The goal of this post is to show how convnet (CNN — Convolutional Neural Network) works. I will be using classical cat/dog classification example described in François Chollet book — Deep Learning with Python. Source code for this example is available on François Chollet GitHub. I’m using this source code to run my experiment.
Convnet works by abstracting image features from the detail to higher level elements. An analogy can be described with the way how humans think. Each of us knows how airplane looks, but most likely when thinking about airplane we are not thinking about every little bit of airplane structure. In a similar way, convnet learns to recognize higher level elements in the image and this helps to classify new images when they look similar to the ones used for the training.
Image classification model should be trained using this notebook (you will find a description there from where to download image dataset with cats and dogs images). Model is being used and classification prediction is invoked in this notebook. For the convenience, I uploaded my own notebooks (based on the code from Deep Learning with Python book) to GitHub.
Read more in my Towards Data Science article.
Convnet works by abstracting image features from the detail to higher level elements. An analogy can be described with the way how humans think. Each of us knows how airplane looks, but most likely when thinking about airplane we are not thinking about every little bit of airplane structure. In a similar way, convnet learns to recognize higher level elements in the image and this helps to classify new images when they look similar to the ones used for the training.
Image classification model should be trained using this notebook (you will find a description there from where to download image dataset with cats and dogs images). Model is being used and classification prediction is invoked in this notebook. For the convenience, I uploaded my own notebooks (based on the code from Deep Learning with Python book) to GitHub.
Read more in my Towards Data Science article.
