how does matlab splits the data set into 3 sets for nnet ?

Hello The default function for mat lab nn toolbox is diverand which can also be seen in the GUI of nnstart. It splits the data set based on what percent we define the training,validation and testing.I want to ask does everytime we give the percentage for the testing training and validation the inputs are chosen entirely randomly ?

Suppose I have the input as 1234x400 and out put is 40x400 that represnt 40 class.

So it can be seen from the input that every 10 columns belongs to one class out of 40 .So If I choose training as 80% and validation as 10 and testing as 10 in the GUI so does it mean after frist 8 columns of each class will be chosen for training? And again if choose the same partition(80 10 and 10) some other 8 will be chosen for training ? means it is going to b randomly choosen always again and again ?Please explain me this.

Answer 1

[Walter Roberson]

Every time you train() the data will be chosen randomly if you are using random division.

[Newman]

Sir thank you for your answer can you also tell me how to.store weights of the nnent?

This is the link

https://www.mathworks.com/matlabcentral/answers/296913-how-to-store-weights-after-neural-network-training

[Greg Heath]

You have the choice of accepting the default DIVIDERAND or changing to one of the four alternatives. For details, use the help and doc commands on each of the five settings

>> lookfor divide

...

 divideblock  - Partition indices into three sets using blocks of indices.
 divideind   - Partition indices into three sets using specified indices.
 divideint   - Partition indices into three sets using interleaved indices.
 dividerand  - Partition indices into three sets using random indices.
 dividetrain - Partition indices into training set only.

 For example

 help divideblock
 doc divideblock

Regardless of which you use, it is good practice to always intialize the RNG before the first design.

When using dividerand you can

 a. Obtain different results by changing the initial state of the random 
    number generator.
 b. Obtain identical results by changing the state of the random 
number generator to the state at the beginning of the design.

Hope this helps

Thank you for formally accepting my answer

Greg