CNN-Renew
This commit is contained in:
e2hang
8
Pytorch/Project/ez0/.idea/ez0.iml
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8
Pytorch/Project/ez0/.idea/ez0.iml
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239
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|
||||
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||||
<inspection_tool class="RegExpSuspiciousBackref" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="RegExpUnexpectedAnchor" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="RegExpUnnecessaryNonCapturingGroup" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="RequiredAttributes" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="RestRoleInspection" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="SSBasedInspection" enabled="true" level="WARNING" enabled_by_default="true" />
|
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<inspection_tool class="ShellCheck" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="SpellCheckingInspection" enabled="false" level="TYPO" enabled_by_default="false">
|
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<option name="processCode" value="true" />
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||||
<option name="processLiterals" value="true" />
|
||||
<option name="processComments" value="true" />
|
||||
</inspection_tool>
|
||||
<inspection_tool class="TaskProblemsInspection" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
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<inspection_tool class="TodoComment" enabled="false" level="WARNING" enabled_by_default="false" />
|
||||
<inspection_tool class="TomlUnresolvedReference" enabled="false" level="WARNING" enabled_by_default="false" />
|
||||
<inspection_tool class="TrailingSpacesInProperty" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="UndefinedNestedVariableInspection" enabled="false" level="WARNING" enabled_by_default="false" />
|
||||
<inspection_tool class="UnresolvedReference" enabled="false" level="ERROR" enabled_by_default="false" />
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||||
<inspection_tool class="UnsatisfiedRequirementInspection" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="UnusedDefine" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="UnusedProperty" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="UseEllipsisInPropertyInspection" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="UvPackageVersionsInspection" enabled="false" level="WARNING" enabled_by_default="false" />
|
||||
<inspection_tool class="VulnerableLibrariesLocal" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="WrongPropertyKeyValueDelimiter" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
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<inspection_tool class="XmlDefaultAttributeValue" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="XmlDeprecatedElement" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="XmlDuplicatedId" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="XmlHighlighting" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="XmlInvalidId" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="XmlPathReference" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="XmlUnboundNsPrefix" enabled="false" level="WARNING" enabled_by_default="false" />
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<inspection_tool class="XmlUnresolvedReference" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="XmlUnusedNamespaceDeclaration" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="XmlWrongRootElement" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="YAMLDuplicatedKeys" enabled="false" level="ERROR" enabled_by_default="false" />
|
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<inspection_tool class="YAMLIncompatibleTypes" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="YAMLRecursiveAlias" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="YAMLSchemaDeprecation" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
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<inspection_tool class="YAMLSchemaValidation" enabled="false" level="WARNING" enabled_by_default="false" />
|
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<inspection_tool class="YAMLUnresolvedAlias" enabled="false" level="ERROR" enabled_by_default="false" />
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<inspection_tool class="YAMLUnusedAnchor" enabled="false" level="WARNING" enabled_by_default="false" />
