To train our model, we are creating another object model under the school history.
And we are going to fit our model using extranet Invitrogen data.
We want a number of epochs to be totally and Betsey's to be 64.
And we also have a validation set in which we have X valid and five valid datasets.
So let's run this.
This may take our own Ben.
Ben Monette's.
Now, as you can see, this will take around ten to fifteen minutes.
So if you have a system with low configuration, I recommend you to use five to bucks on Lee.
Here you can also see we are getting training, loss, training, accuracy, validation, loss and validation,
accuracy.
And you can notice that I have not used any callbacks, but it is recommended to use callbacks to save
the model after each epoch.
It is better to use a list topping callback before running this model.
So what training is going to complete?
You can see on our train set, we have the accuracy score of zero point nine two.
So around 92 percent accuracy in the last Depok.
And the validation, accuracy of it be eight percent.
We can also plot Epoch Ways training and validation accuracy on the plot.
So all this accuracy information, along with the e-book where you are, is stored in dark history.
So we are going to plot modern history, not history, on autograph and a few plaudit.
You can see the training losses decreasing and the validation losses also decreasing and both validation,
accuracy and training accuracy are increasing.
You can also identified that the model has still not converged.
And if we run it for a few more, Reebok's border training and validation accuracy are going to rise
to a certain point.
I think it will be better to run it for our own 70 or 80 bucks with a lead stopping callback.
But for more, we will evaluate our model on our test data.
If you remember, for over Hénin model, without any corn layer, we were getting accuracy of around
86 percent on our test data.
So now let's check how our convolutional neural network is performing on our test radio.
We will use door to a new method and we are receiving this value.
And another variable that is Eevee.
So let's look at the value.
You can see the lost value is around zero point three.
And we are getting an accuracy of around 88 percent.
So as compared to neural network, we are getting around two percent increase in the accuracy on our
test dataset using CNN model.
And another thing to notice is that we have just used a single convolutional layer.
If we use multiple convolutional layers and if we run it for a few more Reeboks, we will definitely
get better accuracy on our test dataset.
Now, we have already seen how to predict the classes for any new data.
The matter we are willing to use this predict under school classes, and then we have to provide a new
dataset.
As of now, I don't have any new dataset, so I'm just taking the first three object from my dad's dataset.
And then we are going to restore the predicted class and have widespread variable and we are returning
it.
So for the first object, the predicted last label is nine minus sense for the ankle boots and for the
second object, the predicted classes to and for the third object.
The predictive classes one.
Now let's see the actual values we have the actual values in Y test.
So if we ran this, you can see the predicted values are the same as the actual values.
You can also look at the image by using blood.
I am sure.
Just remember to reshape it again from three to two to three.
And after that, you can use log dot.
I am sure method to plot the object image.
And for the first object, the class label was nine, which is ankle boots.
And in the image also, we can see that the image looks like of an ankle boot.
So all these steps are similar.
What we did for in in classification models, in this model, we just change over and put X and Y shapes.
And then we added a convolutional layer and a pulling layer before a word.
And then model.
That's all for this lecture in the next lecture.
We will see a comparison of model with and without willingly.
Thank you.