‫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.