Simple understanding of Convolutional Neutral Network I want to ask a basic understanding of CNN.
Let say I have 1 dataset (100 pictures) with

*

*Class A (Picture of Cat: 40 pictures)

*Class B (Picture of Dog: 60 pictures)

And then, I input 100 pictures into CNN and run it.
My question is:

*

*What is the output should I look at?

*Is that mean if I input a picture (either Cat and Dog), I can know the picture (is cat or is a dog) by looking at the output?

Thank you.
 A: The classifier would not necessarily be a logistic regression(it would be an SVM or some other classifiers), but it would be simpler to illustrate the issue using just logistic regression.

What is the output should I look at?

I thought you mean logistic regression. The output would just be a float between 0 to 1 or two floats complementing each other(we use this type here, but they are equivalent) and adding up to 1. You can refer to this answer. You can see from the picture below that the blue and red nodes form a binomial logistic regression model.

Is that mean if I input a picture (either Cat and Dog), I can know the picture (is cat or is a dog) by looking at the output?

Yes. The CNN or any other encoders is just for extracting features. The last layer of a CNN is just like the input of the logistic regression.
The fully connected layer depicted below is what I mean the last layer of the CNN, and the output as depicted is what you want. In your case, there would be just 2 floats(each red node is a float number). Each red node stands for a class and they add up to 1 and you choose the one with the biggest number. In your case, if 0 indexed node represents cats and 1 indexed node represents dogs(you train it to do that with such labels) and node 1 is bigger than node 0(in the inference mode) we can say that the input of the model would be a dog.

A simple and typical CNN binary classifier.
Hope this removes your doubts.
A: The basic intuition of a CNN is that it is a modified neural network which uses a trick of parameter sharing to capture more abstract patterns in the images which are then used to classify different classes like dog and cat in your case. So, the purpose of a CNN architecture is feature extraction in an efficient way. Each layer in CNN captures different abstract patterns which add to the overall differentiability among the classes. These extract features can be then fed to a fully connected layer which acts as a classifier and finally classified using a sigmoid(binary classification) or softmax function(multi-class) which produces in terms of probabilities.

*

*You should look at which class has maximum probability in the output layer. Ex. if your output layer has .2 for cat and .8 for dog, then final output of your model is a dog.

*If you feed an image of a cat in input layer and your output layer produces .95 probability for cat label in output layer then you can know your output is a cat.

