Keras: Explore This Deep Neural Network API

AI is revolutionizing how work gets done in nearly every industry. It helps people generate content rapidly, streamline and automate tasks, make better-informed decisions, and analyze data with unprecedented depth and speed.

At the core of this AI transformation are neural networks, deep learning, and machine learning algorithms. They tirelessly parse through extensive datasets, identifying trends and connections that would otherwise go unnoticed, and amplifying AI functionalities.

Keras stands out in the AI toolkit. Keras was crafted specifically to develop deep learning models that give AI capabilities that were once only science fiction.

In this guide, we explore Keras, its standout features and architecture, and its role in AI.

Table of contents:

• What is Keras?
• Key features
• The Keras architecture
• Working with data
• Building models
• Advanced features
• Keras and TensorFlow: A powerful duo
• Real-world applications
• Community and resources
• Future directions

What is Keras?

Keras is a high-level neural network API for building machine learning and deep learning models. It’s open source, meaning you can interact with its core features and integrate them into your application for free. The framework is also written using Python, which is easy to learn and master, even for beginners.

With its different APIs, Keras provides a layer of abstraction over underlying algorithms so developers and engineers can focus more on implementing their core application logic. This results in faster development and deployment of applications. Keras is also designed with iterative development and enhanced experimentation in mind, helping professionals bring highly functional and reliable applications to the market.

Keras is compatible with other deep learning frameworks, TensorFlow, PyTorch, Theano and other back-end products. This means you can create AI apps that work in different environments, including on the cloud and Android and iOS devices.

With version 3.0, Keras brings even better integration with TensorFlow, Ajax, and PyTorch. This allows you to access even more algorithms and models to integrate into your application for extra functionality. Keras 3.0 allows developers to use it as a low-level language for building custom models.  

Key features

Keras makes it easy to quickly build, train, and deploy AI applications at scale. Here are its core features:

• User-friendly design. This aspect makes it easy to develop and train neural networks with Keras. Its modular and extensible characteristics mean you can reuse building blocks and tap into more features from other deep learning frameworks.
• Support for more networks. Out of the box, Keras supports the development of convolutional neural networks and recurrent networks.
• Abstraction. Keras features pre-built algorithms for specialized tasks, including natural language processing (NLP), computer vision, and generative AI. You can tap into these existing functionalities to develop custom applications quickly.
• Cross-platform compatibility. Keras integration with JAX and TensorFlow lets you build AI apps for multiple platforms, including mobile, server, browser, and embedded devices.
• Fast experimentation. Keras provides the necessary tools for testing and experimenting with neural networks. It also offers actionable messages for tracking errors and other performance metrics.
• Flexibility. Keras allows you to run computations on both the CPUs and GPUs, making it a great tool for those with limited resources.

The Keras architecture

Keras architecture consists of two core models: functional API and the sequential model.

The functional API (which Keras recommends for most use cases) lets you create highly flexible models with multiple outputs and inputs. The sequential model is more rigid—use it to build apps using multiple layers, but only one input and one output tensor.

The Keras architecture comes with its own jargon. Some frequently used terms, and their definitions, include:

• Keras models. Apart from the sequential model and functional API (discussed above), Keras also supports model subclassing: building custom models from scratch.
• Layers. These components serve as the basic building blocks of neural networks, specifically for input and output computation.
• Callbacks. These are objects that allow you to perform specific actions during training. For instance, you can use a callback to stop the ongoing training or save data.
• Modules. A module contains methods and classes useful in the development of neural networks.
• Tensors. These multi-dimensional arrays help with data representation and gradient computation.
• Activation functions. These components are part of Keras layers and allow models to identify more intricate patterns and relationships in data.
• Optimizers. Optimizers help in adjusting Keras models for the best performance. For example, you can use an optimizer to adjust the learning rate of your model.

Working with data

AI apps have to be trained on large datasets to improve. But the data needs to be cleaned and formatted, and you should add more variety to it through data augmentation.

Keras is here to help with exactly that. Its preprocessing layers are perfect for tidying up your data. For example, the text vectorization layer can turn sentences into tokens for natural language processing. Keras can also prepare your images by resizing, rescaling, or cropping layers, and with image classification.

As for handling datasets, Keras plays well with NumPy arrays and TensorFlow data types. Using NumPy, a Python library, you can easily convert or store your data in arrays, making it accessible for whatever you need next. These arrays are quite flexible, storing all sorts of data formats from integers to floats.

For the heavy-duty stuff, TensorFlow data types are great for managing bigger datasets and come with more advanced options for preprocessing and loading your data.

Building models

Since Keras provides a layer of abstraction of underlying complex algorithms, developing neural network models is relatively easy. The neural network model creation process usually goes like this:

Note: In this tutorial, we will use Google Colab—an online coding environment.

1. The first step is to import the necessary dependencies into your coding environment. In our case, we will be using Keras in combination with Tensorflow. Run the following code.

--CODE language-markup--
import tensorflow as tf
from tensorflow import keras

2. Once you’ve installed the TensorFlow library and imported Keras to our workflow, download the training dataset. In this tutorial, we will use the fashion_mnist, which contains images of different products. Our aim is to create a model to identify unique images in the dataset.

