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Reading list

What is Deep Learning? DL vs ML vs AI Why Deep Learning is So Popular?
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Gradient Descent Algorithm Variants of Gradient Descent Algorithm
Introduction to Loss Function Binary and Categorical Cross Entropy
Why Do We Need Activation Functions? Linear Activation Function Sigmoid and Tanh Function Softmax How to Select Right Activation Function?
Perceptron Weights in Perceptron Introduction to Artificial Neural Network
Understanding Forward Propagation Mathematically Understand Backward Propagation Mathematically Implementing Neural Networks in Python
Problems with Gradient Descent Gradient Descent with Momentum Adagrad and Adadelta
Introduction to Learning Rate Schedulers
Overview of Deep Learning Frameworks Implementing Neural Networks using Keras Functional API in Keras Implementing Neural Networks using Keras Hyperparameter Tuning of MLP in Keras
Understanding Early stopping Understanding Dropout Vanishing and Exploding Gradients Weights Initialization Techniques Implementing Weight Initializing Techniques Batch Normalization Image Augmentation Techniques Image Generator and Fit Generator Model Checkpointing Implementing Model Checkpointing Dealing with Class Imbalance Ensemble Deep Learning
Introduction to GPU and TPU TensorRT
Introduction to Unsupervised Learning How to Solve Unsupervised Learning Problems? Introduction to Autoencoders Implementing Autoencoders
A Beginners Guide to Codeless Deep Learning
TensorFlow Serving Build Deep Learning Models for Android
Introduction to PyTorch and Tensors Mathematical and Matrix Operations in PyTorch Important PyTorch Modules Implement CNN in PyTorch Transfer Learning in PyTorch Working with Text Data in PyTorch Building a RNN model in PyTorch Autoencoders in PyTorch

TensorFlow vs Keras: Which is a Better Framework?

Mounish V
Mounish V Last Updated : 18 Jun, 2024
3 min read

Introduction

Tensorflow and Keras are well-known machine learning frameworks for data scientists or developers. In the upcoming sections we will examine the pros, downsides, and differences between these libraries. We will also explore Tensorflow vs Keras in this article.

Overview

  • Learn about Keras vs TensorFlow.
  • Learn how they differ from each other.
  • Find out which is more suited for you.
  • Learn the pros and cons of both these frameworks.

Table of contents

What is TensorFlow?

TensorFlow is a robust end-to-end Deep Learning framework. TensorFlow APIs are organized in a hierarchical structure, with higher-level APIs building on lower-level APIs. Machine learning researchers use low-level APIs to create and test new algorithms. 

What is Keras?

Keras is a Python-based deep learning API, Keras is simple, yet not simplistic. Keras decreases the cognitive load on developers, allowing them to focus on the most important aspects of the problem.

It is flexible, adhering to the principle of increasing complexity disclosure: basic tasks are quick and straightforward, while advanced workflows can be achieved through clear, incremental steps. It boasts industry-leading performance and scalability, and is used by organizations such as NASA, YouTube, and Waymo.

TensorFlow vs Keras

Feature TensorFlow Keras
Developed By Google Brain François Chollet (now part of TensorFlow)
API Level Low-level and high-level High-level
Flexibility Highly flexible, supports custom operations and layers Less flexible, primarily for standard layers and models
Ease of Use Steeper learning curve, more control User-friendly, simple to implement
Deployment Extensive support (TensorFlow Lite, TensorFlow Serving) Uses TensorFlow for deployment
Performance Optimized for performance, supports distributed training Optimized through TensorFlow backend
Community Support Large community, extensive resources Large community, integrated within TensorFlow
Use Case Suitable for complex, large-scale projects Ideal for rapid prototyping and experimentation
Data Handling Advanced data handling with tf.data API Simplified data handling with built-in methods
Visualization TensorBoard for advanced model visualization Supports TensorBoard

Pros and Cons

Let us now explore pros and cons of Tensorflow and Keras.

TensorFlow

Pros:

  • Tensor flow outperforms all other top platforms in terms of graph representation for a given data set.
  • Tensor flow offers the benefit of supporting and using a wide range of backend software.
  • It offers the finest community support and is also useful for debugging sub-graphs.
  • Easy to extend since it allows you to create custom blocks to build on new concepts.

Cons:

  • The tensor flow is slower than other platforms of the same type.
  • Creating custom layers and operations in can be intricate and time-consuming. For example, designing a novel convolutional layer for a specialized image processing task may require significant effort and expertise.

Keras

Pros:

  • It is intended to be simple and intuitive. It encapsulates most of TensorFlow’s low-level complexity, making it an ideal alternative for those new to deep learning.
  • It supports rapid prototyping of neural networks, allowing you to experiment with alternative topologies quickly.
  • Its code is often more succinct and readable than TensorFlow code.
  • It has been included as the official high-level API in TensorFlow from version 2.0, assuring compatibility and synergy between the two.

Cons:

  • It has little versatility, despite its considerable simplicity. It may not be the ideal option for sophisticated users who need exact control over all aspects of their models.
  • Customizing layers and processes is tough.

Also Read: Top 6 Deep Learning Frameworks You Should Know in 2024

Conclusion

TensorFlow excels in flexibility and scalability for intricate projects, offering extensive control over neural network design, making it ideal for large-scale applications like Google’s search algorithms. In contrast, Keras shines with its user-friendly interface, perfect for rapid prototyping, such as quickly building and testing a sentiment analysis model for customer reviews. Now you can make a choice on which framework to adopt and find out which is best suited for you – TensorFlow or Keras!

Frequently Asked Questions

Q1. Are there performance differences between using Keras and TensorFlow directly?

A. Performance differences between using Keras and TensorFlow directly are minimal because Keras operations ultimately get compiled into TensorFlow computational graphs. 

Q2. Is Keras a part of TensorFlow 2.0?

A. Yes, TensorFlow 2.0 integrates Keras as its official high-level API. This helps for a unified experience for both high-level and low-level operations.

beginnersdata sciencekerasmachine learningtensorflowtensorlow
Mounish V
Mounish V

I'm a tech enthusiast, graduated from Vellore Institute of Technology. I'm working as a Data Science Trainee right now. I am very much interested in Deep Learning and Generative AI.

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