检测cuda可用性

如可用则将默认张量位置设置为cuda，此处为pytorch 2.1.0 推荐写法

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if torch.cuda.is_available():
    torch.set_default_dtype(torch.float)
    torch.set_default_device("cuda")
    print("using cuda:",torch.cuda.get_device_name())

device=torch.device("cuda" if torch.cuda.is_available() else "cpu")

保存和读取模型参数

注意在读取时模型对象应由原模型同一类派生

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torch.save(C1.state_dict(),"models/mnist97_state_dict.pth")
C2=mnist_classifier.Classifier()
C2.load_state_dict(torch.load("models/mnist97_state_dict.pth",map_location=device))

mnist_classifier 模型和数据处理类示例

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import torch
import torch.nn as nn
from torch.utils.data import Dataset

import pandas
import matplotlib.pyplot as plt

class Classifier(nn.Module):

    def __init__(self):
        super().__init__()

        self.model=nn.Sequential(
            nn.Linear(784,200), # 全连接层连接
            nn.LeakyReLU(0.02), # 带泄漏线性整流函数
            nn.LayerNorm(200), # 标准化
            nn.Linear(200,10),
            nn.Sigmoid() # Sigmoid激活函数
        )

        self.loss_function=nn.BCELoss() # 二元交叉熵损失函数

        self.optimiser=torch.optim.Adam(self.parameters()) # Adam优化器

        self.counter=0
        self.progress=[]

    def forward(self,inputs):
        return self.model(inputs)
    
    def train(self,inputs,targets):
        outputs=self.forward(inputs)
        loss=self.loss_function(outputs,targets)

        self.counter+=1

        if(self.counter%10==0):
            self.progress.append(loss.item())

        if(self.counter%10000==0):
            print("counter = ",self.counter)

        self.optimiser.zero_grad()
        loss.backward()
        self.optimiser.step()

    def plot_progress(self):
        df=pandas.DataFrame(self.progress,columns=['loss'])
        df.plot(ylim=(0,1.0),figsize=(16,8),alpha=0.1,marker='.',grid=True,yticks=(0,0.25,0.5))



class MnistDataset(Dataset):
    def __init__(self,csv_file):
        self.data_df=pandas.read_csv(csv_file,header=None)

    def __len__(self):
        return len(self.data_df)
    
    def __getitem__(self,index):
        label = self.data_df.iloc[index,0]
        target=torch.zeros((10))
        target[label]=1.0

        image_values=torch.cuda.FloatTensor(self.data_df.iloc[index,1:].values)/255.0

        return label,image_values,target
    
    def plot_image(self,index):
        arr=self.data_df.iloc[index,1:].values.reshape(28,28)
        plt.title('label = '+str(self.data_df.iloc[index,0]))
        plt.imshow(arr,interpolation='None',cmap='Blues')

mnist_classifier 训练代码示例

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# 读取训练数据集并展示
mnist_dataset=MnistDataset('mnist_data/mnist_train.csv')
mnist_dataset.plot_image(9)

# 创建分类器实例并测试参数传递
C=Classifier()
C.to(device)
C.forward(torch.rand(784))

# 训练过程
epochs=3

for i in range(epochs):
    print('train epoch',i+1,'of',epochs)
    for label,image_data_tensor,target_tensor in mnist_dataset:
        C.train(image_data_tensor,target_tensor)

C.plot_progress() # 展示训练过程

# 加载验证数据集
mnist_test_dataset = MnistDataset('mnist_data/mnist_test.csv')

# 展示图像
record = 19
mnist_test_dataset.plot_image(record)

# 测试分类器
image_data = mnist_test_dataset[record][1]

print(image_data.shape)

output = C.forward(image_data)

pandas.DataFrame(output.cpu().detach().numpy()).plot(kind='bar', legend=False, ylim=(0,1))

# 测试识别精度
score = 0
items = 0

for label, image_data_tensor, target_tensor in mnist_test_dataset:
    answer = C.forward(image_data_tensor).cpu().detach().numpy()
    if (answer.argmax() == label):
        score += 1
        pass
    items += 1
    
    pass

print(score, items, score/items)

