Author: Chen ZhiyanThis article is about 3500 words, and it is recommended to read in 5 minutes
This article introduces several very useful deep learning frameworks, their advantages and applications. By comparing each framework, developers can learn how to selectively use them to efficiently and quickly complete project tasks.
For beginners in data science, using open-source deep learning frameworks can greatly simplify the implementation process of complex large-scale learning models. Building models under deep learning frameworks allows you to easily implement complex models like convolutional neural networks without spending days or weeks writing code from scratch. This article will introduce several very useful deep learning frameworks, their advantages and applications, helping developers understand how to selectively use them to efficiently and quickly complete project tasks.
1. Overview of Deep Learning Frameworks
A deep learning framework is an interface, library, or tool that makes it easier and faster for programmers to build deep learning models without needing to understand the details of the underlying algorithms. Deep learning frameworks define models using a collection of pre-built and optimized components, providing a clear and concise method for model implementation.
Using the appropriate framework can quickly build models without writing hundreds of lines of code. A good deep learning framework has the following key features:
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Optimized performance
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Easy to understand and code
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Good community support
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Parallel processes to reduce computation
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Automatic gradient calculation
These five points are also the primary criteria for selecting the top five deep learning frameworks.
2. PyTorch
PyTorch is an interface for the Torch deep learning framework, used for building deep neural networks and performing tensor computations. Torch is a Lua-based framework, while PyTorch runs on Python.
PyTorch is a Python package that provides tensor computation. Tensors are multi-dimensional arrays, similar to numpy’s ndarray, and they can also run on GPUs. PyTorch uses dynamic computation graphs, and its Autograd package generates computation graphs from tensors and automatically calculates gradients.
Unlike predefined graphs with specific functions, PyTorch provides a framework for building computation graphs at runtime, and these graphs can even be modified during execution. This feature is valuable when it is unclear how much memory is needed to create a neural network.
PyTorch is highly flexible and can handle various deep learning tasks, including but not limited to:
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Image (detection, classification, etc.)
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Text (natural language learning)
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Reinforcement learning
3. JAX
Google’s latest JAX is officially defined as NumPy on CPU, GPU, and TPU. It has excellent automatic differentiation capabilities and is a Python library for high-performance machine learning research. Numpy is widely used in scientific computing, but in deep learning, it is often replaced by frameworks like TensorFlow or PyTorch due to its lack of automatic differentiation and GPU acceleration support.
JAX uses XLA to compile and run NumPy on accelerators like GPUs and TPUs. It is very similar to the NumPy API, and almost everything that can be done with numpy can also be done with jax.numpy.
XLA is a tool for JIT compilation optimization in TensorFlow. It can perform operator fusion on computation graphs, merging multiple GPU kernels into fewer kernels to reduce the number of calls, significantly saving GPU memory IO time. JAX does not reinvent the execution engine but directly reuses the XLA backend from TensorFlow for static compilation to achieve acceleration.
JAX performs particularly well in solving partial differential equations. Researchers tested several pre-trained models on both PyTorch and JAX frameworks and found that JAX was about six times faster than PyTorch, showcasing JAX’s great potential in scientific computing, represented by molecular dynamics.
4. TensorFlow
TensorFlow was developed by researchers and engineers from the Google Brain team and is the most commonly used framework in deep learning.
There are two main reasons for TensorFlow’s popularity: first, it is completely open-source and has excellent community support. TensorFlow has pre-written code for most complex deep learning models, such as recurrent neural networks and convolutional neural networks. Second, it supports multiple languages for creating deep learning models, such as Python, C, and R, and has good documentation and guidance.
TensorFlow has many components, among which the most prominent are:
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Tensorboard: helps visualize data effectively using data flow graphs;
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TensorFlow: for rapid deployment of new algorithms/experiments.
TensorFlow’s architecture is flexible, capable of deploying deep learning models on one or more CPUs (and GPUs). Typical TensorFlow application scenarios include:
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Text-based applications: language detection, text summarization
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Image recognition: image captioning, face recognition, object detection
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Sound recognition
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Time series analysis
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Video analysis
5. Keras
Keras is written in Python and can run on top of TensorFlow (as well as CNTK and Theano). TensorFlow’s interface is a low-level library, which may be difficult for new users to understand in some implementations.
