Keras Vs Tensorflow

Keras vs TensorFlow – Which is Better for Career?

Keras vs TensorFlow – Which is Better for Career?

In today’s computer world, there are millions of frameworks evolving in every area. In modern years, Keras and TensorFlow have been recognized as the leading frameworks used by most Data Scientists and Deep Learning newbies.

In this article, we will conduct a Comparative analysis of the two frameworks.

This blog also concentrates on providing relevant knowledge, such as the distinction between TensorFlow and Keras.

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What is Keras?

Keras is a widely used open-source API that includes a neural network framework developed in Python.

It is compatible with the top Deep Learning toolkits, including Microsoft Cognitive, TensorFlow, and Theano.

It enables quicker deep neural network analysis.

Key characteristics of Keras include:

  • User-friendly: Because it is entirely Python-based and has a high-level interface, it is simple to grasp.
  • Modular: It is modular in nature, making it suited for imaginative, versatile, and expressive study. It also relies on both the CPU and the GPU.
  • Supports arbitrary networks: model sharing, multiple input-output models, layer sharing, and many more features.

What is TensorFlow?

TensorFlow is a Machine Learning library that is open source and designed for analytical computing. It is a cross-platform application.

It is compatible with both the Graphical Processing Unit (GPU) and the Central Processing Unit (CPU), as well as TPUs and embedded systems. What exactly are TPUs? Let us first comprehend them before diving into TensorFlow’s features.

Google-developed Tensor Processing Units (TPUs) are utilized to execute Machine Learning tasks.

They are intended to improve flexibility and performance, allowing developers and researchers to construct TensorFlow clusters that employ TPUs and GPUs.

TensorFlow has the following features:

  • It helps with model construction by providing several degrees of ideas for training and developing models.
  • TensorFlow lets us design difficult topologies by controlling the model subclassing API and the Keras functional API.
  • It is robust and platform-independent, allowing us to simply deploy our apps regardless of the language or platform we employ.

Comparison Between Keras vs TensorFlow

Comparison Between Keras vs TensorFlow
OriginFrancois Chollet created a basic Python module for Deep Learning.Google Brain created a collection of Machine Learning libraries.
ArchitecturePure architecture, concise and readableScalable architecture that is both readable and customizable
PerformanceSlow performance since it processes data using Theano or TensorFlow in the backend.Fast speed in backend processing thanks to the profiler
APIsHas a high-level API and is compatible with Theano and CNTK.APIs at both the high and low levels
DebuggingSimple to debugDebugging is difficult.
DatasetSuitable for tiny datasets.Used for large datasets.
CapabilityRapid prototyping and diverse back-end support capabilitiesObject detection and functioning capability


TensorFlow 2.0

When Google faced severe competition from top frameworks such as PyTorch and Keras, it created a second iteration: TensorFlow 2.0—the most popular open-source library offering. It was first made available in the year 2019.

  • TensorFlow 2.0 makes Deep Learning jobs simple to comprehend and execute for novices. Let’s have a look at the new features in TensorFlow 2.0.
  • Tensorflow 2.0 is a powerful framework that can be readily integrated with Python runtime via eager execution. It also eliminates unnecessary APIs and improves stability.
What exactly is eager execution?

Eager execution is a define-by-run interface that requires operations to be executed as soon as they are invoked from Python. Here are some of the advantages of quick execution:

  • Supports dynamic models for quick debugging
  • High-order gradients are quite strong.
  • TensorFlow’s operations are completely supported.

TensorFlow 2.0 – Significant Changes

Here are some of the key features in TensorFlow 2.0 that make it more user-friendly and simpler to use and comprehend.

API Cleanup : Similar to TensorFlow 1.x, there are various APIs that are visible and ready to be expanded. TensorFlow 1.x included several choices, including tf.layers (TensorFlow.layers), tf.slim, and tf.contrib.layers.

There were also several code patterns for math, ML functions, and debugging. TensorFlow 2.0 removes unwanted APIs to simplify things. TensorFlow 2.0 removes certain APIs, including tf.flags, tf.logging, and It has been replaced by a basic Keras version.

Keras as the Elevated API: In TensorFlow 1.x, there were both high-level and low-level APIs that were difficult to comprehend and utilize. Deep Learning projects were similarly difficult to run.

Keras has been designated as an approved high-level API in TensorFlow 2.0. Keras will join us after we install TF2.0 (TensorFlow). As a result, no code bridge is required for the Keras-TensorFlow connection. We can also utilize Keras code with TensorFlow, making it simple to create something unique.

TensorFlow Datasets: It was difficult to learn huge datasets in TensorFlow 1.x. As a result, queuing runners were required. TensorFlow’s queue runners have been fully replaced in the latest version. TensorFlow 2.0 replaces queue runners with

Using input pipelines, large training datasets can now be read clearly. It also accepts NumPy arrays as memory data input. Keras flow functions are simple to utilize and manage with TensorFlow 2.0.

It is simple to use the previous version code in the newer version: The fact that developers can utilize TensorFlow 1.x code while running TF 2.0 is a huge relief. It smooths the transition from version 1.x to version 2.0 and eliminates any critical gaps.

It also enables developers to create stable modifications by changing a section of code as needed by 2.0 improvements.

Which one should you learn: Keras or TensorFlow?

We hope this article on TensorFlow versus Keras provided you with useful Keras and TensorFlow insight. Rather than comparing Keras and TensorFlow, we must learn how to exploit both, as each Machine Learning framework has advantages and disadvantages.

The decision between Keras and TensorFlow is based on their distinct characteristics and the various tasks for which both frameworks are utilized.

Developers and researchers must select frameworks based on the requirements of their activities.

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