Mean and Standard Deviation of outputs on a neural network

8 次查看（过去 30 天）
Vasu Sharma
Vasu Sharma 2024-7-31
评论： Vasu Sharma 2024-8-1
Ran in:
I am trying to train a Bayesian neural network, and with 5 inputs and 4 outputs. In the end, I want to have a mean prediction for all the outputs and a estimate of the standard deviation. When I ran the following code, it says that the network must have an output layer. I am wondering whats incorrect. I have followed this example.
PS: I have updated the code and am using trainnet now. The error that comes now is that :Fixed sequence length training option is not supported: in Trainnet.
My utrain_load has a size 5*78739 and ytrain_load has a size 4*78739.
addpath('~\MATLAB\Examples\R2024a\nnet\TrainBayesianNeuralNetworkUsingBayesByBackpropExample')
Warning: Name is nonexistent or not a directory: /users/mss.system.wjdqm/~\MATLAB\Examples\R2024a\nnet\TrainBayesianNeuralNetworkUsingBayesByBackpropExample
numHiddenUnits1= 8;
numResponses = 4; % y1 y2 y3 y4
featureDimension = 5; % u1 u2 u3 u4 u5 %
% featureDimension = 4; % u1 u2 u3 u4 u5
maxEpochs = 100; % IMPORTANT PARAMETER
miniBatchSize = 512; % IMPORTANT PARAMETER
% architecture
Networklayer_h2df = [...
sequenceInputLayer(featureDimension)
fullyConnectedLayer(4*numHiddenUnits1)
reluLayer
bayesFullyConnectedLayer(4*numHiddenUnits1,Sigma1=1,Sigma2=0.5)
reluLayer
fullyConnectedLayer(8*numHiddenUnits1)
reluLayer
gruLayer(LSTMStateNum,'OutputMode','sequence',InputWeightsInitializer='he',RecurrentWeightsInitializer='he')
fullyConnectedLayer(8*numHiddenUnits1)
reluLayer
fullyConnectedLayer(4*numHiddenUnits1)
reluLayer
fullyConnectedLayer(numResponses)
bayesFullyConnectedLayer(numResponses,Sigma1=1,Sigma2=0.5)
];
'bayesFullyConnectedLayer' is used in Train Bayesian Neural Network.
% training options
options_tr = trainingOptions('adam', ...
'MaxEpochs',maxEpochs, ...
'MiniBatchSize',miniBatchSize, ...
'GradientThreshold',1, ...
'SequenceLength',8192,...
'Shuffle','once', ...
'Plots','training-progress',...
'Verbose',1, ...
'VerboseFrequency',64,...
'LearnRateSchedule','piecewise',...
'LearnRateDropPeriod',250,...
'LearnRateDropFactor',0.75,...
'L2Regularization',0.1,...
'ValidationFrequency',10,...
'InitialLearnRate', 0.001,...
'Verbose', false, ...
'ExecutionEnvironment', 'cpu', ...
'ValidationData',[{uval_load} {yval_load}],...
'OutputNetwork','best-validation-loss');
%% training and Saving model data
savename = [sprintf('%04d',MP),'_',sprintf('%04d',numHiddenUnits1),'_',sprintf('%04d',LSTMStateNum),'_',sprintf('%04d',trainingrun),'.mat'];
if do_training == true
tic
[h2df_model, h2df_model_infor] = trainnet(utrain_load,ytrain_load,Networklayer_h2df,"mse",options_tr);
toc
ElapsedTime = toc;
h2df_model_analysis = analyzeNetwork(h2df_model); % analysis including total number of learnable parameters
h2df_model_infor.ElapsedTime = ElapsedTime;
save(['../Results/h2df_model_',savename],"h2df_model")
save(['../Results/h2df_model_info_',savename],"h2df_model_infor")
save(['../Results/h2df_model_analysis_',savename],"h2df_model_analysis")
else
load(['../Results/h2df_model_',savename])
load(['../Results/h2df_model_info_',savename])
load(['../Results/h2df_model_analysis_',savename])
end

请先登录，再回答此问题。

回答（1 个）

Matt J
Matt J 2024-7-31
编辑：Matt J 2024-7-31
You have shown the code for your network, but not how you have adapted the training code, nor the error messages. So, it calls for guesswork.
A fair guess however is that you are using the deprecated trainnetwork() function rather than trainnet() to do the training. With trainnetwork, the network must have an output layer, for example a regressionLayer or a classificationLayer which specifies the loss function to be used during training. The network in your code does not have such a layer.
  1 个评论
Vasu Sharma
Vasu Sharma 2024-8-1
Hi @Matt J! Thanks for the response. I am still getting an error after updating to trainnet.:
Namely: Error using trainnet (line 46)
Fixed sequence length training option is not supported.
Could you kindly advice?
Best,
Vasu

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Image Data Workflows 的更多信息

标签

产品


版本

R2024a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by