matlab代码 batchNormalizationLayer;
时间: 2024-06-04 16:12:37 浏览: 4
以下是batchNormalizationLayer的matlab代码示例:
% 创建batchNormalizationLayer
batchnorm = batchNormalizationLayer();
% 为batchnorm设置参数
batchnorm.Epsilon = 0.001;
% 将batchnorm添加到网络层中
layers = [
imageInputLayer([28 28 1])
convolution2dLayer(5,20)
batchnorm
reluLayer()
maxPooling2dLayer(2,'Stride',2)
fullyConnectedLayer(10)
softmaxLayer()
classificationLayer()];
% 训练神经网络
net = trainNetwork(trainData,layers,options);
相关问题
resnet matlab 代码
您好!以下是一个简单的 MATLAB 实现 ResNet 的代码示例:
```matlab
classdef ResidualBlock < nnet.layer.Layer
properties
ConvolutionLayer
BatchNormalizationLayer
end
methods
function layer = ResidualBlock(name, numFilters, bottleneckFactor)
layer.Name = name;
layer.ConvolutionLayer = convolution2dLayer(1, numFilters * bottleneckFactor, ...
'Stride', 1, 'Padding', 0, 'Bias', false, 'Name', [name '/conv1']);
layer.BatchNormalizationLayer = batchNormalizationLayer('Name', [name '/bn1']);
end
function Z = predict(layer, X)
Y = layer.ConvolutionLayer.predict(X);
Y = layer.BatchNormalizationLayer.predict(Y);
Z = relu(Y);
end
end
end
layers = [
imageInputLayer([32 32 3], 'Name', 'input')
convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1')
batchNormalizationLayer('Name', 'bn1')
reluLayer('Name', 'relu1')
maxPooling2dLayer(3, 'Stride', 2, 'Padding', 'same', 'Name', 'maxpool1')
ResidualBlock('resblock1a', 64, 1)
ResidualBlock('resblock1b', 64, 1)
ResidualBlock('resblock1c', 64, 1)
averagePooling2dLayer(8, 'Name', 'avgpool')
fullyConnectedLayer(10, 'Name', 'fc')
softmaxLayer('Name', 'softmax')
classificationLayer('Name', 'output')
];
lgraph = layerGraph(layers);
plot(lgraph)
```
这段代码实现了一个简单的 ResNet 网络结构,包括一个输入层、卷积层、批归一化层、ReLU 层、池化层、残差块、全连接层和输出层。您可以根据自己的需求进行修改和扩展。
请注意,这只是一个示例,并非完整的 ResNet 实现。如果您想要更复杂的网络结构或其他特性,请根据具体需求进行相应的修改和调整。希望对您有所帮助!
残差网络matlab代码
以下是一个简单的残差网络的 MATLAB 代码示例:
```matlab
% 定义网络结构
layers = [
imageInputLayer([32 32 3])
convolution2dLayer(3, 32, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 32, 'Padding', 'same')
batchNormalizationLayer
additionLayer(2)
reluLayer
convolution2dLayer(3, 64, 'Stride', 2, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 64, 'Padding', 'same')
batchNormalizationLayer
convolution2dLayer(1, 64, 'Stride', 2)
batchNormalizationLayer
additionLayer(2)
reluLayer
fullyConnectedLayer(10)
softmaxLayer
classificationLayer];
% 定义训练选项
options = trainingOptions('sgdm', ...
'MaxEpochs', 20, ...
'MiniBatchSize', 128, ...
'Shuffle', 'every-epoch', ...
'Verbose', false, ...
'Plots', 'training-progress');
% 加载数据集
[XTrain, YTrain] = cifar10TrainData;
% 训练网络
net = trainNetwork(XTrain, YTrain, layers, options);
```
这个代码示例创建了一个简单的残差网络,用于 CIFAR-10 数据集的图像分类。在网络中,添加了残差块以增加网络深度。训练使用了随机梯度下降(SGD)优化器,并使用了数据增强来增加数据集的多样性。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![m](https://img-home.csdnimg.cn/images/20210720083646.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)