Highlight continue with our project, and it is we will do some neural network model sortable for my
neural network regression analysis.
We will do it across sequential model.
So there are some force that you need to do to perform the model so that some Stanford also.
Certainly tomorrow, and the first that a.
Import.
Sequential.
From Kiraz DOT models, the next one is.
Stock the life using DOT at.
Michael.
The next one is.
Configure the learning process using DOT compile.
Method, and the last one is.
Trained model anda.
Trade data said using.
Don't fit.
Madha.
So we have made the import data step before, so we call our data is already available.
We raise you to.
Following steps, are we right?
I'm Cofferati.
So first we have to import from Usdaw model.
In part.
Cranshaw.
And from cross dot dildoes import.
Dence.
And then from Kiraz, not magic.
From Keri's import matrix.
So to be happy about that which are sequential and metric so that I am not what I recall, they are
very important.
So.
Sequential glass.
This a usual Devi.
Linear stock of.
Network.
They are not a model.
We will show you the.
Sequential.
Constructor to create a model.
Which will be.
Inrush would play a.
Using.
They add.
Method.
That as glass.
There's a huge hole in charge here.
Uh.
They are.
Which is the basic.
For.
Fully connected, the.
And then the final is Metrick.
Class, so.
The ADA is a function.
That is due to evaluate the performance of the model.
So now let's start to write something called.
Next one will be Modahl.
Equa.
Sequential.
Modahl, not at this.
20 Khama.
In both underscore the.
And the dimension of the 2013.
And activation.
Equal.
Relu.
Then add one more.
There's.
Ten input equa.
They're all because this is our first hidden layer.
And the last one is this is one this one will be the award show activation with A'Lelia.
So we're going to say that for the first role, we have set a to model a sequential and then we have
added Nalliah.
So are we.
We were.
You were completely correct, that nanostructure, which will us fully connected level advisers, the
glass sort of the most important because it's a laboratory fighter, a boat, it might have the right
number.
It was so because we had 13 rows that far away, we had to set in to be 13, 13 predictors.
And we Balestrieri by Ramatoulaye 20.
In beauty and activation.
So the trend is a positive integer, probably ending the dimensionality of the abuse by.
And they note the number of neurons on the inner level.
In what they attribute is the number in both variables and finally the activation is equal, RELU action
is to set the activation function.
The second layer has 10 neurons and RELU activation function.
So this one is trending around.
This one is ten neurons.
And finally, the outlier.
I had only one neuron and the activation function is linear.
So we don't have.
An all time to talk about a.
Activation version, I did explain very clearly on my auto courses, which is I in healthcare, so.
Now before training.
Before training a model, you need to configure the learning process, which is a combined method so
that at.
They're going to say who say it, we didn't make any mistake.
And I forgot to run this out, that's why and we got no mistake.
So it's very easy to come by.
Up the Mizar equal idam.
Los.
Equa.
I mean, underscores why underscore error and then the last one is.
Metrick.
Equa.
Racey.
So that horrible so that come by for us and then they talk about it.
So the first one is I optimizer.
So.
Adam.
Up the Mizar.
So why is it this age?
An al.
We are for the first order.
Gradient by optimization of stochastic objective.
Function.
Based on duct tape, estimates of lower order.
Moman.
The next one is the maze where Soviet era.
Of course, we want to minimize.
We want our model is very good.
Which is the lowest.
Function.
So it measured the average of the square of the.
Arroz.
So the last one is.
The.
These are Kilrea, see Mattrick, which is a function.
That is used to evaluate.
The.
Well, foreman of the model.
Doray.
The training and testing.
So now we got you.
More out of it.
My thoughts are Modot dog fit.
Then as.
Train, comma, why?
Train.
And Epoch.
A 1000 and proposed.
Which is one, so, yeah, we don't have time to go through all the definition in here, but if you
can, how will we explain or I have explained very carefully on my order courses, which had five big
projects we got than this in health care.
So now that when our.
Neuron and eyes are far, far away because it will take a long time.
I did trained Amoroso assembly, and now we need to bring the model summary to say.
What is happening?
I secured a sale and we got a 501 Paretta.
So here we clearly say the opposite and no surprises in life, so to test the model capacity for predicting
the median value of owner occupied homes as a function of the 13 variables has shown, we can display
the predicted values against the current one.
So to do this, we must perform the prediction on all the observation contending that has by the predict
function.
So it's very easy with that Cranio called.
Tell you why underscore.
That you its corporate.
K.M..
Equal model, not predict.
And then.
I underscored tests that run the sale.
And.
We did not get any error, so for the time being, we don't need to add anything else, the that will
be useful for us to make a comparison with another model.
So to evaluate our performance, we must use to evaluate function so that Antonio Cazale and score.
Equal model or evaluate.
I underscored his.
Why underscore the test and the polls equals zero and then just bring.
Carus.
Model.
And then bring the skull.
Score zero.
And this one said, A had lost value and magic value for the modern enticement.
So can both computation is done in patches.
So.
That run the sale and we got zero point zero six, so as workmen said, to be a good result, but to
have a confirmation necessary to compare these results with those deriving from the application or another
model, as we are doing regression analysis, this model seemed to be the most suitable one.
And this is the end of this video.
I hope you enjoy it.
And I will see you in the next video.