The training part is complete.
The training ran for 20 epochs, and at the last epoch, we have a training accuracy of 93 percent and
validation accuracy of seventy three point six percent.
You can see that there is a huge difference in validation, accuracy and training accuracy.
This is a sign of overfitting after 10 to 15 debug.
We can see that there is no considerable improvement in the validation accuracy.
However, our training accuracy keeps on increasing.
This is because our model is trying to work on the training data.
However, we are not seeing any considerable improvement in the validation accuracy to see the result.
In the last model, which is the model I did when did Epoch, we can run this command of saving saving
model in the HD five format.
This will create a cat and dog small wonder.
It's five file in the working directory.
This last combine is for plotting the accuracy and lost function.
Odd by default, plotted for us, however, at a later point of pain, if you have removed this image
and you want to plot the model accuracy and loss over 20 epochs, you can run this.
Come on.
So it is the same glove only that each epoch point for validation is marked with a cross and for training
is with a subtler hollowed out.
That's all.
Notice that we have achieved a validation accuracy of 73 percent with this convolutional architecture.
In the next video, we will create artificial data to augment this data that we have.
And instead of having 2000 images, we'll have larger training data.
And in that scenario, we'll have considerable improvement in ad validation accuracy.
Let's see that in the next video.