# 保存模型参数
torch.save(C.state_dict(),"models/mnist97_state_dict.pth")

加载并使用mnist_classifier代码示例

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import torch
from torch.utils.data import Dataset
import pandas
import matplotlib.pyplot as plt
import numpy as np

import mnist_classifier # 导入之前mnist_classifier的定义文件

if torch.cuda.is_available():
    torch.set_default_dtype(torch.float)
    torch.set_default_device("cuda")
    print("using cuda:",torch.cuda.get_device_name())

device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

C=mnist_classifier.Classifier() # 创建分类器实例

# 加载之前保存的模型参数，加载到device上
C.load_state_dict(torch.load("models/mnist97_state_dict.pth",map_location=device))
print(C)
print(next(C.parameters()).device)

# 导入验证数据集并进行推理
mnist_test_dataset=mnist_classifier.MnistDataset("mnist_data/mnist_test.csv")

def classify_image(idx):
    mnist_test_dataset.plot_image(idx)
    test_image=mnist_test_dataset[idx][1]
    output=C.forward(test_image)
    pandas.DataFrame(output.cpu().detach().numpy()).plot(kind='bar',legend=False,ylim=(0,1))
    print("模型认为此数字为 ",end='')
    print(int(np.argmax(output.cpu().detach().numpy())))

classify_image(78)

mnist_cnn 参考代码

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import torch
import torch.nn as nn
from torch.utils.data import Dataset

import pandas
import matplotlib.pyplot as plt

# 展开成一维张量
class View(nn.Module):
    def __init__(self, shape):
        super().__init__()
        self.shape = shape,

    def forward(self, x):
        return x.view(*self.shape)
    
class MnistDataset(Dataset):
    
    def __init__(self, csv_file):
        self.data_df = pandas.read_csv(csv_file, header=None)
    
    def __len__(self):
        return len(self.data_df)
    
    def __getitem__(self, index):
        # 生成标签对应的独热标签
        label = self.data_df.iloc[index,0]
        target = torch.zeros((10))
        target[label] = 1.0
        
        # 读取图像数据并作正规化
        image_values = torch.tensor(self.data_df.iloc[index,1:].values,dtype=torch.float) / 255.0
        
        # 返回 类别 图像数据 独热标签 
        return label, image_values, target
    
    def plot_image(self, index):
        img = self.data_df.iloc[index,1:].values.reshape(28,28)
        plt.title("label = " + str(self.data_df.iloc[index,0]))
        plt.imshow(img, interpolation='none', cmap='Blues')

class Classifier(nn.Module):
    
    def __init__(self):

        super().__init__()
        
        # 定义神经网络层结构
        self.model = nn.Sequential(
            # 1 --> 10 通道
            nn.Conv2d(1, 10, kernel_size=5, stride=2), # out: 12*12
            nn.LeakyReLU(0.02),
            nn.BatchNorm2d(10),
        
            nn.Conv2d(10, 10, kernel_size=3, stride=2), # out: 5*5
            nn.LeakyReLU(0.02),
            nn.BatchNorm2d(10),
            
            View(250),
            nn.Linear(250, 10),
            nn.Sigmoid()
        )
        
        # 指定损失函数
        self.loss_function = nn.BCELoss()

        # 指定随机梯度下降优化器
        self.optimiser = torch.optim.Adam(self.parameters())

        # 训练计数器和损失函数值记录
        self.counter = 0
        self.progress = []
    
    
    def forward(self, inputs):
        return self.model(inputs)
    
    
    def train(self, inputs, targets):

        outputs = self.forward(inputs)
        

        loss = self.loss_function(outputs, targets)

        self.counter += 1
        if (self.counter % 10 == 0):
            self.progress.append(loss.item())
            pass
        if (self.counter % 10000 == 0):
            print("counter = ", self.counter)
            pass

        self.optimiser.zero_grad()
        loss.backward()
        self.optimiser.step()  

    def plot_progress(self):
        df = pandas.DataFrame(self.progress, columns=['loss'])
        df.plot(ylim=(0, 1.0), figsize=(16,8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5))