Software Components in Deep Learning

 Keras Vs TensorFlow

Introduction to Keras

Keras is a high-level Python-based open-source deep learning API that allows users to easily create, train, and deploy neural networks. François Chollet created it, and it is currently part of the TensorFlow project. Keras is a popular choice for both novices and professional deep learning practitioners because it is user-friendly, modular, and extendable.

Keras's straightforward and intuitive API is one of its key advantages. Users can simply change numerous factors such as the number of layers, activation functions, and optimization techniques by defining their neural network design with a few lines of code. Keras also offers pre-trained models for basic tasks like image classification and natural language processing, making it simple to begin using deep learning.

Keras works with a variety of backends, including TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). This enables users to quickly switch between frameworks or take advantage of the unique features of each backend.

Overall, Keras is a strong tool for creating and training neural networks with a high level of abstraction and simplicity. Its modular and flexible design makes it a popular choice among deep learning researchers and developers.

Introduction to TensorFlow

TensorFlow is a famous open-source deep learning framework created by Google that enables users to create, train, and deploy neural networks. It debuted in 2015 and has since grown to become one of the most popular deep-learning frameworks.

TensorFlow is built on a dataflow graph paradigm, in which computations are represented as directed graphs. The edges in the graph represent the data that travels between the nodes in the graph, and the nodes in the graph indicate the operations. This enables effective parallel computation and allows for simple distribution of the calculation across numerous CPUs or GPUs.

TensorFlow's flexibility and scalability are two of its primary advantages. It supports several different neural network architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). TensorFlow also includes Keras, a high-level API that allows users to easily design and train neural networks.

TensorFlow is widely used in industry and academics for image and audio recognition, natural language processing, and reinforcement learning, among other things. It is maintained by a huge and active developer and researcher community, and it is continually updated with new features and enhancements.

Overall, TensorFlow is a sophisticated and adaptable deep learning framework that offers a diverse set of tools and resources for developing and training neural networks.

 Relationship between Keras and TensorFlow

Keras is a high-level deep learning API with an intuitive interface for creating neural networks. It may be used with a variety of deep learning frameworks, including TensorFlow.

Google's TensorFlow is a prominent open-source deep learning framework. It offers a low-level interface for creating and training neural networks, as well as higher-level APIs such as Keras.

Keras may be used as a frontend API for TensorFlow, which means you can construct your neural network architecture in Keras and then train and optimize the model in TensorFlow. Other deep learning frameworks supported by Keras include Theano and Microsoft Cognitive Toolkit (CNTK).

Generally, Keras offers a simpler, more intuitive interface for creating neural networks, whereas TensorFlow offers a more low-level and adaptable framework for sophisticated deep learning research. They form a potent mix for developing and training deep learning models.

Keras Set Up

Here is a list of some useful dependencies:

• The numpy package supports massive, multidimensional arrays and matrices, as well as high-level mathematical functions.
• Scikit-learn package - Python Swiss army knife for machine learning and data exploration.
• Pillow library - handy for image processing 
• h5py library - used for data serialization for model saving
• Instead, Anaconda Python can be installed, which will automatically install these and many additional libraries required for scientific computing.

Model Training

The fit() function can be used to train a built model with the following parameters:

The number of times the model is exposed to the training set (n epochs). The optimizer attempts to alter the weights at each iteration in order to minimize the objective function.

Batch size: The number of training examples observed prior to a weight change by the optimizer.

Syntax:

history = model.fit(X_train, Y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT)

Predicting the Result

Predictions can be made using a trained model using the following method:

• predictions = model.predict(X)
• Many sorts of output can be computed for a given input design.
• model. evaluate(): compute the n model's loss values.
• model.predict_classes(): compute model category outputs.
• model.predict_proba(): returns the class probabilities.

Model Evaluation

The syntax for the model evaluation of neural networks for processing is shown below.

