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Table of contents
1.
Introduction
2.
Deep Learning
3.
Frameworks in Deep Learning
3.1.
TensorFlow 
3.2.
MXNet 
3.3.
CNTK
3.4.
PyTorch
3.5.
Caffe2 
3.6.
Theano 
3.7.
Keras 
3.8.
SciKit
4.
Deep Learning Frameworks Comparison
5.
Frequently Asked Questions
5.1.
What is Bidirectional Recurrent Neural Network?
5.2.
What are Transformers?
5.3.
What is Vectorization?
6.
Conclusion
Last Updated: Mar 27, 2024

Deep Learning Frameworks Comparison

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Introduction

The enthusiasm for deep learning research and application has continued to rise. And various open-source deep learning frameworks have emerged one after another. Including TensorFlow, Keras, MXNet, PyTorch, CNTK, Theano, Caffe, DeepLearning4j, Lasagne, and Neon.

Google, Microsoft, and other business and tech giants have joined this deep learning framework war. The most mainstream frameworks are TensorFlow, Keras, MXNet, and PyTorch.

In this article, we will briefly compare these four mainstream deep learning frameworks from several different aspects.

Deep Learning Frameworks Comparison

Deep Learning

Deep learning is a set of machine learning algorithms. That uses multiple layers that correspond to a different level of abstraction at each level. It consists of an input layer, an output layer, and several hidden layers.

It is used for voice synthesis, image processing, handwriting recognition, object detection, prediction analytics, and decision-making. Deep learning can be broadly classified into Hybrid, Discriminative, and Generative.

Deep learning consists of supervised or unsupervised learning techniques. Based on many layers of artificial neural networks. That can learn hierarchical representations in deep architectures.

It is an extended version of an artificial neural network. Deep Learning architectures consist of multiple processing layers. Each layer can produce non-linear responses based on the data from its input layer. Deep learning is a new era of machine learning.

Deep learning includes both supervised and unsupervised learning paradigms of machine learning. Machine learning and deep Learning help provide intelligence. To the system that can predict the future using past data.

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Frameworks in Deep Learning

TensorFlow 

TensorFlow is an open-source framework developed by Google Brain Team. It is currently released under the Apache 2.0 open-source license. TensorFlow is designed for large-scale distributed training and inference. Its computation graph went from static in the pre-2.0 versions to dynamic, with a static option that can be enabled anytime.

TensorFlow

MXNet 

MxNet is a machine-learning framework with APIs in languages. Such as R, Python, and Julia, which has been adopted by Amazon Web Services (AWS).

MXNet

CNTK

Microsoft Cognitive Toolkit (CNTK) is an Open source DL framework developed by Microsoft Research. For training and testing many types of NN across multiple GPUs or servers. CNTK supports different DL architectures. Like Feedforward, Convolutional, Recurrent, LSTM, and Sequence-to-Sequence NN.

CNTK

PyTorch

Facebook has rewritten the Lua-based DL framework Torch in Python. And refactored all modules on the Tensor level, which led to the release of PyTorch. As the most popular dynamic framework, PyTorch embeds primitives. For constructing dynamic dataflow graphs in Python. Where the control flow is executed in the Python interpreter.

PyTorch

Caffe2 

Caffe2 is deployed at Facebook to help developers. And researchers train large machine-learning models. And deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools. Allowing them to run large-scale distributed training scenarios. And build machine learning applications for mobile.

Caffe2

Theano 

Theano is a Python library that was developed at the University of Montreal. Theano provides the strongest competition to TensorFlow, given the numerous similarities. This includes the capability to support multiple CPUs and GPUs.

Theano

Keras 

Keras is an open-source library that is also written in Python. It is primarily used for building Neural Networks. Follows an object-oriented design throughout its interface. Resulting in a consistently cleaner framework. Keras is capable of working with multiple backend sources.

Keras

SciKit

SciKit-Learn is not technically considered a Deep Learning framework. However, its ease of use and general-purpose approach. Have paved the way for other frameworks to improve their principles. SciKit is an open-source machine Learning library. That makes use of Python. It uses a simple fit-predict workflow model. Allowing supervised or unsupervised Learning.