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6
Pytorch/Project/ez0/.idea/inspectionProfiles/profiles_settings.xml
generated
Normal file
6
Pytorch/Project/ez0/.idea/inspectionProfiles/profiles_settings.xml
generated
Normal file
@@ -0,0 +1,6 @@
|
||||
<component name="InspectionProjectProfileManager">
|
||||
<settings>
|
||||
<option name="USE_PROJECT_PROFILE" value="false" />
|
||||
<version value="1.0" />
|
||||
</settings>
|
||||
</component>
|
||||
7
Pytorch/Project/ez0/.idea/misc.xml
generated
Normal file
7
Pytorch/Project/ez0/.idea/misc.xml
generated
Normal file
@@ -0,0 +1,7 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="Black">
|
||||
<option name="sdkName" value="Python 3.13" />
|
||||
</component>
|
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<component name="ProjectRootManager" version="2" project-jdk-name="eflow" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
8
Pytorch/Project/ez0/.idea/modules.xml
generated
Normal file
8
Pytorch/Project/ez0/.idea/modules.xml
generated
Normal file
@@ -0,0 +1,8 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/.idea/ez0.iml" filepath="$PROJECT_DIR$/.idea/ez0.iml" />
|
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</modules>
|
||||
</component>
|
||||
</project>
|
||||
81
Pytorch/Project/ez0/.idea/workspace.xml
generated
Normal file
81
Pytorch/Project/ez0/.idea/workspace.xml
generated
Normal file
@@ -0,0 +1,81 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="AutoImportSettings">
|
||||
<option name="autoReloadType" value="SELECTIVE" />
|
||||
</component>
|
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<component name="ChangeListManager">
|
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<list default="true" id="cf0ee316-b352-4fc4-a4fe-2ad2ea2fd96a" name="更改" comment="" />
|
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<option name="SHOW_DIALOG" value="false" />
|
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<option name="HIGHLIGHT_CONFLICTS" value="true" />
|
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<option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
|
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<option name="LAST_RESOLUTION" value="IGNORE" />
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</component>
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<component name="FileTemplateManagerImpl">
|
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<option name="RECENT_TEMPLATES">
|
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<list>
|
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<option value="Python Script" />
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</list>
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</option>
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</component>
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<component name="ProjectColorInfo">{
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"associatedIndex": 0
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}</component>
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<component name="ProjectId" id="32SE6bu8V95AvemUU03ZWUFUodr" />
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<component name="ProjectViewState">
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<option name="hideEmptyMiddlePackages" value="true" />
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<option name="showLibraryContents" value="true" />
|
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</component>
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<component name="PropertiesComponent"><![CDATA[{
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"keyToString": {
|
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"ModuleVcsDetector.initialDetectionPerformed": "true",
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||||
"Python.HandWrite.executor": "Run",
|
||||
"Python.HandWriteCNN.executor": "Run",
|
||||
"Python.MyFirstModule.executor": "Run",
|
||||
"Python.main.executor": "Run",
|
||||
"Python.noise.executor": "Run",
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"RunOnceActivity.ShowReadmeOnStart": "true",
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"settings.editor.selected.configurable": "com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable"
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}
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}]]></component>
