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作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/.idea/inspectionProfiles/Project_Default.xml

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+  <profile version="1.0">
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+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
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作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
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作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/卷积神经网络训练--每行代码注释版本.iml" filepath="$PROJECT_DIR$/.idea/卷积神经网络训练--每行代码注释版本.iml" />
+    </modules>
+  </component>
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/.idea/卷积神经网络训练--每行代码注释版本.iml

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+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/conv_demo.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/encodings.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/lenet5.pt" charset="UTF-8" />
+  </component>
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/inspectionProfiles/Project_Default.xml

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+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="14">
+            <item index="0" class="java.lang.String" itemvalue="opencv-contrib-python" />
+            <item index="1" class="java.lang.String" itemvalue="networkx" />
+            <item index="2" class="java.lang.String" itemvalue="opencv-python" />
+            <item index="3" class="java.lang.String" itemvalue="PyQt5-sip" />
+            <item index="4" class="java.lang.String" itemvalue="torch" />
+            <item index="5" class="java.lang.String" itemvalue="MarkupSafe" />
+            <item index="6" class="java.lang.String" itemvalue="numpy" />
+            <item index="7" class="java.lang.String" itemvalue="Jinja2" />
+            <item index="8" class="java.lang.String" itemvalue="PyQt5-Qt5" />
+            <item index="9" class="java.lang.String" itemvalue="filelock" />
+            <item index="10" class="java.lang.String" itemvalue="mpmath" />
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+            <item index="12" class="java.lang.String" itemvalue="typing_extensions" />
+            <item index="13" class="java.lang.String" itemvalue="sympy" />
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+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/conv_demo.iml" filepath="$PROJECT_DIR$/.idea/conv_demo.iml" />
+    </modules>
+  </component>
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../.." vcs="Git" />
+  </component>
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/ds.py

@@ -0,0 +1,35 @@
+import torchvision
+import torch
+import numpy as np
+import cv2
+
+from  torchvision.datasets import MNIST
+from  torchvision.transforms import Compose, ToTensor
+
+transform = Compose([ToTensor()])
+
+# 加载数据集
+ds_mnist = MNIST(root="datasets", train=True, download=True, transform=transform)
+
+# print(len(ds_mnist))
+# print(ds_mnist[0][0].shape)
+# print(ds_mnist[0][1])
+
+# 保存图像
+# 取图像
+
+for i in range(10):
+    data, target = ds_mnist[i]
+
+    # 值变换为0-255
+    img = data.mul(255)
+    # 转换为numpy
+    img = img.numpy().copy()
+    # 转为uint8
+    img = img.astype(np.uint8)
+    # 转换通道
+
+    img = img.transpose(1, 2, 0)
+
+    cv2.imwrite(f"{i:02d}_{target}.jpg", img)
+

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/lenet5.py

@@ -0,0 +1,58 @@
+from torch.nn import Module
+from torch.nn import Conv2d, Linear
+from torch.nn.functional import relu, max_pool2d
+import torch
+import torch.nn as nn
+class Lenet5(Module):
+    def __init__(self):
+        super(Lenet5, self).__init__()
+        # 第一层卷积层，输入通道为1，输出通道为6，卷积核大小为5
+        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
+        # 最大池化层，池化核大小为2，步长为2
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        # 第二层卷积层，输入通道为6，输出通道为16，卷积核大小为5
+        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
+        # 全连接层，输入节点数为16*4*4，输出节点数为120
+        self.fc1 = nn.Linear(16 * 4 * 4, 120)
+        # 全连接层，输入节点数为120，输出节点数为84
+        self.fc2 = nn.Linear(120, 84)
+        # 输出层，输入节点数为84，输出节点数为10
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        # 第一层卷积，通过relu激活函数
+        x = self.pool(relu(self.conv1(x)))
+        # 第二层卷积，通过relu激活函数
+        x = self.pool(relu(self.conv2(x)))
+        # 展开张量，将其变成一维向量
+        x = x.view(-1, 16 * 4 * 4)
+        # 全连接层，通过relu激活函数
+        x = relu(self.fc1(x))
+        # 全连接层，通过relu激活函数
+        x = relu(self.fc2(x))
+        # 输出层，不使用激活函数
+        x = self.fc3(x)
+        return x
+
+
+
+# # 加载训练数据和测试数据
+# transform = transforms.Compose([
+#     transforms.ToTensor(),  # 转换为Tensor对象
+#     transforms.Normalize((0.5,), (0.5,))  # 归一化处理
+# ])
+# trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
+# trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
+# testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
+# testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
+#
+# # 实例化LeNet-5模型和损失函数、优化器
+# net = LeNet5()
+# criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
+# optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  # 随机梯度下降优化器
+#
+# # 训练网络
+# for epoch in range(10):
+#     running_loss = 0.0
+#     for i, data in enumerate(trainloader, 0):
+#         inputs, labels = data

