Extreme.
Now, let's start building our artificial neural network model using Get US.
Kiraz is a powerful and easy to use free open source Biton Library for developing and evaluating deep
learning models.
So here we are building artificial.
So artificial neural networks are inspired by biological neural network that constituting brains subsystem
learn to perform tasks, but considering examples generally without being programmed with tasks, specific
roles, rules and is based on a collection of connected units are nodes called artificial neurons,
which loosely model the neuron in a biological brain.
Now let's import Kiraz.
And then.
From general models, import sequential.
Sequential specifies to us that we are creating model sequential and output of each year will add an
input to the next layer.
And then from Kiraz, Dr. Layer's importance, the dances used to specify the full cuddlier classifier
is equal to sequential.
So here we are calling our sequential lurcher, Anderson.
Now, let's add the lawyers in our neural network to our facilities, classified ad dense units is equal
to 10.
Colonel Initialised is uniform activation function is Relu and our input, the mission is 21.
So this is our input important, the output of this layer will be taken as input for this next year,
but she'll have to check all the parameters which are used in this layer.
So here we have units, activation units, Eusebius, Carnel initialised.
So and there is also explanation about that barometer's, so if you want to change any default parameter,
then you can see this document.
And make any changes.
So now we're adding a second layer.
And secondly, we have Colonel initialisms, Unicomp and Activision function is Relu, and here we have
not mentioned input because it has the same input demolition and then we're initializing the output
layer.
So we are classified ad dance units is equal to one colonel.
Initialise that is uniform and activation function is sigmoid.
Now we need to compile our model, so you're classified at compile optimizer is Adam.
Lost is called a binary cross entropy, and Matrix is called accuracy.
Now, later, Anderson, Al Sharpton told Anderson.
Just ignore these warnings.
Now they need to fix our neural network trainings like.
So classify it not fit because other models have been classified in.
So we are putting it on our extreme and vedran and backspaces 10 and number of boxes, hundred.
So we are taking a hundred.
You could take 200 or so, you can increase the boxes and you can increase number of a box available,
Anderson.
And plus you can do.
So if we start training on modern.
So we have successfully presented our model.
Let's check that crazy.
Two zero one six three zero point six three.
So it's nearly constant.
Let's check the final accuracy of the Depok.
Finally, we got accuracy of zero point sixty four point sixty four percent accuracy and loss at zero
point six one.
No, oh, no.
That's just it.
So you're what is going to classify dog predicator function?
And are projecting on our best and we're seven little.
Veteran little.
So here we are taking a conditioner's bread, which is above zero point zero five and started in my
bread, which is a prediction.
So now let's check the accuracy score with an all white predicted and lightest.
So here we got an accuracy of 61 percent on our test.
So we got 64 percent.