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<component name="RunManager">
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<configuration name="main" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
|
||||
<module name="ez0" />
|
||||
<option name="ENV_FILES" value="" />
|
||||
<option name="INTERPRETER_OPTIONS" value="" />
|
||||
<option name="PARENT_ENVS" value="true" />
|
||||
<envs>
|
||||
<env name="PYTHONUNBUFFERED" value="1" />
|
||||
</envs>
|
||||
<option name="SDK_HOME" value="" />
|
||||
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
|
||||
<option name="IS_MODULE_SDK" value="true" />
|
||||
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/main.py" />
|
||||
<option name="PARAMETERS" value="" />
|
||||
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||
<option name="EMULATE_TERMINAL" value="false" />
|
||||
<option name="MODULE_MODE" value="false" />
|
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<option name="REDIRECT_INPUT" value="false" />
|
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<option name="INPUT_FILE" value="" />
|
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<method v="2" />
|
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</configuration>
|
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</component>
|
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<component name="SharedIndexes">
|
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<attachedChunks>
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<set>
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<option value="bundled-python-sdk-880ecab49056-36ea0e71a18c-com.jetbrains.pycharm.pro.sharedIndexes.bundled-PY-251.25410.122" />
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</set>
|
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</attachedChunks>
|
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</component>
|
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<component name="TaskManager">
|
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<task active="true" id="Default" summary="默认任务">
|
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<changelist id="cf0ee316-b352-4fc4-a4fe-2ad2ea2fd96a" name="更改" comment="" />
|
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<created>1757405400023</created>
|
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<option name="number" value="Default" />
|
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<option name="presentableId" value="Default" />
|
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<updated>1757405400023</updated>
|
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</task>
|
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<servers />
|
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</component>
|
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</project>
|
||||
115
Pytorch/Project/ez0/HandWriteCNN.py
Normal file
115
Pytorch/Project/ez0/HandWriteCNN.py
Normal file
@@ -0,0 +1,115 @@
|
||||
import torch
|
||||
import matplotlib.pyplot as plt
|
||||
from torch import nn
|
||||
import torch.optim as optim
|
||||
import torch.nn.functional as F
|
||||
from torchvision import datasets, transforms
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from main import optimizer
|
||||
|
||||
# 设置超参数
|
||||
batch_size = 64
|
||||
|
||||
# 定义预处理步骤
|
||||
transform = transforms.Compose([
|
||||
transforms.ToTensor(), # 转换为张量，范围 [0,1]
|
||||
transforms.Normalize((0.1307,), (0.3081,)) # 标准化：均值、方差是 MNIST 的经验值
|
||||
])
|
||||
|
||||
# 加载训练集
|
||||
train_dataset = datasets.MNIST(
|
||||
root='./data', # 数据存放路径
|
||||
train=True, # 训练集
|
||||
download=True, # 如果没有就下载
|
||||
transform=transform # 应用预处理
|
||||
)
|
||||
|
||||
# 加载测试集
|
||||
test_dataset = datasets.MNIST(
|
||||
root='./data',
|
||||
train=False, # 测试集
|
||||
download=True,
|
||||
transform=transform
|
||||
)
|
||||
|
||||
# 构建 DataLoader
|
||||
train_loader = DataLoader(
|
||||
dataset=train_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=True # 打乱数据，适合训练
|
||||
)
|
||||
|
||||
test_loader = DataLoader(
|
||||
dataset=test_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=False # 测试集不需要打乱
|
||||
)
|
||||
|
||||
# 简单测试一下
|
||||
print(f"训练集大小: {len(train_dataset)}")
|
||||
print(f"测试集大小: {len(test_dataset)}")
|
||||
|
||||
# 取一个 batch 看看形状
|
||||
images, labels = next(iter(train_loader))
|
||||
print(f"图片批次维度: {images.shape}") # [batch_size, 1, 28, 28]
|
||||
