Ly  Jangi

Ly Jangi

1596435765

How to Implement a CNN with Deeplearning4j

1. Overview

In this tutorial, we’ll build and train a convolutional neural network model using the Deeplearning4j library in Java.

For further information on how to set up the library please refer to our guide on Deeplearning4j.

2. Image Classification

2.1. Problem Statement

Suppose we have a set of images. Each image represents an object of a particular class. Moreover, the object on the image belongs to the only known class. So, the problem statement is to build the model which will be able to recognize the class of the object on the given image.

For example, let’s say we have a set of images with ten hand gestures. We build a model and train it to classify them. Then after training, we may pass other images and classify the hand gestures on them. Of course, the given gesture should belong to the known classes.

2.2. Image Representation

In computer memory, the image can be represented as a matrix of numbers. Each number is a pixel value, ranging from 0 to 255.

A grayscale image is a 2D matrix. Similarly, the RGB image is a 3D matrix with width, height, and depth dimensions.

As we may see,** the image is a set of numbers**. Therefore, we can build multi-layer network models to train them to classify images.

3. Convolutional Neural Networks

A Convolutional Neural Network (CNN) is a multi-layer network model that has a specific structure. The structure of a CNN may be divided into two blocks: convolutional layers and fully connected (or dense) layers. Let’s look at each of them.

3.1. Convolutional Layer

Each convolutional layer is a set of square matrices, called kernels. Above all, we need them to perform convolution on the input image. Their amount and size may vary, depending on the given dataset. We mostly use 3×3 or 5×5 kernels, and rarely 7×7 ones. The exact size and amount are selected by trial and error.

In addition, we randomly select the variables of kernel matrices at the beginning of the train. They are the weights of the network.

To perform convolution, we can use the kernel as the sliding window. We will multiply the kernel weights to the corresponding image pixels and compute the sum. Then we can move the kernel to cover the next chunk of the image using stride (move right) and padding (move down). As a result, we’ll have values that will be used in further computations.

In short, with this layer, we get a convolved image. Some variables might be less than zero. This usually means that these variables are less important than the other ones. That is why applying the ReLU function is a good approach to make fewer computations further.

3.2. Subsampling Layer

The subsampling (or pooling) layer is a layer of the network, usually used after the convolutional one. After the convolution, we get a lot of computed variablesHowever, our task is to choose the most valuable among them.

The approach is to apply a sliding window algorithm to the convolved image. At each step, we’ll choose the maximum value in the square window of a predefined size, usually between 2×2 and 5×5 pixels. As a result, we’ll have fewer computed parameters. Therefore, this will reduce the computations.

3.3. Dense Layer

A dense (or fully-connected) layer is one that consists of multiple neurons. We need this layer to perform classification. Moreover, there might be two or more of such consequent layers. Importantly, the last layer should have a size equal to the number of classes for classification.

The output of the network is the probability of the image belonging to each of the classes. To predict the probabilities, we’ll use the Softmax activation function.

3.4. Optimization Techniques

To perform training, we need to optimize the weights. Remember, we randomly choose these variables initially. The neural network is a big function. And, it has lots of unknown parameters, our weights.

When we pass an image to the network, it gives us the answer. Then, we may build a loss function, which will depend on this answer. In terms of supervised learning, we also have an actual answer – the true class. Our mission is to minimize this loss function. If we succeed, then our model is well-trained.

To minimize the function, we have to update the weights of the network. In order to do that, we can compute the derivative of the loss function with respect to each of these unknown parameters. Then, we can update each weight.

We may increase or decrease the weight value to find the local minimum of our loss function because we know the slope. Moreover, **this process is iterative and is called **Gradient Descent. Backpropagation uses gradient descent to propagate the weight update from the end to the beginning of the network.

In this tutorial, we’ll use the Stochastic Gradient Decent (SGD) optimization algorithm. The main idea is that we randomly choose the batch of train images at each step. Then we apply backpropagation.

3.5. Evaluation Metrics

Finally, after training the network, we need to get information about how well our model performs.

The mostly used metric is accuracy. This is the ratio of correctly classified images to all images. Meanwhile, **recall, precision, and F1-score are very important **metrics for image classification as well.

#algorithms #java #cnn #deeplearning4j

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How to Implement a CNN with Deeplearning4j
Ly  Jangi

Ly Jangi

1596435765

How to Implement a CNN with Deeplearning4j

1. Overview

In this tutorial, we’ll build and train a convolutional neural network model using the Deeplearning4j library in Java.

For further information on how to set up the library please refer to our guide on Deeplearning4j.

2. Image Classification

2.1. Problem Statement

Suppose we have a set of images. Each image represents an object of a particular class. Moreover, the object on the image belongs to the only known class. So, the problem statement is to build the model which will be able to recognize the class of the object on the given image.

For example, let’s say we have a set of images with ten hand gestures. We build a model and train it to classify them. Then after training, we may pass other images and classify the hand gestures on them. Of course, the given gesture should belong to the known classes.

2.2. Image Representation

In computer memory, the image can be represented as a matrix of numbers. Each number is a pixel value, ranging from 0 to 255.

A grayscale image is a 2D matrix. Similarly, the RGB image is a 3D matrix with width, height, and depth dimensions.

As we may see,** the image is a set of numbers**. Therefore, we can build multi-layer network models to train them to classify images.

3. Convolutional Neural Networks

A Convolutional Neural Network (CNN) is a multi-layer network model that has a specific structure. The structure of a CNN may be divided into two blocks: convolutional layers and fully connected (or dense) layers. Let’s look at each of them.

