首页class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5)

class Model(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(1, 20, 5)

时间: 2024-05-22 09:16:39 浏览: 156

self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(20, 50, 5) self.fc1 = nn.Linear(4*4*50, 500) self.fc2 = nn.Linear(500, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 4*4*50) x = F.relu(self.fc1(x)) x = self.fc2(x) return x This is a PyTorch model for classifying handwritten digits using a convolutional neural network. The model consists of two convolutional layers followed by two fully connected layers. The first convolutional layer has 20 filters of size 5x5 and takes in a grayscale image of size 28x28. The second convolutional layer has 50 filters of size 5x5. The output of the convolutional layers is passed through a max pooling layer with a kernel size of 2x2. The output of the max pooling layer is then flattened and passed through two fully connected layers with ReLU activation. The final output is a probability distribution over the 10 possible digit classes.

阅读全文

最新推荐

class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5)

相关推荐

Example20.class

RNN.zip_RNN_python_深度学习

pytorch 中的重要模块化接口nn.Module的使用

class Model(nn.Module): def __init__(self): super(Model, self).__init__()

class LinearModel(torch.nn.Module): def __init__(self): super(LinearModel, self).__init__() def forward(self, x): y_pred = self.linear(x) return y_pred

class linearModel(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear(1,1) def forward(self,x): y_hat = self.linear(x) return y_hat此处代码出现的两个self.linear分别是什么意思，为什么输入的参数不一样

# 自定义 MLP 块 class MLP(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(10, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.fc1(x) x = torch.relu(x) x = self.fc2(x) return x

代码为：class LogisticRegression(nn.Module): def __inti__(self,n_input_features): super(LogisticRegression,self).__inti() self.linea=nn.Linear(n_input_features,1) def forward(self,x): y_predicted=torch.sigmoid(self.linea(x)) return y_predicted model=LogisticRegression(n_feature)

最新推荐

CoreOS部署神器：configdrive_creator脚本详解

管理建模和仿真的文件

【在线考试系统设计秘籍】：掌握文档与UML图的关键步骤

如何在Verilog中实现一个参数化模块，并解释其在模块化设计中的作用与优势？

探索CCR-Studio.github.io: JavaScript的前沿实践平台

"互动学习：行动中的多样性与论文攻读经历"

三维点云里程碑：PointNet++模型完全解析及优化指南

华为GPON技术如何在光纤传输网络中实现数据高效传输和管理，并阐述其在业务发放和网络管理模式中的关键作用？

RapidMatter：Web企业架构设计即服务应用平台

关系数据表示学习

class Model(nn.Module): def init(self): super().init() self.conv1 = nn.Conv2d(1, 20, 5)

class Model(nn.Module): def init(self): super(Model, self).init()

class LinearModel(torch.nn.Module): def init(self): super(LinearModel, self).init() def forward(self, x): y_pred = self.linear(x) return y_pred

class linearModel(torch.nn.Module): def init(self): super().init() self.linear = torch.nn.Linear(1,1) def forward(self,x): y_hat = self.linear(x) return y_hat此处代码出现的两个self.linear分别是什么意思，为什么输入的参数不一样

# 自定义 MLP 块 class MLP(nn.Module): def init(self): super().init() self.fc1 = nn.Linear(10, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.fc1(x) x = torch.relu(x) x = self.fc2(x) return x

代码为：class LogisticRegression(nn.Module): def inti(self,n_input_features): super(LogisticRegression,self).__inti() self.linea=nn.Linear(n_input_features,1) def forward(self,x): y_predicted=torch.sigmoid(self.linea(x)) return y_predicted model=LogisticRegression(n_feature)