Dominic  Feeney

Dominic Feeney


How Tensorflow and Keras Used for Image Classification

In this tutorial, we'll share How Tensorflow and Keras Used for Image Classification with 6 steps:

  1. Examine and understand data
  2. Build an input pipeline
  3. Build the model
  4. Train the model
  5. Test the model
  6. Improve the model and repeat the process

#tensorflow #keras 

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How Tensorflow and Keras Used for Image Classification
Mckenzie  Osiki

Mckenzie Osiki


Image Generation Using TensorFlow Keras - Analytics India Magazine

Computer Vision is a wide, deep learning field with enormous applications. Image Generation is one of the most curious applications in Computer Vision. Again, Image Generation has a great collection of tasks; to mention, a few can outperform humans. Most image generation tasks are common for videos, too, since a video is a sequence of images.

A few popular Image Generation tasks are:

  1. Image-to-Image translation (e.g. grayscale image to colour image)
  2. Text-to-Image translation
  3. Super-resolution
  4. Photo-to-Cartoon/Emoji translation
  5. Image inpainting
  6. Image dataset generation
  7. Medical Image generation
  8. Realistic photo generation
  9. Semantic-to-Photo translation
  10. Image blending
  11. Deepfake video generation
  12. 2D-to-3D image translation

One deep learning generative model can perform one or more tasks with a few configuration changes. Some famous image generative models are the original versions and the numerous variants of Variational Autoencoder (VAE), and Generative Adversarial Networks (GAN).

This article discusses the concepts behind image generation and the code implementation of Variational Autoencoder with a practical example using TensorFlow Keras. TensorFlow is one of the top preferred frameworks for deep learning processes. Keras is a high-level API built on top of TensorFlow, which is meant exclusively for deep learning.

The following articles may fulfil the prerequisites by giving an understanding of deep learning and computer vision.

  1. Getting Started With Deep Learning Using TensorFlow Keras
  2. Getting Started With Computer Vision Using TensorFlow Keras

#developers corner #autoencoders #beginner #decoder #deepfake #encoder #fashion mnist #gan #image generation #image processing #image synthesis #keras #super-resolution #tensorflow #vae #variational autoencoder

Dominic  Feeney

Dominic Feeney


Computer Vision Using TensorFlow Keras - Analytics India Magazine

Computer Vision attempts to perform the tasks that a human brain does with the aid of human eyes. Computer Vision is a branch of Deep Learning that deals with images and videos. Computer Vision tasks can be roughly classified into two categories:

  1. Discriminative tasks
  2. Generative tasks

Discriminative tasks, in general, are about predicting the probability of occurrence (e.g. class of an image) given probability distribution (e.g. features of an image). Generative tasks, in general, are about generating the probability distribution (e.g. generating an image) given the probability of occurrence (e.g. class of an image) and/or other conditions.

Discriminative Computer Vision finds applications in image classificationobject detectionobject recognitionshape detectionpose estimationimage segmentation, etc. Generative Computer Vision finds applications in photo enhancementimage synthesisaugmentationdeepfake videos, etc.

This article aims to give a strong foundation to Computer Vision by exploring image classification tasks using Convolutional Neural Networks built with TensorFlow Keras. More importance has been given to both the coding part and the key concepts of theory and math behind each operation. Let’s start our Computer Vision journey!

Readers are expected to have a basic understanding of deep learning. This article, “Getting Started With Deep Learning Using TensorFlow Keras”, helps one grasp the fundamentals of deep learning.

#developers corner #computer vision #fashion mnist #image #image classification #keras #tensorflow #vision

Hello Jay

Hello Jay


Keras vs. Tensorflow - Difference Between Tensorflow and Keras

Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

What is Keras?

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

Major Applications of Keras

  • The performance of Keras is smooth on both CPU and GPU.
  • Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
  • The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

Major applications of Tensorflow

  • From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
  • Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
  • Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
  • Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

Building Image Classifier using Keras and TensorFlow

Callbacks are an important type of object in Keras and TensorFlow. They are designed to be able to monitor the model performance in metrics at certain points in the training run and perform some actions that might depend on those performances in metric values.

Keras has provided a number of built-in callbacks, for example, EarlyStoppingCSVLoggerModelCheckpointLearningRateScheduler etc. Apart from these popular built-in callbacks, there is a base class called Callback which allows us to create our own callbacks and perform some custom actions. In this article, you will learn what is the Callback base class, what it can do, and how to build your own callbacks.

If you want to learn more about those built-in callbacks, please check out the following article:

#deep-learning #neural-networks #keras #tensorflow #image-classification

I am Developer


Crop and Resize Image Before Upload In Laravel Using with jQuery Copper JS

Crop and resize image size before upload in laravel using jquery copper js. In this post, i will show you how to crop and resize image size in laravel using jQuery copper js in laravel.

This laravel crop image before upload using cropper js looks like:

laravel crop image before upload

Laravel Crop Image Before Uploading using Cropper js Tutorial

Laravel crop image before upload tutorial, follow the following steps and learn how to use cropper js to crop image before uploading in laravel app:

  • Step 1: Install New Laravel App
  • Step 2: Add Database Details
  • Step 3: Create Migration & Model
  • Step 4: Add Route
  • Step 5: Create Controller By Artisan
  • Step 6: Create Blade View
  • Step 7: Make Upload Directory
  • Step 8: Start Development Server


Live Demo Laravel Crop image Before Upload.

#laravel crop image before upload, #laravel crop and resize image using cropper.js #ajax image upload and crop with jquery and laravel #crop and upload image ajax jquery laravel #crop image while uploading with jquery laravel #image crop and upload using jquery with laravel ajax