Non Binary Image Classification using Convolution Neural Networks Python Project смотреть онлайн

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Name:Venkatarao Ganipisetty
Mobile:+91 9966499110
Email :[email protected]
Website:www.venkatjavaprojects.com
About Project:
In this project we are using python KERAS and Google TENSORFLOW CNN algorithm to classify images, CNN algorithm can predict images correctly up to 90% which is better prediction accuracy compare to all other algorithms such as SVM, KNN etc. To classify images using CNN we need to train CNN network with all possible images and when new images uploaded then CNN train model will be applied on this new image to predict or identify image.
CNN working procedure
To demonstrate how to build a convolutional neural network based image classifier, we shall build a 6 layer neural network that will identify and separate one image from other. This network that we shall build is a very small network that we can run on a CPU as well. Traditional neural networks that are very good at doing image classification have many more parameters and take a lot of time if trained on normal CPU. However, our objective is to show how to build a real-world convolutional neural network using TENSORFLOW.
Neural Networks are essentially mathematical models to solve an optimization problem. They are made of neurons, the basic computation unit of neural networks. A neuron takes an input (say x), do some computation on it (say: multiply it with a variable w and adds another variable b) to produce a value (say; z= wx + b). This value is passed to a non-linear function called activation function (f) to produce the final output (activation) of a neuron. There are many kinds of activation functions. One of the popular activation function is Sigmoid. The neuron which uses sigmoid function as an activation function will be called sigmoid neuron. Depending on the activation functions, neurons are named and there are many kinds of them like RELU, TanH.
If you stack neurons in a single line, it’s called a layer; which is the next building block of neural networks. See below image with layers