So yeah, we have this killers.
And then if you click on this graphs here or have this graph coming up, let's reduce this slightly.
Okay?
So we have this notice that the graph type here is up graph and this actually means we're viewing the
graph from the operations level.
If you could come right here and zoom in, you'll see you have the cough layer, you have the comfort
layer again, dense layer, dense layer.
And then zoom in this other way here.
You see you have the albums optimizer.
Let's double click your where you have this, plus you double click and then you get to see exactly
what goes on in this Adam Optimizer.
Let's reduce that zooming and then let's double click again to reduce this.
Okay?
So you have that.
And for this dense layer, you could double click.
You see, you have that, you see you have this can now you double click to better understand what goes
on.
And as you could see here, we have this regular riser.
So the regular riser we had defined previously.
Now let's reduce this by double clicking.
Let's scroll here, we double click that reduced and then double click this.
So basically, it hangs 2/10 of a bar.
We're able to visualize exactly what goes on under the hood when TensorFlow creates this graphs, which
in turn parameters do computations even faster.
Now, another way we can look at this is by coming right here, the stack and selecting carrots.
Once we select the carrots, we instead of having the operation graph we have just seen, we now have
this conceptual graph.
We'll see that this is going to be quite easy compared to or easy to understand compared to what we
had done previously.
So you have this focuses on the cross model.
We have built a cross sequential model we had specified.
And here what we have is you see the input.
So unlike previously where we had stuff like the Atom Optimizer, the different metrics and the say
loss computations with the up graph here we have just the Keras model.
So here you have the input conf batch normalization max pulling drop out conf batch normalization max
pulling flatten dense batch norm dropout dense batch norm and finally dense.
So that's our conceptual graph and that's it for the section on graphs you see, you get to understand
exactly what goes on under the hood thanks to this visualization.
Or better still, this graphs visualization made available with tensor board.