A simple classifier to classify Cars and aeroplanes with CNN(Part 2: inference)

Hello there, hope you are doing well. This is a sequential post of classifier with CNN. In our earlier post we learned how collect the data, organize them and train a model for classification. In this post we will learn how we can use the trained model and actually classify the Cars and Planes. When I was starting to train a CNN and learn, I had a difficult time to learn how to use the model and actually see the result. All the article or blogs I was following only talks about how to train the network but no one was actually talking about how we can see the classification results. Enough talking lets start :

If you followed my previous post, the model file(model.h5) was created with 96% accuracy and save in the models folder. Now we will use that model for inference. 

Step1: We will start by importing the required libraries as we did for the training.

Step2: In the test.py code we will specify where the model and the test images are. We will load the model and the weights. Specify the image size we dealing with.

Step3: Now we will define a function for prediction which will take the test image as input and return the prediction output accordingly. As we have only two classes(cars and areoplanes), we will get the probability of two classes as output. We will read that probability and show the output result.
You can clone the whole project from github here. Do let me know if you have any feedback or suggestions. Hope you enjoyed coding with me. Wish you all a very happy new year 2020 in advace.

A simple classifier to classify Cars and aeroplanes with CNN(Part 1)

Today we will build a simple supervised algorithm with keras to classify cars and aeroplanes.  We will implement a simple CNN(convolution Neural Network), which we will train with the dataset, after the model is generated we can easily classify the images. Here we are using only two classes, but you can classify as many classes as you want.

I am using a small dataset. For training 200 images of cars and 200 images of planes. And for testing 50 images from each class. You can use your own dataset with different classes if you want.

The dataset contains lot of information or features of the images we provide. The model learns the distinguishable features from the data-set in the training process. With that information we can classify the images.  So let’s get started.

We will divide this tutorial in two parts, in part1 we will learn how to train the data-set and generate the model file and in part2 we will use this model file to do inference and real classification.

Step 1: Preparing Data-set

You can download the data from my github here: gitHub

Once you have the data-set we need to organize our data before we start actual training code. Below image shows the structure of folders for the data.

Photos of Cars:

Photos of Planes:

Step 2: Installing required Packages

• -          Tensorflow > 1.13
• -          Numpy
• -          Keras

Step 3: Implementation 

Frist we will import the required libraries 

Read the data-set

Initialize the CNN and writing the layer… we will have one convolution layer followed by an 

activation function and a pooling. And we will repeat the same.

Flattering, dense layer, dropouts and activation at the end.

Compiling the CNN we shall use the ‘rmsprop’ optimisation method, binary cross entropy loss function

Now we have feed the images to the CNN we just created

Finally the classifier, model will be saves as ‘model.h5'

If you run the above code the result should look something like this-

  After 10 epoch is done the model will be save with an accuracy of 96%.

You can download the whole code from my git repository here: gitHub

Stay tuned for the inference part. Do share your feedback in the comment section. See you soon. Regards.

Basic Concepts of Artificial Intelligence, Machine Learning, Deep Learning

Today we will start our journey to the world of Artificial Intelligence(AI). We will learn the basic definition of Artificial Intelligence (AI), Machine Learning(ML), Deep Learning(DL), Natural Language Processing(NLP), Computer Vision and Image Processing. Later we will go deeper with the machine learning algorithms and how those algorithm works. This tutorial is for beginners, if you have an idea of AI skip this course and go to the next lesson where I will discuss different Machine Learning algorithms.

What is Artificial Intelligence(AI)?

-          Artificial intelligence (AI) is the ability of a machine or a computer program to think and learn by doing certain task. The concept of AI is based on the idea of building machines capable of thinking, acting, and learning like humans. On other words the creating the machine capable of understanding the environment, understanding the problem and act intelligently according to the situation.

What is Machine Learning(ML)?

-          Machine Learning(ML) is an application of AI that provides system the ability to automatically learn and improve performance without being explicitly programmed. ML focuses on the development of computer program that can access data and learn for themselves. The main aim is to allow computer learn automatically without human intervention or assistance and act accordingly.

-          Next question in your mind may have, how the machine is learning? –  The answer is as human learns. Frist the machine gathers information and knowledge then use those knowledge to take decisions. Also, past experiences helps to take decisions in future.

What is Deep Learning(DL) or Deep Neural Network(DNN)?

-          Deep Learning(DL) is part of a broader family of Machine Learning and AI, which emulate the learning approach that human beings use to gain certain types of knowledge. Traditionally machine learning algorithms used to be linear, but with deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. Because this process mimics a system of human neurons, deep learning is sometimes referred to as Deep Neural Learning(DNN) or deep neural networking. Let me explain the concept with an example blow-

-          A baby when starts learning about what a cat is (and is not) by pointing to some objects and saying the word cat. The parent guides him by saying, "Yes, that is a cat," or, "No, that is not a cat." As the baby continues to point to objects, he becomes more aware of the features that all cat have. What the baby does, without knowing it, is clarify a complex abstraction by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy. A machine follows more or less similar approach. Each algorithm in the hierarchy applies a nonlinear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.

What is Natural Language Processing(NLP)?

-          Natural Language Processing is the ability of a computer program to understand human languages as it is spoken. NLP is also component of AI. The development of NLP is challenging because traditionally computer requires human to speak to them in a programming language or unambiguous or highly structured, clear commands. Whereas natural languages are generally ambiguous, have different structures, dialects, regional effects which are difficult to distinguish.

-          Semantic analysis and Natural Language Processing can help machines automatically understand text, which supports the even larger goal of translating information, understanding potentially valuable piece of customer feedback, understanding insight in a tweet or in a customer service log into the realm of business intelligence for customer support, corporate intelligence or knowledge management.

What is Computer Vision and Image Processing?

-          Computer vision is about granting the computer the ability to ‘see’ and ‘understand’ what it sees. In image processing you get an image as input and provide processed image as output, whereas in computer vision you get an image (or video) as input and provide other quantitative data as an output (e.g geometrical information about the objects in question). Computer Vision tries to do what a human brain does with the retinal input, it includes understanding and predicting, detecting certain things. For example, given an input image, using computer vision the computer can classify the objects (cars,humans,train.. etc) as human does. There are many other applications but this is just to give you a basic idea.

  This was the basic concepts. Please comment below if you have any questions or feedback. Stay tuned for more detailed concepts of Machine Learning Algorithms.

Next topic is Supervised, Un-Supervised, Semi-Supervised machine and Reinforcement Learning algorithms