Here we can create another neural nets for train, the same neural net for the second target that we
have.
But if the targets are having somehow a relation and we can train the same neural network for boat,
then why creating another one here?
I'm going to import all the targets I want to show them separate.
In a separate figures, I use the same neural network to train for Boult outputs.
Again, I can't do that because in this particular examples, the old foods are somehow having a relation.
That's why I can use the same neural network for training both targets.
But if your targets are totally different, then you need to train a separate neural network.
Now, the endpoint point at all the targets, all the two targets in the same neural network.
Let's show the results for the separate outputs to do that copy's part.
Paschi one more time.
The same this one articulates for first targets.
And this one is all the plot for second target.
To specify this target, I need to open and close apprentice's and choose the first target, do the
same for this one and choose the second one, you need to copy this hard and pasted for all the network
units out food for that train target, train out foods.
Do the same for the second target here.
I'm going to copy and paste it for all of them to see the result for the second output.
Of course, change the name to specify them.
This one is for the first one and this is for the second one.
Now, if I were on the program, I should be able to see eight different figures in eight different
windows, four of them are for the first output and four of them are for the second output.
So let's see the result.
OK, it already heated a number of Pook 500.
Maybe the reason is because we have too many old boots we need to increase, maybe to 1000.
Now, you have eight figures.
This one is to see the second output result, for example, for the test data.
Let's compare to test data with this test data.
This is a test data result for the first one, for the first output.
And this is a test data result for the second output.
As you can see, the 15 networks defeating RAF's are a bit different because we have totally different
outputs.
So if you are writing a paper, you can simply train your network using these techniques and then show
the result with different figures, which you really are looking for.
Training data and for testing data can have a figure for training and another separate figure for testing.
Validation is not very over interest, but if you can show it, that's better and just analyze it,
comparing different out foods and complete the training process.
OK, here is all you need to know for training and neural network.
You can just load different samples and adjust it to have the best results.