Defining number of neurons/layers in neural network

35 次查看（过去 30 天）
Rémi
Rémi 2017-2-21
评论： George Bezman 2018-11-6
Hello all,
I am applying a multiplayer perceptron with back-propagation and momentum to a classification problem. The data consists of 20 continuous features and 1 binary predictor (class: 0 or 1).
As the title suggests, I am unsure how to specify the number of neurons/layers in my network. I am using the traingdm function.
Using the following code, I have access to the number of neurons (3 here) but not the number of hidden layers:
%%Data
%Load Data
data = csvread('voice.csv');
%Seperate features from predictors
X = data(:, 1:end-1)';
Y = data(:, end)';
%%Model 1
net1 = patternnet(3,'traingdm');
net2 = train(net1, X, Y);
Preferably though, I would rather use the traingdm function like this as it gives access to a lot more parameters, however, I seem to lose the ability to specify the number of neurons:
%%Data
%Load Data
data = csvread('voice.csv');
%Seperate features from predictors
X = data(:, 1:end-1)';
Y = data(:, end)';
%%Model 2
net.trainFcn = 'traingdm';
net.trainParam.epochs = 10000; %Maximum number of epochs to train
net.trainParam.lr = 0.5; %Learning rate
net.trainParam.max_fail = 6; %Validation vectors are used to stop training
%early if the network performance on the validation vectors fails to improve
%or remains the same for max_fail epochs in a row.
net.trainParam.mc = 0.3 % Momentum constant
[net,tr] = train(net, X, Y)
I've read the documentation: http://uk.mathworks.com/help/nnet/ref/traingdm.html but can't seem to find parameters for number of hidden layers or number of neurons in each layer.
I also can't seem to manually define training/validation/testing sets, it has defaults it uses (70/15/15) but I can't access these values and change them. (I can through the GUI using nnstart but then I lose access to the other parameters - eg learning rate).
Lastly, I can't seem to specify my own activation function.
Assuming I can get around these issues, the end goal would be to do a grid-search to find the best set of parameters for this problem.
I've attached the .csv file in case it's of interest/use to anyone.
Thanks in advance,
Rémi

请先登录，再回答此问题。

采纳的回答

Nachiket Katakkar
Nachiket Katakkar 2017-2-24
If you take a look at the example on the "traingdm" documentation page, you will see a call to "feedforwardnet":
net = feedforwardnet(3,'traingdm');
The first argument is a vector specifying one or more hidden layer sizes, so if you want more hidden layers:
net = feedforwardnet([3,4,5],'traingdm');
Alternatively, you could change these values from the "net" object:
net.numLayers = 5;
net.layers{1}.size = 4;
You can change the proportion of train/test and validation sets as follows:
net.divideParam.trainRatio = 80/100;
net.divideParam.valRatio = 10/100;
net.divideParam.testRatio = 10/100;
Setting a custom activation function is more difficult, but the following discussion could be a good place to start:
https://www.mathworks.com/matlabcentral/answers/56137-how-to-use-a-custom-transfer-function-in-neural-net-training
This link is useful for Neural Network object properties:
http://www.mathworks.com/help/nnet/ug/neural-network-object-properties.html

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Sequence and Numeric Feature Data Workflows 的更多信息

标签

产品

Community Treasure Hunt

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

Start Hunting!

Translated by