Tensor-Flow Image Classification: All you need to know about Building Classifiers

Image Classification is defined as a task of extracting information classes from a multiband raster image (i.e. multiband raster is referred as multispectral images and posters up to hundreds of bands). Image classification is a type of task which evens a baby can do. But until recent developments in Artificial Intelligence, Artificial Intelligence Training, Artificial Intelligence Certification, and Artificial Intelligence Online training, it is considered as a difficult task for a machine. For example Self-driving cars can detect objects coming in their path and take quick decisions in real time because of Tensor-Flow Image Classification.

What is Tensor-Flow Classification and why to choose Tensor-Flow?

Tensor-Flow Classification is an Open source machine learning program by Google. It is used for dataflow programming for a variety of tasks. In the graph, the edges denote about the multi-dimensional arrays communicated between them, while the nodes represent the mathematical operations in the graph.

Tensor-Flow is end to end open-source platform for Data Mining Vs. Machine Learning. It contains a variety of tools, libraries, and community resources that enable the researchers to push the state-of-art in Machine Learning and to develop a variety of Machine learning powered applications easily and efficiently. A variety of developers are using Tensor-Flow to solve real word challenging problems.

• Robust ML production everywhere- No matter which language an individual is using he/she can easily train and deploy the models on the device, in the browser or in cloud
• Powerful experimentation- A simple and flexible architecture that can take the new ideas from the concept of code and for the publication purposes faster.
• Easy Model Building- By making use of high-level API like Keras, we can easily build and train Machine learning models, which makes for immediate model iteration and easy debugging.

Different types of Image Classification

Image Classification is further divided into 2 types that are discussed below-

1. Supervised Image Classification- Supervised Image Classification can be referred as the user can select pixels in an image that is the particular representative of the specific classes. Then the user can redirect the image-processing software to use these training sites as a reference for the classification of all other pixels in the image.

2.Unsupervised Image Classification – Unsupervised image classification can be referred as a process in which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection, without using any labeled training samples by Sprintzeal.

Tensor Flow/Keras

Tensor-Flow is end to end open-source platform for machine learning. It contains a variety of tools, libraries, and community resources that enable the researchers to push the state-of-art in Machine Learning and to develop a variety of Machine learning powered applications. In terms of Keras, it is a High level API (Application Programming Interface) language that make use of Tensor-Flow functions as well as other ML libraries like Thenao underneath. The main principle of Keras is its user-friendly interface and its modularity. Keras implement many complex and powerful functions of Tensor-Flow in an easy manner as much as possible. It is designed to configure with Python without any modification or configuration. Tensor Flow support platforms like Linux, Windows Android, and macOS.

Advantages of Tensor-Flow

• Better Library management- As Google backs it, it has the advantages like quick updates, frequent new releases with new features and a seamless performance.
• Community support- Tensor-Flow has the best community support.
• The unique approach of Tensor-Flow allows to monitor and train the progress of our models and keep a track of several metrics.
• Graphs- Tensor-Flow shows the best computational graph visualizations when compared to other libraries like Thenao and Torch.
• Tensor-Flow is designed to make use of various backend software’s like GPUs ASIC etc.

Disadvantages of Tensor-Flow

• Speed- Tensor flow lacks both in usage and speed when it is compared with its competitors.
• No direct support for Windows- If a user is friendly with the Windows environment, then tensor-flow does not satisfy these users. But users can use tensor-flow by installing python or conda package library.
• No support for OpenCL.
• Due to the unique structure of Tensor-Flow it is very hard to find an error and difficult to debug it.
• To use Tensor-Flow, users must have a good knowledge of Linear algebra, Advanced Calculus along with Machine Learning.
• Benchmark Test- Tensor-Flow benchmark scores are low when compared to its competitors.