Here is a summary table of a classification neural network architecture.
In the second day well you can see I have put forward columns.
First column in bold the first table and the second table is for height but parameters.
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
1 and 2
the second third and fourth column in the second table are 4 3 classifications and I use first as binary
classification which is classifying into two classes like marking a meal as spam or not spam.
Second is multi-level binary classification which means there are multiple binary variables.
For example the first variable is whether a mailing spam or not.
And the second variable could be whether a mail is important or not the turban is multi class classification
which we discussed in last lecture.
Also if we have food classes trousers shirts socks and ties this scenario falls under my dick class
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 neutrons are always equal to the number
of input features.
So if you have 16 input variables you will have 16 input neutrons so you'll always have one neuron but
input feature the second to happen parameter is how many hidden layers do we want in our network.
Ideally this depends on the problem but typically we keep the number of the layers between 1 2 5 keeping
more than 5 in a list only increase the computational effort for our system third type a barometer is
it an activation that is the activation function that we put on the neurons in decoding lives.
This is usually Gray Lou.
We discussed rectified linear unit.
It is a very common function which is used for hidden lid activation.
I told you earlier also we used a little because it is very fast to execute in our systems other sleep
hyper parameters vary with the type of classification that we are doing.
So the number of output neurons in binary classification is 1.
But then multi-level binary classification it is one but 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 two neurons in the output layer.
One neuron would tell us whether it is spam or not spam.
And the other neuron regardless whether it is important or not important for multi class classification
we have one output neuron but loss.
So for example if we are classifying images into shirts trousers socks and dyes we will have four different
output neuron for each of these glass and we will put a soft Max activation layer on top of it to get
the probability of each class happening next hyper parameter is 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 then
a step function.
So for binary classification and multi-level binary classification we use the sigmoid function but in
multi class classification after the sigmoid function we have to put an additional layer of soft Max
Activision blaster hyper parameter that 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 a neural network model
in a 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 summary table of the regression neural network architecture.
Here on the left we have hyper barometers and on the right we have typical values that we use for these
hyper parameters.
The first one is number of input neurons.
Again it is one but input feature number of lives that depend on the problem.
But usually we keep 1 2 5 human lives then comes number of neurons per alert.
Again this depends on the problem but typically we take 10 two hundred neurons head and live then as
output neurons.
If we are predicting only one thing we need only one output 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 output neuron on the other hand
if you are predicting the length and breadth of a better life a lot from the image of the flood that
requires two output neurons one for the length of the petal and taking for the top dependent next is
it an activation hyper parameter which means what will be the activation function indignantly as most
commonly used activation function is really to indicate in layers that they look cannot be used as activation
function in 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 apply any particular boundary condition on the output for example if you want that the
output should only be positive then you can apply a relay kind of function on top of it.
Otherwise there is no requirement of an activation function on the output.
Last time what parameters lost function for regression neural network.
The squared error can also work very well as a loss 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 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 these practical actives.