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For students learning data science, implementing a neural network from scratch can help you understand many interesting concepts. However, I do not think it is wise to build deep learning models on real datasets unless you have days or weeks to wait for the model to build. So for the vast majority of those who cannot access unlimited resources, using user-friendly open-source deep learning frameworks allows us to immediately implement complex models like convolutional neural networks.
In this article, I will introduce 5 super useful deep learning frameworks and compare each framework to understand when and where we can use which framework.
Table of Contents
1. What is a Deep Learning Framework
2. TensorFlow
3. Keras
4. PyTorch
5. Caffe
6. DeepLearning4j
7. Comparison of These Deep Learning Frameworks
1. What is a Deep Learning Framework
Let’s understand this concept with an example. Consider the following set of images:
This image contains various categories, such as cats, camels, deer, elephants, etc. Our task is to classify these images into their respective classes (or categories). Google search tells us that convolutional neural networks (CNNs) are very effective for such image classification tasks. So we need to implement this model, but if you start writing a CNN from scratch, it may take days (or even weeks) to get a working model, and this is where deep learning frameworks really change the awkward situation.
There’s no need to write hundreds of lines of code; we just need to use a suitable framework to help us quickly build such models. Here are some key features of a good deep learning framework:
1. Optimized for performance
2. Easy to understand and code
3. Good community support
4. Parallel processing to reduce computation
5. Automatic gradient computation
2. TensorFlow
TensorFlow is developed by researchers and engineers from the Google Brain team. It is the most commonly used software library in the field of deep learning (although others are quickly catching up).
The main reason TensorFlow is so popular is that it supports multiple languages to create deep learning models. For example, Python, C++, and R, it has appropriate documentation and guided tutorials. There are many components in TensorFlow, two of the more prominent ones are:
1. TensorBoard: Helps achieve effective data visualization using data flow graphs
2. TensorFlow: For quickly deploying new algorithms/experiments
The flexible architecture of TensorFlow allows us to deploy our deep learning models on one or more CPUs (and GPUs). Here are some common use cases for TensorFlow:
1. Text-based applications: Language detection, text summarization
2. Image recognition: Image captioning, face recognition, object detection
3. Sound recognition
4. Time series analysis
5. Video analysis
Installing TensorFlow is also a very simple task:
Only for CPU:
For CUDA-supported GPU cards:
Learn how to use TensorFlow to build neural network models from the following comprehensive tutorials
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An Introduction to Implementing Neural Networks using TensorFlow
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TensorFlow tutorials
3. Keras
For Python enthusiasts, Keras is the perfect framework to start your deep learning journey. Keras is written in Python and can run on top of TensorFlow (as well as CNTK and Theano). The TensorFlow interface can be a bit challenging as it is a low-level library, and new users may find it difficult to understand certain implementations. On the other hand, Keras is a high-level API focused on rapid 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. It can run seamlessly on both CPUs and GPUs. At the same time, Keras helps deep learning beginners correctly understand complex models, designed to minimize user operations and make models very easy to understand.
We can roughly categorize Keras models into two types:
1. Sequential: The layers of the model are defined in a sequential manner, meaning that when we train the deep learning model, these layers are implemented in order. Here is an example of a sequential model:
2. Keras Functional API: Typically used to define complex models, such as multi-output models or models with shared layers, see the following code for a practical understanding:
Keras has various architectures as described below for solving a wide range of problems:
1. VGG16
2. VGG19
3. InceptionV3
4. Mobilenet and many more
You can refer to the official Keras documentation for detailed information on how the framework works: https://keras.io/
It only takes one line of code to install Keras:
If you want to go further on how to use Keras to implement neural networks, you can check:
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Optimizing Neural Networks using Keras
4. PyTorch
PyTorch is the most flexible among all the frameworks I have studied. It is a port of 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 and uses dynamic computation graphs. Its Autograd package builds computation graphs from tensors and automatically computes gradients. Tensors are multi-dimensional arrays, similar to numpy’s ndarrays, and can also run on GPUs.
PyTorch does not use predefined graphs with specific functions but provides us with a framework to build computation graphs that can even be modified at runtime. This is useful in cases where we do not know how much memory we will need when creating a neural network.