Keras is a high-level API designed for quick experimentation. Therefore, if you want quick results, Keras will automatically handle core tasks and generate outputs. Keras supports convolutional neural networks and recurrent neural networks and can run seamlessly on CPUs and GPUs.
Beginners in deep learning often complain about not being able to understand complex models correctly. If you are such a user, Keras is the right choice! Its goal is to minimize user operations and make models truly easy to understand.
Keras has various architectures for solving a wide range of problems, with image classification being one of the most typical applications!
6. Caffe
Caffe is another popular deep learning framework focused on image processing, developed by Yangqing Jia during his PhD at the University of California, Berkeley. It is also open-source!
Although Caffe’s support for recurrent networks and language modeling is not as strong as the previously mentioned frameworks, its most prominent advantage is that it can process over sixty million images per day with a single NVIDIA K40 GPU, taking 1 millisecond/image for inference and 4 milliseconds/image for learning.
It supports interfaces for C, Python, MATLAB, and traditional command lines.
Through the Caffe Model Zoo, frameworks can access pre-trained networks, models, and weights to solve deep learning problems. These models can accomplish the following tasks:
1) Simple recursion
2) Large-scale visual classification
3) Siamese networks for image similarity
4) Speech and robotics applications
7. Deeplearning4j
For Java programmers, Deeplearning4j is the ideal deep learning framework!
Deeplearning4j is implemented in Java and is more efficient than Python. It uses a tensor library called ND4J, which provides the ability to handle n-dimensional arrays (also known as tensors). The framework also supports both CPU and GPU.
Deeplearning4j handles data loading and training algorithms as separate processes, which provides great flexibility. This has become its standout advantage!
Deep learning models built with Deeplearning4j can include:
1) Convolutional Neural Networks (CNNs)
2) Recurrent Neural Networks (RNNs)
3) Long Short-Term Memory (LSTM) and various other structures.
8. Comparison of Deep Learning Frameworks
We have discussed six popular deep learning frameworks, each with its unique characteristics. How will data scientists make a choice among them?
When starting a new project, how do you decide which framework to use? For this, it is necessary to understand the advantages and limitations of each framework. Choosing the right framework can lead to significant efficiency gains. Some frameworks work very well for image data but cannot parse text data; others perform well with both image and text data, but their internal workings can be difficult to understand.
In this section, we will compare the above deep learning frameworks using the following criteria:
1) Community support strength
2) Programming languages used
3) Interfaces
4) Support for pre-trained models
All these frameworks are open-source, support CUDA, and have pre-trained models. But how should one correctly start, and which framework should be chosen to build an (initial) deep learning model? Let’s discuss in detail!
First, let’s talk about TensorFlow. TensorFlow can handle both image and sequence-based data. If you are a beginner in deep learning or do not have a solid foundation in mathematical concepts like linear algebra and calculus, the learning curve for TensorFlow will be quite steep. For beginners, TensorFlow might be too complex. It is recommended to practice continuously, explore the community, and keep reading articles to master the nuances of TensorFlow. Once you have a good understanding of this framework, implementing a deep learning model will be a breeze.
Keras is a very solid framework to kickstart your deep learning journey. If you are familiar with Python and are not doing advanced research or developing a specific type of neural network, Keras is suitable for you. If you have a project related to image classification or sequence models, you can start with Keras and quickly build a working model. Keras is also integrated into TensorFlow, so you can also use tf.keras to build models.
When building deep learning models on image data, Caffe is a good choice. However, when it comes to recurrent neural networks and language models, Caffe falls behind other frameworks. The main advantage of Caffe is that deep learning models can be built without strong machine learning or calculus knowledge. Caffe is primarily used for building and deploying deep learning models for mobile phones and other computation-constrained platforms.
As mentioned earlier, Deeplearning4j is a paradise for Java programmers, providing extensive support for CNNs, RNNs, and LSTMs, capable of handling large amounts of data without sacrificing speed.
Editor: Wang Jing
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