print(f"标签批次维度: {labels.shape}") # [batch_size]
|
||||
|
||||
class CNN(nn.Module):
|
||||
def __init__(self):
|
||||
super(CNN, self).__init__()
|
||||
self.c1 = nn.Conv2d(1, 16, 3, padding=1)
|
||||
self.c2 = nn.Conv2d(16, 32, 3, padding=1)
|
||||
self.c3 = nn.Conv2d(32, 64, 3, padding=1)
|
||||
self.pool = nn.MaxPool2d(2, 2)
|
||||
self.linear0 = nn.Linear(64 * 7 * 7, 128) # 注意这里是14*14，如果只池化一次，池化一次减半
|
||||
self.linear1 = nn.Linear(128, 64)
|
||||
self.linear2 = nn.Linear(64, 32)
|
||||
self.linear3 = nn.Linear(32, 10)
|
||||
self.drop = nn.Dropout(p=0.31) # 丢弃概率
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.c1(x))
|
||||
x = self.pool(F.relu(self.c2(x))) # [batch,32,14,14] → pool → [batch,32,7,7]
|
||||
x = self.pool(F.relu(self.c3(x)))
|
||||
x = x.view(x.size(0), -1) # flatten
|
||||
x = F.relu(self.linear0(x))
|
||||
x = F.relu(self.linear1(x))
|
||||
x = self.drop(x)
|
||||
x = F.relu(self.linear2(x))
|
||||
x = self.linear3(x)
|
||||
return x
|
||||
|
||||
ez = CNN()
|
||||
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.AdamW(ez.parameters(), lr=0.001)
|
||||
|
||||
for i in range(10):
|
||||
ez.train()
|
||||
for images, labels in train_loader:
|
||||
out = ez(images)
|
||||
loss = criterion(out, labels)
|
||||
#反向传播
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
print(f"<UNK>: {loss}")
|
||||
|
||||
#训练结束
|
||||
ez.eval() # 关闭 dropout/batchnorm 等训练特性
|
||||
|
||||
correct = 0
|
||||
total = 0
|
||||
|
||||
with torch.no_grad(): # 测试不需要计算梯度，节省显存
|
||||
for images, labels in test_loader:
|
||||
outputs = ez(images) # [batch_size, 10]
|
||||
|
||||
# 取每行最大值对应的索引作为预测类别
|
||||
_, predicted = torch.max(outputs, 1)
|
||||
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
|
||||
print(f"测试集准确率: {correct}/{total} = {correct/total*100:.2f}%")
|
||||
111
Pytorch/Project/ez0/HandWriteMLP.py
Normal file
111
Pytorch/Project/ez0/HandWriteMLP.py
Normal file
@@ -0,0 +1,111 @@
|
||||
import torch
|
||||
import matplotlib.pyplot as plt
|
||||
from torch import nn
|
||||
import torch.optim as optim
|
||||
from torchvision import datasets, transforms
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from main import optimizer
|
||||
|
||||
# 设置超参数
|
||||
batch_size = 64
|
||||
|
||||
# 定义预处理步骤
|
||||
transform = transforms.Compose([
|
||||
transforms.ToTensor(), # 转换为张量，范围 [0,1]
|
||||
transforms.Normalize((0.1307,), (0.3081,)) # 标准化：均值、方差是 MNIST 的经验值
|
||||
])
|
||||
|
||||
# 加载训练集
|
||||
train_dataset = datasets.MNIST(
|
||||
root='./data', # 数据存放路径
|
||||
train=True, # 训练集
|
||||
download=True, # 如果没有就下载
|
||||
transform=transform # 应用预处理
|
||||
)
|
||||
|
||||
# 加载测试集
|
||||
test_dataset = datasets.MNIST(
|
||||
root='./data',
|
||||
train=False, # 测试集
|
||||
download=True,
|
||||
transform=transform
|
||||
)
|
||||
|
||||
# 构建 DataLoader
|
||||
train_loader = DataLoader(
|
||||
dataset=train_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=True # 打乱数据，适合训练
|
||||
)
|
||||
|
||||
test_loader = DataLoader(
|
||||
dataset=test_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=False # 测试集不需要打乱
|
||||
)
|
||||
|
||||
# 简单测试一下
|
||||
print(f"训练集大小: {len(train_dataset)}")
|
||||
print(f"测试集大小: {len(test_dataset)}")
|
||||
|
||||
# 取一个 batch 看看形状
|
||||
images, labels = next(iter(train_loader))
|
||||
print(f"图片批次维度: {images.shape}") # [batch_size, 1, 28, 28]
|
||||
print(f"标签批次维度: {labels.shape}") # [batch_size]
|
||||
|
||||
# 从训练集中取一个 batch
|
||||
images, labels = next(iter(train_loader))
|
||||
'''
|
||||
# 画前 9 张图
|
||||
fig, axes = plt.subplots(3, 3, figsize=(6, 6))
|
||||
for i, ax in enumerate(axes.flat):
|
||||
img = images[i].squeeze().numpy() # [1,28,28] -> [28,28]
|
||||
label = labels[i].item()
|
||||
ax.imshow(img, cmap="gray")
|
||||
ax.set_title(f"Label: {label}")
|
||||
ax.axis("off")
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
'''
|
||||
|
||||
ez = nn.Sequential(
|
||||
nn.Linear(28 * 28, 256),
|
||||
nn.ReLU(),
|
||||
nn.Linear(256, 100),
|
||||
nn.ReLU(),
|
||||
nn.Linear(100, 10),
|
||||
)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
|
||||
optimizer = optim.Adam(ez.parameters(), lr=0.002)
|
||||
|
||||
for images, labels in train_loader:
|
||||
images = images.view(images.size(0), -1) # [batch_size, 28*28]
|
||||
out = ez(images)
|
||||
loss = criterion(out, labels)
|
||||
#反向传播
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
print(f"<UNK>: {loss}")
|
||||
|
||||
#训练结束
|
||||
ez.eval() # 关闭 dropout/batchnorm 等训练特性
|
||||
|
||||
