Hi and welcome to the section on the soft Mammalia, the soft actually is what produces the final output.
The probabilities that come out of our CNN.
So let's take a look at this list.
So remember, this was a fully connected layer.
We had our feature maps here from the Max Boulia coming out of that operation.
Then we flattened it and then we connected to these output nodes.
But these output nodes don't output probabilities just yet.
There's another layer it's kind of considered earlier, but it's not technically a little mathematical
operation on these outputs.
And that operation is a soft max layer.
And the reason we do it is because we need to produce probability outcomes for each class in the network.
So to self max converts to log into probabilities.
And when I say logs, I mean the actual outputs we experience, we take of the outputs, which you'll
see shortly.
So it's as I said, it takes the experience of every output and normalizes each x each output by the
sum of the expense.
And that guarantees that it will all sum up to one and no values actually zero.
So this is the mathematical operational formula for this off max.
Let's take a look and see how it works.
So imagine that we have these scores coming out of the output layers.
We can apply the max function here, which would be two squared divided by the sum of the squared two
squared, which is four plus one plus 0.1 squared.
And in the end, that'll give us the probabilities corresponding to each class.
So imagine if this note here corresponded to the number in nodes are the final output of the CNN and
Imagine and CNN's or even in neural networks or, you know, networks, the outputs correspond to a
class in a classification problem.
So if we have a classification problem where we're trying to identify ten digits like the zero one two
three four five six seven eight nine 10, you will have 10 output nodes.
Similarly, if you're trying to identify cats, dogs and penguins, you will have tree output nodes
and we ticked.
And basically the output nodes are some scores that indicate like a high trend or high prevalence of
it being a high indication of it being that specific class.
But we want that in a probability form because probabilities are easy to work with mathematically,
and that's where the soft max operation gives us.
So in the end, we convert these output values two point seven point two and point one in this case.
So that's stop there.
And now we'll start putting together everything to show how we build for CNN.
So I'll see you in the next section.
Thank you.