Hello, i am new to neural networks and find it difficult to understand a few things about them. First of all i have created with patternet a network with 4 inputs , 3 hidden layers (4 if we consider the output also) and 3 outputs. I have several questions:

1) Is there some formula to find the nodes of each hidden layer? also can i plot so i can view them?

2) For each hidden layer can i extract vectors/struct with the weights and biases? note that i don't want the final values but instead all the values for each iteration. I have found a previous post saying to create a for loop and for each iteration extract them, but how would i know when will my nn stop, for example sometimes it stops in 30th iteration and sometimes in 50th.

3) For each iteration can i see the regression of mse? i mean to extract the actual numbers for each iteration and not to see the graph only with plotperform.

4) How can i actually save my network so when i open it i will alos have the plots of mse, confusion, plotroc and to view the structure of my network. When i save the object 'net' it saves only the matrix and when i restart my matlab i can't get all the above.

5) The correct approaching is to calculate the number of nodes, weights and biases according to this post? (Calculating biased mse etc) http://www.mathworks.com/matlabcentral/answers/78809-how-to-load-own-data-set-into-neural-network

Here is the network i created

And here is my code what i have done so far:

` net = patternnet(3);%,'trainscg','mse');`

net.trainFcn = 'trainlm'; net.trainParam.epochs=2*50; %Maximum number of epochs to train

net.trainParam.goal=0; %Performance goal

net.trainParam.max_fail=150; %Maximum validation failures

net.trainParam.min_grad=1e-70; %Minimum performance gradient

net.trainParam.mu=1e-4; %Initial mu

net.trainParam.mu_dec=0.01; %mu decrease factor

net.trainParam.mu_inc=10; %mu increase factor

net.trainParam.mu_max=1e10; %Maximum mu

net.trainParam.show=25; %Epochs between displays (NaN for no displays)

net.trainParam.showCommandLine=true; %Generate command-line output

net.trainParam.showWindow=true; %Show training GUI

net.trainParam.time=inf; net = train(net,X,Target);

Thanks in advance.

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