Syntax:

score = model.evaluate(X_test, Y_test, verbose=VERBOSE) print("Test score:", score[0]) print('Test accuracy:', score[1])

Difference 

The fundamental distinction between Keras and TensorFlow is that Keras is a high-level API that provides an easy-to-use interface for developing neural networks, whereas TensorFlow is a low-level deep learning framework that allows more flexibility and control over neural network construction and training. Here are some key distinctions between Keras and TensorFlow:

Category	Keras	TensorFlow
Use or Accessibility	Kera’s is user-friendly and straightforward to understand, with a simple API that allows users to easily build and train neural networks. Because of its low-level nature, TensorFlow has a steeper learning curve.	To utilize TensorFlow, the user needs to be familiar with tensors, computational graphs, and optimization algorithms.
Flexibility	Kera’s, on the other hand, is more opinionated and offers fewer customization choices.	TensorFlow is a more adaptable framework that enables users to create and fine-tune every part of the neural network, from the optimization technique to the activation functions.
Speed	Kera’s is also quick, although in some cases, it may not be as efficient as TensorFlow.	TensorFlow is well-known for its great performance and scalability, which makes it ideal for large-scale deep-learning projects.
Compatibility with other frameworks:	Kera can be used as a front-end API for a variety of deep learning frameworks, including TensorFlow, Theano, and CNTK.	TensorFlow, on the other hand, works solely with TensorFlow.

Key Points to Remember

Here are some crucial considerations to have in mind while contrasting the two well-known deep learning frameworks Keras and TensorFlow:

• Relationship: TensorFlow is just one deep learning framework that Keras, a high-level neural network API, can be used on top of. On the other hand, Google developed the open-source deep learning framework known as TensorFlow. In essence, Keras serves as an intuitive interface for creating models on top of TensorFlow.

• Level of Abstraction: Keras offers a greater level of abstraction, making it simpler to use and comprehend, especially for beginners. Building, training, and deploying deep learning models is made simpler by its user-friendly and intuitive API. 

• TensorFlow, on the other hand, offers a more complex user interface, providing users more freedom and control but necessitating more coding and a more in-depth comprehension of the underlying ideas.

• Community & Ecosystem: In comparison to Keras, TensorFlow has a larger and more developed ecosystem. There are many resources, tutorials, and pre-trained models available because TensorFlow has a large user base and a thriving community. This broad community support is advantageous to Keras because it is a component of the TensorFlow ecosystem.

• Greater Flexibility and Customization are Possibilities with TensorFlow. It offers low-level functionality and detailed management of the model architecture and training procedure. TensorFlow is suited for sophisticated users who want to experiment with complex models or carry out deep learning research. On the other hand, Keras emphasizes use and simplicity at the expense of certain flexibility and customizability possibilities.

• Options for deployment: TensorFlow provides more options for deployment than Keras. For deploying models on a variety of platforms, including mobile devices, web browsers, cloud platforms, and specialized hardware like GPUs and TPUs, TensorFlow offers tools and frameworks. Since Keras is a high-level API for creating models on top of TensorFlow, it can also make use of these deployment choices.

• Integration: As a result of Keras' recent inclusion in the TensorFlow project, it is now fully integrated with TensorFlow and gains access to all of TensorFlow's powerful features. Users can use the TensorFlow ecosystem to get new features and tools and directly utilize TensorFlow functionality within Keras models.

• Learning Curve: When compared to TensorFlow, Keras has a shallower learning curve. Users may quickly develop and train models with less code because of its clear and straightforward API. TensorFlow has a steeper learning curve and necessitates a deeper comprehension of the underlying mechanics due to its lower-level interface and more complicated ideas.

Conclusion 

Finally, Keras and TensorFlow are two prominent deep-learning tools for creating and training neural networks. TensorFlow is a low-level framework that allows more flexibility and control over the construction and training process, whereas Keras is a high-level API that provides a simple and user-friendly interface for creating and training neural networks.

Despite their differences, Keras and TensorFlow can be used in tandem to use both tools' strengths. Keras can be used as a high-level API for building neural networks on top of TensorFlow, allowing users to benefit from the TensorFlow backends' efficiency and scalability while also enjoying the Keras API's simplicity and ease of use.

References

[1] https://www.projectpro.io/article/keras-vs-tensorflow-the-differences/454

[2] https://keras.io/about/#:~:text=Keras%20is%20the%20high%2Dlevel,solutions%20with%20high%20iteration%20velocity.