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/train.py

@@ -0,0 +1,102 @@
+from lenet5 import Lenet5
+from torch.nn import CrossEntropyLoss
+from torch.optim import Adam
+from torchvision.datasets import MNIST
+from torchvision.transforms import Compose, ToTensor
+from torch.utils.data import DataLoader
+import torch
+import os
+
+class Lenet5Trainer:
+    def __init__(self, lr=0.0001, model_file="lenet5.pt", ds_path="datasets"):
+        """
+            lr:学习率
+            model_file:保存的模型文件
+            ds_path:数据集目录
+        """
+        super(Lenet5Trainer, self).__init__()
+        # GPU运算
+        self.CUDA = torch.cuda.is_available()
+        self.lr = lr
+        self.model_file = model_file
+        self.ds_path = ds_path 
+        # 神经网络模型
+        self.net = Lenet5()
+        if self.CUDA:
+            self.net.cuda()
+        
+        # 判定模型文件是否存在
+        if os.path.exists(self.model_file):
+            print("模型存在")
+            state = torch.load(self.model_file, map_location='cpu')
+            self.net.load_state_dict(state)
+        else:
+            print("模型不存在，从头训练")
+        # 损失函数
+        self.loss_f = CrossEntropyLoss()
+        # 优化器
+        self.optimizer = Adam(self.net.parameters(), lr=self.lr) 
+        # 数据集的初始化
+        self.transform = Compose([ToTensor()])
+        self.ds_train = MNIST(self.ds_path, download=True, train=True, transform=self.transform)
+        self.ds_valid = MNIST(self.ds_path, download=True, train=False, transform=self.transform)
+        # 批次数据集
+        self.loader_train = DataLoader(self.ds_train, shuffle=True, batch_size=1000)
+        self.loader_valid = DataLoader(self.ds_valid, shuffle=True, batch_size=1000)
+
+
+    def train_one(self):
+        for x,  y in self.loader_train:
+            if self.CUDA:
+                x = x.cuda()
+                y = y.cuda()
+            # 计算预测输出
+            y_ = self.net(x)
+            # 计算损失
+            loss = self.loss_f(y_, y)   # 单热编码one-hot
+            # 求导
+            self.optimizer.zero_grad()
+            loss.backward()
+            # 梯度更新
+            self.optimizer.step()
+
+    @torch.no_grad()
+    def valid(self):
+        all_num = 0.0
+        acc_num = 0.0
+        all_loss = 0.0
+        for t_x, t_y in self.loader_valid:
+            if self.CUDA:
+                t_x = t_x.cuda()
+                t_y = t_y.cuda()
+            all_num += len(t_y) # 累计所有批次的总数
+            t_y_ = self.net(t_x) # 批次预测
+            # 累计计算损失
+            all_loss += self.loss_f(t_y_, t_y)
+            # 统计识别正确数
+            prob = torch.softmax(t_y_, dim=1)
+            y_cls = torch.argmax(prob, dim=1)
+            # 统计
+            acc_num += (y_cls == t_y).float().sum()
+        
+        # 输出
+        print(F"测试集损失：{all_loss:8.6f}")
+        print(F"\t|-测试集识别正确率：{100.0 * acc_num / all_num:5.2f}%")
+
+
+
+    def train(self, epoch, interval=1):
+        # 迭代训练
+        for e in range(epoch):
+            # 训练一轮
+            self.train_one()
+            # 验证判定
+            if e % interval == 0:
+                # 验证
+                self.valid()
+                # 保存模型
+                torch.save(self.net.state_dict(), self.model_file)
+                 
+if __name__ == "__main__":
+    trainer = Lenet5Trainer()
+    trainer.train(5)

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络训练/卷积神经网络训练/train_new.py

@@ -0,0 +1,61 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+
+# 定义LeNet-5模型
+class LeNet5(nn.Module):
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        # 第一层卷积层，输入通道为1，输出通道为6，卷积核大小为5
+        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
+        # 最大池化层，池化核大小为2，步长为2
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        # 第二层卷积层，输入通道为6，输出通道为16，卷积核大小为5
+        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
+        # 全连接层，输入节点数为16*4*4，输出节点数为120
+        self.fc1 = nn.Linear(16 * 4 * 4, 120)
+        # 全连接层，输入节点数为120，输出节点数为84
+        self.fc2 = nn.Linear(120, 84)
+        # 输出层，输入节点数为84，输出节点数为10
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        # 第一层卷积，通过relu激活函数
+        x = self.pool(torch.relu(self.conv1(x)))
+        # 第二层卷积，通过relu激活函数
+        x = self.pool(torch.relu(self.conv2(x)))
+        # 展开张量，将其变成一维向量
+        x = x.view(-1, 16 * 4 * 4)
+        # 全连接层，通过relu激活函数
+        x = torch.relu(self.fc1(x))
+        # 全连接层，通过relu激活函数
+        x = torch.relu(self.fc2(x))
+        # 输出层，不使用激活函数
+        x = self.fc3(x)
+        return x
+
+
+# 加载训练数据和测试数据
+transform = transforms.Compose([
+    transforms.ToTensor(),  # 转换为Tensor对象
+    transforms.Normalize((0.5,), (0.5,))  # 归一化处理
+])
+trainset = torchvision.datasets.MNIST(root='./datasets', train=True, download=True, transform=transform)
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
+testset = torchvision.datasets.MNIST(root='./datasets', train=False, download=True, transform=transform)
+testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
+
+# 实例化LeNet-5模型和损失函数、优化器
+net = LeNet5()
+criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
+optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  # 随机梯度下降优化器
+
+
+# 训练网络
+for epoch in range(10):
+    running_loss = 0.0
+    for i, data in enumerate(trainloader, 0):
+        inputs, labels = data

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络预测/.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络预测/.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+  </profile>
+</component>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络预测/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络预测/.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络预测/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
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+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/卷积神经网络预测.iml" filepath="$PROJECT_DIR$/.idea/卷积神经网络预测.iml" />
+    </modules>
+  </component>
+</project>

作业/20200404107郭林杰/Day6 20200404107郭林杰/卷积神经网络预测/.idea/卷积神经网络预测.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>