Hello guys!
So finally we will also see how you can implement this entire project with the help of GRU RNN.
There is only one difference that you really need to use over here, right?
So if you see over here we are completely implemented with the help of LSTM RNN, right?
We imported the LSTM layers and all right along with this, in the layer itself you also have an option
to add GRU.
Okay.
So if I just go ahead and use this attribute called as uh this library called as GRU.
Sorry, it should be capital letter.
So here you will be able to see that this grew.
Will you will also be able to use for a specific grew instead of LSTM, RNN.
And if I really want to write the same thing with respect to GRU.
So here I will just give you with GRU RNN.
Okay, here I can just copy paste over here.
Okay.
And remember this is imported over here.
Instead of writing LSTM you can go ahead and write GRU here itself okay.
Similarly here you can go ahead and write GRU, grew.
Right.
So as you know this is a different variant of LSTM RNN.
So you can specifically use this.
And uh you can go ahead and execute this right.
If you really want to work with GRU in itself.
Right.
This is the only change that will happen.
And based on this, whether you are using this or whether you are using LSTM, RNN, uh, you can go
ahead and update that and you can go ahead and continuously train it.
Okay.
So this is the only difference with respect to the adding layers with respect to GRU.
Now with respect to an assignment, I will just go ahead and give it to you.
Please go ahead and go ahead and solve this entire problem statement with the help of GRU, and see
how much difference you are able to get with respect to the accuracy.
But if I talk with respect to GRU over here, you have less number of gates when compared to the LSTM
RNN.
So, uh, this is the only difference, uh, with GRU and LSTM.
I hope you like this particular video, and I hope you like this all entire series of videos where we
have created this amazing project.
So yes, this was it from my side.
I will see you all in the next video.
Thank you.