SciKit

Deep Learning Frameworks Comparison

Frameworks

Features

Flexibility

Difficulty of learning

TensorFlow  TensorFlow is a second-generation artificial intelligence. Learning system developed by Google Brain based on disbelief. Its name comes from its operating principle. It was released under Apache 2.0. Open-source license on November 9, 2015.

TensorFlow mainly supports the form of static calculation graphs. The structure of the calculation graph is relatively intuitive. But it is very complicated and troublesome in the debugging process. And some errors are more difficult to find. 

Although the dynamic graph mechanism. Eager Execution was released at the end of 2017. Adding support for dynamic calculation graphs. The original static calculation graph form is still used. Moreover, TensorFlow has the TensorBoard application, which can monitor. The running process and visualize the calculation graph.

TensorFlow is relatively unfriendly. Has a large gap with languages like Python. It is like redefining a programming language based on a language. It is more complicated when debugging. Every time the version is updated. The various interfaces of TensorFlow. Often have huge changes, which also greatly increases its learning time.
Keras Keras is an open-source neural network library. Written in Python, which can run on TensorFlow, CNTK, Theano, or MXNet. To achieve rapid experiments of deep neural networks. It focuses on user-friendliness, modularity, and scalability. Its primary author and maintainer is Google engineer François Chollet. Keras is a high-level API based on multiple different frameworks. It can quickly design and build models. It supports both sequential and functional design model methods. It can quickly turn ideas into results. However, due to the high degree of encapsulation. Modifications to existing models may need to be more flexible. Keras is a high-level API based on various deep-learning frameworks. Pursuing simplicity, fast model building, and easy to use. And can quickly realize what you want. It’s very suitable for entry or rapid implementation. However, excessive encapsulation leads to insufficient learning of deep-learning knowledge. And rewriting existing neural network layers is very complicated.
MXNet  MXNet is an open-source, lightweight, portable, and flexible deep learning library. Developed by DMLC (Distributed Machine Learning Community). Allows users to mix symbolic programming modes. And instructional programming mode to maximize efficiency and flexibility. It is currently the deep learning framework officially recommended by AWS. Many of the authors of MXNet are Chinese, and its biggest contribution is Baidu.

MXNet supports both imperative and declarative programming methods. It supports static and dynamic calculation graphs simultaneously. And has packaged training functions that integrate flexibility and efficiency. And it has launched an advanced interface. Gluon with MXNet as the backend, just like Keras.

 

MXNet supports imperative programming and declarative programming at the same time. It’s very flexible, simple, and convenient and supports multiple languages simultaneously, which can reduce the time to learn a new primary language. The upper interface Gluon is also straightforward to use.

 

PyTorch  PyTorch is an open-source deep learning library for Python. Released by Facebook on January 18, 2017, based on Torch. It Supports dynamic calculation graphs. And provides excellent flexibility. PyTorch is a typical representative of dynamic calculation graphs. It is easy to debug and highly modular. It is very convenient to build models. At the same time, it has excellent GPU support.  The migration of data parameters between CPU and GPU is very flexible. PyTorch supports dynamic calculation graphs. Pursues as little packaging as possible. The code is concise and easy to read, And the application is very flexible. The interface continues to use Torch, which has strong ease of use. And can use the advantages of the primary language Python.

Frequently Asked Questions

What is Bidirectional Recurrent Neural Network?

The first one is trained with an input sequence in standard time order. While the other is fed with a reversed input sequence. The output of both RNNs is combined for each time step, either by concatenation or summation.

What are Transformers?

Transformers are deep learning models that use a mechanism called: “Attention”. To boost their training speed. Transformers were developed to solve the problem. Like sequence transduction or neural machine translation. That means any task that transforms an input sequence into an output sequence. This includes speech recognition, text-to-speech transformation, etc.

What is Vectorization?

Vectorization is converting an algorithm. From operating on a single value at a time. To work on a set of values simultaneously. Not all code can take benefit from vectorization. Thus, we need to know if this optimization is necessary for each program.

Conclusion

In this article, we learn about Deep Learning and Frameworks and their comparison. The developers are dedicated to it. And as a dominant field in its own right. It is progressing rapidly. While many frameworks currently exist that are capable in their areas. All factors must be considered to select which one is best suited for Deep Learning. 

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