3.1. Convolutional Layer

Each convolutional layer is a set of square matrices, called kernels. Above all, we need them to perform convolution on the input image. Their amount and size may vary, depending on the given dataset. We mostly use 3×3 or 5×5 kernels, and rarely 7×7 ones. The exact size and amount are selected by trial and error.

In addition, we randomly select the variables of kernel matrices at the beginning of the train. They are the weights of the network.

To perform convolution, we can use the kernel as the sliding window. We will multiply the kernel weights to the corresponding image pixels and compute the sum. Then we can move the kernel to cover the next chunk of the image using stride (move right) and padding (move down). As a result, we’ll have values that will be used in further computations.

In short, with this layer, we get a convolved image. Some variables might be less than zero. This usually means that these variables are less important than the other ones. That is why applying the ReLU function is a good approach to make fewer computations further.

3.2. Subsampling Layer

The subsampling (or pooling) layer is a layer of the network, usually used after the convolutional one. After the convolution, we get a lot of computed variablesHowever, our task is to choose the most valuable among them.

The approach is to apply a sliding window algorithm to the convolved image. At each step, we’ll choose the maximum value in the square window of a predefined size, usually between 2×2 and 5×5 pixels. As a result, we’ll have fewer computed parameters. Therefore, this will reduce the computations.

3.3. Dense Layer

A dense (or fully-connected) layer is one that consists of multiple neurons. We need this layer to perform classification. Moreover, there might be two or more of such consequent layers. Importantly, the last layer should have a size equal to the number of classes for classification.

The output of the network is the probability of the image belonging to each of the classes. To predict the probabilities, we’ll use the Softmax activation function.

3.4. Optimization Techniques

To perform training, we need to optimize the weights. Remember, we randomly choose these variables initially. The neural network is a big function. And, it has lots of unknown parameters, our weights.

When we pass an image to the network, it gives us the answer. Then, we may build a loss function, which will depend on this answer. In terms of supervised learning, we also have an actual answer – the true class. Our mission is to minimize this loss function. If we succeed, then our model is well-trained.

To minimize the function, we have to update the weights of the network. In order to do that, we can compute the derivative of the loss function with respect to each of these unknown parameters. Then, we can update each weight.

We may increase or decrease the weight value to find the local minimum of our loss function because we know the slope. Moreover, **this process is iterative and is called **Gradient Descent. Backpropagation uses gradient descent to propagate the weight update from the end to the beginning of the network.

In this tutorial, we’ll use the Stochastic Gradient Decent (SGD) optimization algorithm. The main idea is that we randomly choose the batch of train images at each step. Then we apply backpropagation.

3.5. Evaluation Metrics

Finally, after training the network, we need to get information about how well our model performs.

The mostly used metric is accuracy. This is the ratio of correctly classified images to all images. Meanwhile, **recall, precision, and F1-score are very important **metrics for image classification as well.

#algorithms #java #cnn #deeplearning4j

Keras vs PyTorch vs Caffe - Comparing the Implementation of CNN

In today’s world, Artificial Intelligence is imbibed in the majority of the business operations and quite easy to deploy because of the advanced deep learning frameworks. These deep learning frameworks provide the high-level programming interface which helps us in designing our deep learning models. Using deep learning frameworks, it reduces the work of developers by providing inbuilt libraries which allows us to build models more quickly and easily.

Read more: https://analyticsindiamag.com/keras-vs-pytorch-vs-caffe-comparing-the-implementation-of-cnn/

#cnn #neuralnetworks #machine-learning #deep-learning

How To Implement CNN Model To Count Fingers And Distinguish Between Left And Right Hand?

Convolutional Neural Network (CNN) being computationally strong has the ability to automatically detect the important features without the governance of humans. Also compared to normal neural networks accuracy of CNN models are always high and is considered to be one of the strong architectures when it comes to image classification. CNN models are now capable of doing classification better than humans; it has surpassed human ability for classifying an image.

Read more: https://analyticsindiamag.com/how-to-implement-cnn-model-to-count-fingers-and-distinguish-between-left-and-right-hand/

#cnn

Dicanio Rol

Dicanio Rol

1600939808

Introduction to CNN & Image Classification using CNN in PyTorch

Introduction

Image Classification is the technique to extract the features from the images to categorize them in the defined classes. It is a common-sense problem for the human to identify the images but, for the machine, it’s definitely not. At first, we have to train the machine with the images, and later, when we introduce the model with a new image then based on the training it will give us significant results.

In this article, we will understand how convolutional neural networks are helpful and how they can help us to improve our model’s performance. We will also go through the implementation of CNNs in PyTorch.

Table of Contents

  1. PyTorch Overview
  2. What is CNN?
  3. Implementation of CNNs in PyTorch.
  4. Conclusion

#cnn #machine-learning #image-classification #data-science #deep-learning

Ahebwe  Oscar

Ahebwe Oscar

1624067081

REST Implementation Of Django Authentication System for Python

djoser

REST implementation of Django authentication system. djoser library provides a set of Django Rest Framework views to handle basic actions such as registration, login, logout, password reset and account activation. It works with custom user model.

Instead of reusing Django code (e.g. PasswordResetForm), we reimplemented few things to fit better into Single Page App architecture.

Developed by SUNSCRAPERS with passion & patience.

Requirements

To be able to run djoser you have to meet following requirements:

  • Python (3.6, 3.7, 3.8, 3.9)
  • Django (2.2, 3.1)
  • Django REST Framework 3.11.1

If you need to support other versions, please use djoser<2.

Installation

Simply install using pip:

$ pip install djoser

And continue with the steps described at configuration guide.

#django #authentication #rest implementation of django authentication system for python #rest #django authentication system for python #rest implementation