 输入out->fc，更新到out
        out = self.fc(out)
        return out
#/********** End *********/

cnn = CNN()

params = list(cnn.parameters())
print(len(params))

for name, parameters in cnn.named_parameters():
    print(name, "：", parameters.size())

第3关 训练模型

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
import os
import sys
path = os.path.split(os.path.abspath(os.path.realpath(sys.argv[0])))[0] + os.path.sep


# Hyper Parameters
batch_size = 100
learning_rate = 0.001
num_epochs = 1
# MNIST Dataset
train_dataset = dsets.MNIST(root='./data/',
                            train=True,
                            transform=transforms.ToTensor(),
                            download=False)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)
# CNN Model (2 conv layer)
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
                                    nn.Conv2d(1, 16, kernel_size=5, padding=2),
                                    nn.BatchNorm2d(16),
                                    nn.ReLU(),
                                    nn.MaxPool2d(2))
        self.layer2 = nn.Sequential(
                                    nn.Conv2d(16, 32, kernel_size=5, padding=2),
                                    nn.BatchNorm2d(32),
                                    nn.ReLU(),
                                    nn.MaxPool2d(2))
        self.fc = nn.Linear(7*7*32, 10)
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out
cnnmodel = CNN()
#创建输出文件 output.txt
f = open(path + 'output.txt', 'w')
f.seek(0)
f.truncate()   #清空文件
#/********** Begin *********/
# 声明一个为交叉熵损失函数的变量criterion
criterion = nn.CrossEntropyLoss()
# 声明一个为Adam优化函数的变量optimizer，传入 cnn的参数，并使学习率lr为0.001
optimizer = torch.optim.Adam(cnnmodel.parameters(),lr=learning_rate)
# 训练模型
for i, (images, labels) in enumerate(train_loader):
    # 将images，labels数据转换为Variable类型
    images = Variable(images)
    labels = Variable(labels)
    # optimizer梯度归零
    optimizer.zero_grad()
    # 对 images 应用 cnnmodel 模型并赋值给变量 outputs
    outputs = cnnmodel(images)
    loss = criterion(outputs, labels)
    #Backward
    loss.backward()
    #Optimize
    optimizer.step()
    #共训练60次，分别100次输出一回loss信息，并将输出信息存到文件中
    if (i+1) % 10 == 0:
        f.writelines('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f \n'
                     %(1, num_epochs, i+1, len(train_dataset)//1000, loss.data[0]))
        print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
            %(1, num_epochs, i+1, len(train_dataset)//1000, loss.data[0]))
    if i > 60:
        break
f.close()
#/********** End *********/

第4关 测试保存模型

import torch
import torch.nn as nn
import torchvision.datasets as dsets
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
from torch.autograd import Variable
import warnings
from PIL import Image
warnings.filterwarnings('ignore')

  
import os,sys
path = os.path.split(os.path.abspath(os.path.realpath(sys.argv[0])))[0] + os.path.sep
rootpath = path[:-10]

#print("validation path:" ,root)

# MNIST Dataset
test_dataset = dsets.MNIST(root='./data/',
                           train=False,
                           transform=transforms.ToTensor(),
                           download=False)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=100,
                                          shuffle=True)

# CNN Model (2 conv layer)

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
					nn.Conv2d(1, 16, kernel_size=5, padding=2),
					nn.BatchNorm2d(16),
					nn.ReLU(),
					nn.MaxPool2d(2))
				self.layer2 = nn.Sequential(
					nn.Conv2d(16, 32, kernel_size=5,padding=2),                                    nn.BatchNorm2d(32),
					nn.ReLU(),
					nn.MaxPool2d(2))
				self.fc = nn.Linear(7*7*32, 10)

def forward(self, x):
    out = self.layer1(x)
    out = self.layer2(out)
    out = out.view(out.size(0), -1)
    out = self.fc(out)
    return out

cnnmodel = CNN()
cnnmodel = torch.load( rootpath + 'src/step3/cnnModel.pkl')

#/********** Begin *********/
# 将模型转为测试模式
cnnmodel.eval()
correct = 0
total = 0
i  = 0
for images, labels in test_loader:
    images = Variable(images)
    #对images 应用cnn模型，将结果赋值给 outputs
   outputs = cnnmodel(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()
    i += 1
    # 为了节约时间, 我们测试时只测试前10个
    if  i> 10 :
        break

#按格式输出正确率correct/total 的百分比
print('Test Accuracy of the model on the 200 test images: %d %%' % (100 * correct / total))
#/********** End *********/