Welcome back.
In the last lecture we saw how to combine and train a lot more than in this lecture.
We will learn how to evaluate performance of automotive and how to predict classes on the new dataset
using our this more than first to evaluate performance of our more than we have to use the Evaluate
method.
So just right you have more than object name which in our case is modern.
And then we are pulling evaluate my take and inside evaluate.
We have to give our test data sets.
So we are writing x test for the independent test data side and then dependent test.
So large this run this
so the output here is first the loss and then the accuracy.
This is accuracy because this is the metrics which we mentioned while compiling a lot more than a few
remember.
By combining we mentioned metrics equal to accuracy and that's what the second value which we are getting
here is of accuracy.
So on our test set the accuracy which we are getting is 86 percent which is really good as compared
to logistic regulations or entries on the same data side.
So with those models you will get around 70 to 80 percent accuracy.
But here with it and then we are getting 86 percent accuracy.
So once again to check the accuracy score or just to check the performance of your model you have to
use evaluate metric.
Now let's learn how to predict the probabilities and how to predict the classes on our new unseen data.
Since right now we do not have any new data.
We just taking first three samples from our test dataset and consider that as your new unseen data.
Just from this.
So we have saw the first three reports from our dataset and we are considering it as our new record.
So there are two parameters which you can predict.
First one is the probability of each class and second one is the class.
Let's first calculate the probability score assigned to each class to do that.
We can use not predict metric.
So we will write the object name more than then not predict my.
And as an argument we are passing over new unseen data which is X and that's what you notice on this.
You can see our new data contains three records.
That's why we are getting output for three records and there are 10 values in each of these elements
representing the probability values of corresponding class.
So for the first record the maximum probability is for a label equal to 10 and the probability value
is point nine.
For the second record.
Probability is 1 for the label corresponding to the third object.
And for the third record we have called equal 200 for the second object.
We are also using round equal total just to round off.
This probably gives values to do that similar religions.
No it's not pretty.
We are projecting probabilities but if we want to radically predict the class and not the probability
scores we can use that predict underscore.
Classes matter so so indexes almost same.
First you have to write your model object name and then call the product standards for classes matter
and in fact you have to mention your new asset.
Just run this
you can see here we are getting an array of the labels.
So for the first object the class label is nine so you can also confirm it from here also the maximum
probability was assigned to this object and here also we are getting a position as 9 to remember the
numbering.
It starts from zero.
So this fund is zero.
This fund is 1 and so on.
And this fund is 9 so for the second record we are getting the predicted glasses.
And you can also see that the probability was maximum for this record which is 0 1.
So second object for the third record we are getting the predicted class says 1 and in the probabilities
also you can see that the maximum probability correspond to position number 1 now again it's a little
bit difficult to interpret desserts like this.
Nine to one so large this causes the body description instead of this new medical labels.
So we will do the same thing we did earlier.
We have already created a list called last name.
We are just passing by a red and blue that this if you run this instead of this labels you will get
the description of each record.
So now Lexus confirm this by plotting the image of our first new dataset.
So the position is zero and we are using.
I am sure this
you can see this photo looks like and then cobalt and be also pretty and callable for the second record
where the pollution is one and see that this is a pullover.
And we also predicted below for the third object yellow shine this boot
you can see that this is a jeans trouser and we also
so they're told we predict with a word more than we can use evaluate beryllium performance of the model
on set and to predict few values.
We can either use not predict might hurt or not predict and school classes might not predict that method
will give us the probability values assigned to each class for each record while they attended classes
will give us details about the most probable last for that group or
so that so we create train and predict using MLB more than now just for a quick somebody
we started with importing and installing 10 cents lower then we imported what fashion Amnesty does set
with 10 categories
then we divided that dataset and to green validation and set.
And before that we also normalize our dataset now after that we created architecture for our model.
This is where you mentioned the activation functions number of neurons you want in your lives
after that we compiled our model to give lost function optimizer and the metrics we want to calculate.
And after that we train that one model using not fit method
and after one more delay strain we can use the short history method to get all the loss and accuracy
values for each epoch after that we can use dot evaluate method to check accuracy scoots on the as dataset
and we can also predict values for new records using both predict and plus as matter.