correct = 0
|
||||
total = 0
|
||||
|
||||
with torch.no_grad(): # 测试不需要计算梯度，节省显存
|
||||
for images, labels in test_loader:
|
||||
images = images.view(images.size(0), -1) # flatten
|
||||
outputs = ez(images) # [batch_size, 10]
|
||||
|
||||
# 取每行最大值对应的索引作为预测类别
|
||||
_, predicted = torch.max(outputs, 1)
|
||||
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
|
||||
print(f"测试集准确率: {correct}/{total} = {correct/total*100:.2f}%")
|
||||
49
Pytorch/Project/ez0/MyFirstModule.py
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49
Pytorch/Project/ez0/MyFirstModule.py
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@@ -0,0 +1,49 @@
|
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import torch
|
||||
import torch.nn as nn
|
||||
|
||||
ez = nn.Sequential(
|
||||
nn.Linear(2, 8),
|
||||
nn.ReLU(),
|
||||
nn.Linear(8, 8),
|
||||
nn.ReLU(),
|
||||
nn.Linear(8, 1)
|
||||
)
|
||||
# y = w1 @ x + w2 @ x^2 + b
|
||||
# 两个特征，第一个是x，第二个是x^2
|
||||
criterion = nn.MSELoss()
|
||||
|
||||
x = torch.linspace(-10, 10, 10000).unsqueeze(1)
|
||||
X = torch.cat([x, x**2], dim=1)
|
||||
y = x**2 + 2 * x + 1
|
||||
|
||||
inx = torch.tensor([[2, 4], [1, 1], [3, 9], [4, 16]], dtype=torch.float32)
|
||||
target = torch.tensor([[9], [4], [16], [25]], dtype=torch.float32)
|
||||
|
||||
lr = 0.00001
|
||||
for i in range(10000):
|
||||
#向前传播
|
||||
out = ez(X)
|
||||
loss = criterion(out, y)
|
||||
|
||||
#反向传播
|
||||
loss.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
# 3. 手动更新参数
|
||||
with torch.no_grad(): # 禁止 autograd 追踪
|
||||
for param in ez.parameters():
|
||||
param -= lr * param.grad # 梯度下降更新参数
|
||||
|
||||
#梯度清零
|
||||
ez.zero_grad()
|
||||
|
||||
if i % 1000 == 0:
|
||||
print(f"Epoch {i}: loss={loss.item():.4f}")
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
y_pred = ez(X).detach()
|
||||
plt.scatter(x.numpy(), x.numpy()**2 + 2*x.numpy() + 1, label='real y = x^2 + 2x + 1')
|
||||
plt.plot(x.numpy(), y_pred.numpy(), color='r', label='Predicted y = x^2 + 2x + 1')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
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26
Pytorch/Project/ez0/main.py
Normal file
26
Pytorch/Project/ez0/main.py
Normal file
@@ -0,0 +1,26 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
|
||||
# 1. 定义网络
|
||||
model = nn.Sequential(
|
||||
nn.Linear(10, 20), # 输入10维 → 输出20维
|
||||
nn.ReLU(),
|
||||
nn.Linear(20, 2) # 输出2维（比如2分类）
|
||||
)
|
||||
|
||||
# 2. 定义损失函数 & 优化器
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(model.parameters(), lr=0.01)
|
||||
|
||||
# 3. 模拟训练一步
|
||||
x = torch.randn(5, 10) # batch=5, feature=10
|
||||
y = torch.tensor([0, 1, 0, 1, 1]) # 分类标签
|
||||
|
||||
out = model(x) # forward
|
||||
loss = criterion(out, y) # loss
|
||||
optimizer.zero_grad() # 梯度清零
|
||||
loss.backward() # backward
|
||||
optimizer.step() # 更新参数
|
||||
|
||||
print("loss =", loss.item())
|
||||
55
Pytorch/Project/ez0/noise.py
Normal file
55
Pytorch/Project/ez0/noise.py
Normal file
@@ -0,0 +1,55 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from torch import optim
|
||||
|
||||
# x 张量
|
||||
x = torch.linspace(-10, 10, 100).unsqueeze(1)
|
||||
|
||||
# 特征矩阵
|
||||
X = torch.cat([x, x**2, x**3, torch.sin(2*x)], dim=1)
|
||||
|
||||
# 高斯噪声
|
||||
noise = torch.from_numpy(np.random.normal(0, 3, size=x.shape)).float()
|
||||
|
||||
# 带噪声的 y
|
||||
y_noisy = 0.5 * x**3 - 2 * x**2 + 3*x + 5 + 4 * torch.sin(2*x) + noise
|
||||
|
||||
# 可视化
|
||||
'''
|
||||
plt.scatter(x, y_noisy, label="Noisy data")
|
||||
plt.plot(x, 0.5 * x**3 - 2 * x**2 + 3*x + 5 + 4 * torch.sin(2*x), color='red', label="Original function")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
'''
|
||||
ez = nn.Sequential(
|
||||
nn.Linear(4, 8),
|
||||
nn.ReLU(),
|
||||
nn.Linear(8, 4),
|
||||
nn.ReLU(),
|
||||
nn.Linear(4, 1),
|
||||
)
|
||||
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = optim.Adam(ez.parameters(), lr=0.0065)
|
||||
|
||||
for i in range(10000):
|
||||
#向前传播
|
||||
out = ez(X)
|
||||
loss = criterion(out, y_noisy)
|
||||
#反向传播
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
if i % 100 == 0:
|
||||
print("Step:", i, "loss =", loss.item())
|
||||
|
||||
with torch.no_grad():
|
||||
y_pred = ez(X)
|
||||
|
||||
plt.scatter(x.numpy(), y_noisy.numpy(), alpha=0.3, label="Noisy data")
|
||||
plt.plot(x.numpy(), y_pred.numpy(), color='red', label="NN prediction")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
Reference in New Issue
Block a user