So the model is now fit, but when I posed the video, I had earlier done this for 20 epochs, but I
saw that the solution was not converged.
So I had landed for another 30.
Still, the solution was not in words.
So I have done it for no 50 bucks.
So overall, I have run this model.
Four hundred epochs and after 100 epochs, I can see that in some places, in some epochs, we have
achieved a model giving a validation accuracy of nearly 84 percent also.
So if you are using callbacks, you can store the best model using a callback and you can use that model
to test your performance on your test dataset.
For now, we have this model.
This last model, which is giving us validation accuracy of eighty three point three six percent, which
is also good enough.
You can see that we have achieved over eight nine percent accuracy improvement just by doing the documentation.
So even if you have fewer data observations to bring in CNN, you have the option of creating artificial
data by using the data augmentation for which we used this function image data generator.
So this was the second part of our project.
We have seen considerable improvement in accuracy after using data augmentation.
And the last part we are going to use up retained CNN model.
There are certain architectures which have been trained on large datasets and these networks are huge
and they have performed really well in certain competitions.
And some of these architectures are part of a decade as Labadie.
So we can straightaway use the weight of these Bre-X train models.
So in the next video, we are going to use such a retrain model and applied on our God vs. Dogs dataset.
And let us see if we can improve the accuracy for the.