Here is a summary table of a classification neural network architecture.
In the second table, you can see I have put four columns, first column in both the first table and
the second table is for hyper barometer's.
These are the values that we have to set prior to training our model.
For example, how many layers will add neural network have is something we have to decide and give beforehand.
So the common classification neural network hyper parameters are mentioned in the first row of table
one and two.
The second, third and fourth column in the second table are for three classifications, and I use flusters
binary classification, which is classifying into two classes like marking a mail as spam or not spam.
Second is multilabel binary classification, which means there are multiple binary variables.
For example, the first variable is whether a mail is spam or not.
And the second variable could be whether a mail is important or not.
The third one is multiclass classification, which we discussed in last lecture.
Also, if we have fought losses, trousers, shirts, socks and ties.
This scenario falls under multiclass classification.
Now, let's see what values of hyper parameters do we usually use for these three types of classification
scenarios?
The first parameter is number of input neurons.
Number of input neurons are always equal to the number of input features.
So if you have 16 input variables, you will have 16 input neurons.
So you'll always have one neuron.
But input feature.
The second hyper parameter is how many layers do we want in our network?
Ideally, this depends on the problem.
But typically we keep the number of the layers between one, two, five, keeping more than five and
a lid only increase the computational effort for our system.
Third type of barometer is hidden activation.
That is the activation function that we put on the neurons in decoding layers.
This is usually RELU.
We discussed rectifier linear unit.
It is a very common function which is used for hidden layer activation.
I told you earlier, also we used a loop because it is very fast to execute in our systems.
Other three paper parameters very with the type of classification that we are doing.
So the number of output neurons in binary classification is one.
But then multilabel binary classification, it is one bad label.
For example, if we are classifying an email as spam or not spam and the other label is important or
not important, we will need to neutrons in the output layer.
One neuron would tell us whether it is spam or not spam.
And the other neuron will tell us whether it is important or not important for multiclass classification.
We have one output neuron Berglas.
So for example, if we are classifying images and two shirts, trousers, socks and ties, we will have
four different output neurons for each of these glass.
And we will put a softmax activation layer on top of it to get the probability of each class happening
next, hyper parameterize output, layer activation in binary classification, logistic or sigmoid function
works very well.
You can use step function also.
But as we have discussed, logistic function performs much better than a step function.
So for binary classification and multilabel binary classification, we use the sigmoid function.
But in multiclass classification, after the sigmoid function, we have to put an additional layer of
softmax activation lost.
Her output parameter is lost function.
We will be using cross entropy as the lost function for all types of classifications.
So these are the hyper parameters that you have to mention when you are running MUTILATOR work model
in the software.
The values that are given here are typical values.
That is, these are commonly used values, but it is not a hard and fast rule to use these values.
Only you can customize your neural network by using any other hyper barometer value.
Next is the somebody, David, of the aggression neural network architecture head on the left.
We have hyper barometer's and on the right we have typical values that we use for this hyper barometer's.
The first one is number of input neurons.
Again, it is one, but input feature number of lives, that depends on the problem.
But usually we keep one, two, five hidden lives.
Then comes number of neutrons, but he did it again, this depends on the problem.
But typically we take 10, 200 neurons, indicate a list then as output neurons.
If we are predicting only one thing, we need only one, not put neuron.
If we are predicting multiple things, we need one output neuron.
But the number of things that we want to predict.
For example, if you are predicting house price, that requires only one or per neuron.
On the other hand, if you are predicting the length and weight of a pet, I love a lot from the image
of the flood that requires to output neurons one for the length of the petal and ticking for David totipotent.
Next, US hit an activation hyper parameter, which means what will be the activation function independently
as most commonly used activation function is Relu indicating layers relu cannot be used as activation
function and output layer.
So for regression neural network in the output activation, we do not really need any activation function
as such.
If you want to play any particular boundary condition on the output, for example, if you want that
the output should only be positive.
Then you can play a RELU kind of function on top of it.
Otherwise, there is no requirement of an activation function on the output limit.
Last iBOT barometer is lost function for regression neural network.
The squared error can also work very well as a lost function.
You cannot use cross entropy here.
So we often use means squared error, which is the mean of squared errors that we calculate for individual
training examples.
So these are all the hyper parameters that you need to specify.
While running a regression neural network in the software on the right, you'll see the typical values.
These are not fixed.
You can still customize your neural network by changing these hyper parameter values.
That's all.
See you in the practical actives.