Hello and welcome to this tutorial.
In previous Teito DLP.
How understood input layer and convolution layer in this little deal, we will understand the remaining
layers in convolutional neural network.
Let us begin.
Third layer is Relu Layer and Elu stands for Rectified Línea.
Do you need?
It is widely used activation function.
This is the mathematical equation for RELU activation function.
Output of this layer is podgy.
To add value ranges from zero to infinity.
Nature of this activation function, Relu is non-linear.
We can easily back propo get errors and multiple layers of neurons being activated by the RELU function.
The RELU activation function is computationally less expensive than the other activation functions.
And this is because at the time, only few neurons are activated to add non-linearity to our model.
We use this layer.
Pooling layer deep pooling layer is responsible for reducing this vital signs of Convolve features.
We add a pooling layer to decrease the computational power that is required to process the data.
The pulling layer is useful for extracting the dominant features, which are rotational and positional
invariant so we can play in do more efficiently.
In simple words, we can say that we add puling layer to reduce the size of images.
There are two types of pulling, Max, bullying and average pulling max pulling returns, the maximum
value from a portion of inmates that is covered by colonel.
And the average pulling returns, the average of all the values from a portion of inmate that is covered
by a.
Let us understand the mathematical calculation of Max pulling and average pulling.
First, we will understand Max pulling in, Max pulling this Telek, the maximum value here in this
section.
Twenty is the maximum value here.
Thirty thirty here in this section.
Maximum value is 112, 112.
And here in this last section, the maximum value is 37 37.
Let us understand.
Average pulling 13 is the average of these four values.
Similarly, it is the average of these four values.
Seventy nine is the average of these four values.
And 20 is the average of these four values.
So this is home, Max, pulling an average pulling calculated.
Let us understand the fully connected layer in fully connected layer.
They passed the output of pulling layer, so input of deeply connected layer is the output of pulling
layer and after the input layer.
There are multiple layers in fully connected layer, and these multiple layers are the hidden layers.
And at the end, we get output from that fully connected layer.
You can think this fully connected layer is artificial neural network.
So finally, this is the complete architecture of convolutional neural network flight.
We have to provide the input.
Then we have to add convolution and relu layer.
And after that, we have to add a pooling layer.
There can be multiple pairs of convolution plus Trelew and pooling layers.
After that, there is fully connected layer.
We will get our output at the end of fully connected layers.
So dictatorial about the convolutional neural network NCEA.
I will see you in the next one.
buildOn happy learning.