featureInputLayer:特征输入层,R2020b fullyConnectedLayer:全连接层,R2016a reluLayer或 leakyReluLayer :激活函数,R2017b dlnetwork:深度网络构建函数,R2019b adamupdate:adam 优化器,R2019b dlfeval:网络评估函数,R2019b forward:计算网络的输出,R2019b extractdata:从 dlarray 中提取数据,R2019b gather:将数据...
softmaxLayer('Name','sm') classificationLayer('Name','classification')] I am not sure why I am getting the below error Unable to resolve the name nnet.internal.cnn.layer.FeatureInput. Error in featureInputLayer (line 99) internalLayer = nnet.internal.cnn.layer.FeatureInput(... It w...
featureInputLayer(oinfo.Dimension(1),'Normalization','none','Name','observation') fullyConnectedLayer(128,'Name','ActorFC1','WeightsInitializer','he') reluLayer('Name','ActorRelu1') fullyConnectedLayer(64,'Name','ActorFC2','WeightsInitializer','he') reluLayer('Name','ActorRelu2') fully...
featureInputLayer(100,'Name','input') convolution1dLayer(50, 3,'Stride',1,'Name','cnn1d') fullyConnectedLayer(5,'Name','fc') softmaxLayer('Name','sm') classificationLayer('Name','classification')] layers2 = 5×1Layerarray with layers: 1 'input' Feature Input 100 features 2 'cnn1d...
ActionInputNames="netAin"); % 调用getValue,输入随机观察值,检查评价者输出 getValue(critic,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)}) % 创建一个深度网络用来做行动者逼近器create a network to be used as underlying actor approximator aNet = [ featureInputLayer(prod(obsInfo.Dimension)...
featureInputLayer(numAct,'Normalization','none','Name','action') fullyConnectedLayer(hiddenLayerSize,'Name','fc5')]; % Create the layer graph. criticNetwork = layerGraph(observationPath); criticNetwork = addLayers(criticNetwork,actionPath); ...
I have the feature arrays stored in a structure array. Features and labels in two different fields. Can anyone suggest how the data should be saved to train the network with 'featureInputLayer' as the first layer? Also, is there any easy way to distribute the data in training and testing...
I understand youwant to know on how to usefeature input layer and image input layer. To train a networkcontainingboth an image input layer and a feature input layer, you must use a“dlnetwork”object in a custom training loop. To create anynetwork wit...
featureInputLayer(obsInfo.Dimension(1),'Normalization','none','Name','state') fullyConnectedLayer(24,'Name','CriticStateFC1') reluLayer('Name','CriticRelu1') fullyConnectedLayer(24,'Name','CriticStateFC2') reluLayer('Name','CriticCommonRelu') ...
imageInputLayer([28 28 1]) convolution2dLayer(3,8,'Padding','same') batchNormalizationLayer reluLayer maxPooling2dLayer(2,'Stride',2) convolution2dLayer(3,16,'Padding','same') batchNormalizationLayer reluLayer maxPooling2dLayer(2,'Stride',2) ...