Create a variable named data and assign it to the dataset, as shown below.

--CODE language-markup--
data = tf.keras.datasets.mnist

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3. Use the following code to download and load the datasets. At the same time, this splits it into training and testing sets.

--CODE language-markup--
(x_train, y_train),(x_test, y_test)=data.load_data()

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4. Check the image size using the following code.

--CODE language-markup--
x_test.shape

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5. Next, scale the images to a proper training range with this code.

--CODE language-markup line-numbers--
x_train, x_test = x_train/255, x_test/255

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6. Now that you’ve preprocessed the datasets, create a Keras Sequential model. Use the following code to pass four layers to the constructor along with the necessary parameters.

--CODE language-markup line-numbers--
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28,28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax'),
])
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7. With the model created, include the optimizer, loss function, and proper metrics using this code.

--CODE language-markup--
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
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8. Add the training datasets and epochs (number of iterations on the training data) with the following code. Note: You can also add the batch_size, validation steps, and other parameters in the fit function—but these are optional.

--CODE language-markup line-numbers--
model.fit(x_train, y_train, epochs=5)
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9. The training should run five times and then display the following outputs. An accuracy of 0.9 or higher means your model can perform the task it has been trained to do with greater accuracy. In our case, the accuracy is 0.9758, indicating a high success rate.

10. You can test the model further using the evaluate function and passing in the test dataset (see step three.) Your model should still have a high accuracy score.

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Epochs, batch size, and metrics are all important factors when training models.

Epochs determine the number of iterations the model makes on the training data. Increasing the epochs size can enhance the “learning” process.

Batch size is the number of training samples processed in each iteration. Having smaller batch sizes can facilitate more efficient memory computations.

Advanced features

While the sequential model is more straightforward and useful in creating neural networks, it’s confined to models with single input and output layers.

Functional API is the best choice for building more complex model networks. You can use it to develop models that support multiple inputs and outputs and shared layers. These extra layers help models process data more efficiently and identify more complex patterns and relationships.

You can also take advantage of pre-trained models for transfer learning. These can perform image classification, text generation, computer vision, and natural language processing out of the box, deploying tailored applications faster.

You can customize neural networks using Keras' back-end functions and utilities. For instance, the clear_session function lets you manage the global state and achieve high memory efficiency, while set_image_data_format enables you to transform images.

Keras and TensorFlow: A powerful duo

You can integrate Keras with TensorFlow modules for more enhanced functionality. Already, TensorFlow grants you access to a well-developed ecosystem consisting of different tools, Python libraries, and plugins. With TensorFlow’s intuitive APIs, you can build AI applications that run in different environments.

So, integration of Keras in TensorFlow takes things up a notch, allowing you to create more streamlined models and neural networks. You can use Keras’ high-level APIs and TensorFlow’s low-level APIs to build your models.

And if you don’t like the available pre-trained models, Keras compatibility with Tensorflow lets you develop custom AI solutions from scratch. For instance, you could use these tools to create a unique image recognition system or specialized chatbot.

Real-world applications

Keras has use cases across many industries. Examples of deep learning applications developed with Keras include:

• Netflix. Netflix uses Keras to build and train machine learning models to deliver personalized content and improve user experience.
• Uber. The company uses TensorFlow’s low-level APIs and Keras’ high-level APIs to build its custom AI systems.
• YouTube Discovery. Keras is powering up YouTube’s modeling infrastructure. Its consistent, powerful APIs allow models to be trained faster to generate suitable recommendations.

You can also use Keras to build AI applications for image classification and segmentation, object detection, and image enhancement. Keras also supports the development of generative AI systems.

Community and resources

If you’re interested in learning Keras, you can enroll in online courses and tutorials. Platforms like Udemy and Udacity offer different machine learning and deep learning courses that can equip you with the right knowledge and skills.

Documentation on the official Keras site can also help you understand how Keras works and use it more effectively.

As you master the basics, join online communities on platforms like GitHub and Reddit to connect with peers and mentors. You can also share your code on GitHub and collaborate on other Keras development projects to build your skills.

Future directions

AI is developing rapidly. New versions of deep learning frameworks like PyTorch and TensorFlow are regularly released, revolutionizing how we create AI applications. So, it’s important to stay up to date with the latest AI developments and trends.

Keras’ high-level APIs will continue transforming machine learning and data science fields, allowing professionals to experiment with their applications and make them more reliable. Keras’ high-level abstraction over complex concepts will make it possible for businesses of all sizes to build and implement custom AI systems.

Find deep learning jobs on Upwork

Keras is a deep learning library that can help you develop AI applications. Whether you want to build a generative AI tool or create a platform powered by computer vision to detect objects, Keras can get you started. Its integration with TensorFlow and other frameworks even lets your application run on different types of devices so it can reach more users.

But building neural networks requires technical skills and knowledge, which take time to master. Consider working with Keras freelancers on Upwork to help quickly integrate Keras into your workflow.

And if you’re an AI expert, you can find diverse deep learning jobs on Upwork to grow your portfolio. Get started today!

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