You can use PyTorch to tackle various deep learning challenges, including:
1. Images (detection, classification, etc.)
2. Text (NLP)
3. Reinforcement learning
For installation steps and how to build your first neural network using PyTorch, refer to the following documents:
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Learn How to Build Quick & Accurate Neural Networks using PyTorch – 4 Awesome Case Studies
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PyTorch tutorials
5. Caffe
Caffe is another popular deep learning framework aimed at the field of image processing. The author is Jia Yangqing, a PhD from the University of California, Berkeley, who currently works at Alibaba as Vice President of Technology, leading the development of big data computing platforms. It is worth noting that Caffe’s support for recurrent networks and language modeling is not as strong as the aforementioned three frameworks. However, Caffe stands out for its speed in processing and learning images. This easily becomes its main USP (Unique Selling Proposition).
Caffe provides solid support for interfaces in C, C++, Python, Matlab, and traditional command lines. The Caffe Model Zoo (a large collection of pre-trained models available for download on large datasets) framework allows us to access pre-trained networks, models, and weights that can be used to solve deep learning problems. These models are suitable for the following tasks:
1. Simple regression
2. Large-scale visual classification
3. Siamese Networks for image similarity
4. Speech and robotics applications
You can check Caffe’s installation and documentation for more details.
6. DeepLearning4j
For Java programmers, this is the ideal deep learning framework. DeepLearning4j is implemented in Java, making it more efficient than Python, and it uses a tensor library called ND4J, which provides the ability to handle n-dimensional arrays. This framework also supports GPUs and CPUs.
DeepLearning4j treats the tasks of loading data and training algorithms as separate processes, providing great flexibility. It is also suitable for different data types:
1. Images
2. CSV
3. Plain text, etc.
You can build deep learning models using DeepLearning4j, including:
1. Convolutional Neural Networks (CNN)
2. Recurrent Neural Networks (RNN)
3. Long Short-Term Memory (LSTM) and many other architectures
You can check DeepLearning4j’s installation and documentation for more details.
7. Comparison of the Five Deep Learning Frameworks
We have introduced the five most popular deep learning frameworks. Each has its own unique feature set; some frameworks handle image data well but cannot parse text data. Other frameworks perform well on both image and text data, but their inner workings may be difficult to understand. Below we will compare our five deep learning frameworks using the following criteria:
1. Community support
2. The languages they use
3. Interfaces
4. Support for pre-trained models
The table below compares these frameworks:
All of these frameworks are open-source, support CUDA, and have pre-trained models to help you get started. But what should be the right starting point, and which framework should you choose to build your (initial) deep learning model?
1. TensorFlow
TensorFlow is suitable for image and sequence-based data. If you are a beginner in deep learning or lack a solid understanding of mathematical concepts such as linear algebra and calculus, the steep learning curve of TensorFlow may be daunting. This aspect may be complex for someone just starting out. My advice is to keep practicing and exploring the community. Once you have a good understanding of the framework, implementing deep learning models will be very easy for you.
2. Keras
Keras is a very reliable framework to start your deep learning journey. If you are familiar with Python and have not done some advanced research or developed some special types of neural networks, Keras is right for you. It is more about getting results rather than getting bogged down in the complexities of models. Therefore, if you get a project related to image classification or sequence models, start with Keras, as you can quickly achieve a working model.
Keras is also integrated into TensorFlow, so you can also use tf.keras to build models.
3. PyTorch
Compared to TensorFlow, PyTorch is more intuitive. A quick project that includes both frameworks will make this very clear. Even if you do not have a solid background in math or pure machine learning, you can understand PyTorch models. As the model progresses, you can define or manipulate the graphs, making PyTorch more intuitive. PyTorch does not have visualization tools like TensorBoard, but you can use libraries like matplotlib at any time.
4. Caffe
Caffe is very effective when we build deep learning models on image data. However, when it comes to recurrent neural networks and language models, Caffe falls behind the other frameworks we have discussed. The main advantage of Caffe is that you can build deep learning models even if you do not have strong knowledge of machine learning or calculus. Caffe is mainly used to build and deploy deep learning models for mobile phones and other computation-constrained platforms.
5. DeepLearning4j
As I mentioned earlier, DeepLearning4j is a paradise for Java programmers. It provides extensive support for different neural networks, such as CNN, RNN, and LSTM. It can handle large amounts of data without sacrificing speed.
8. Conclusion
Remember, these frameworks are essentially just tools to help us achieve our ultimate goal. Choosing them wisely can save a lot of effort and time. The following image provides a detailed infographic of each deep learning framework we covered. You can choose to download, print, and use it the next time you build a deep learning model!
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