Keras and TensorFlow: The Power Duo of Deep Learning

In the ever-evolving world of artificial intelligence and machine learning, deep learning has emerged as a transformative force across industries—from healthcare and finance to entertainment and self-driving cars. At the heart of this revolution lie two key tools: TensorFlow and Keras.

Whether you're a budding machine learning enthusiast or a seasoned data scientist looking to explore the world of neural networks, understanding Keras and TensorFlow is essential. In this blog, we’ll take you through a beginner-friendly overview of what they are, how they work, and why they’re such a powerful combination for building deep learning models.

What is TensorFlow?

TensorFlow is an open-source end-to-end platform for machine learning developed by the Google Brain team. Released in 2015, it was designed

to handle the complex computations involved in training large-scale neural networks efficiently.

Key Features of TensorFlow

• Scalability: Supports distributed computing across CPUs, GPUs, and TPUs.

• Flexibility: Can be used for a variety of ML tasks, from image and speech recognition to natural language processing.

• Ecosystem: Includes tools like TensorBoard (visualization), TensorFlow Lite (mobile deployment), and TensorFlow Extended (production pipelines).

• Cross-platform: Works on desktops, mobile devices, browsers, and cloud platforms.

TensorFlow uses dataflow graphs to represent computation. Each node in the graph represents a mathematical operation, while the edges represent tensors—multi-dimensional data arrays that flow between operations.

What is Keras?

Keras is a high-level neural networks API written in Python. Originally developed as an independent project by François Chollet, Keras is now part of the TensorFlow core library (since TensorFlow 2.0).

Its primary goal? To make deep learning accessible and fast to experiment with.

• User-friendly: Simple, consistent interface optimized for common use cases.

• Modular: Building blocks for models, optimizers, layers, and activation functions.

• Pythonic: Designed for Python lovers—no boilerplate code, just clean, concise, readable scripts.

• Integration: Works seamlessly with TensorFlow’s backend, and can also be run on other ML libraries like Theano or CNTK (though TensorFlow is the standard now).

  
  

Keras is ideal for beginners due to its simplicity, while still being powerful enough for research and production-grade model development.

How Do They Work Together?

With the release of TensorFlow 2.0, Keras became the default high-level API for building and training deep learning models. This integration created a best-of-both-worlds scenario: Keras provides simplicity and ease of use, while TensorFlow offers performance and flexibility.

Here's how they work in tandem

• You define your model using Keras' intuitive syntax.

• The computational heavy lifting is done by TensorFlow under the hood.

• You can customize low-level operations if needed using TensorFlow’s backend.

Keras and TensorFlow: A brief history

Keras was developed by François Chollet, a Google engineer, and released in March 2015. The name "Keras" is derived from the Greek word κέρας (keras), meaning "horn" .

Integration with TensorFlow

With the release of TensorFlow 2.0 in 2019, Keras was integrated into TensorFlow as its official high-level API. This integration streamlined model development by combining Keras's simplicity with TensorFlow's robustness.

Evolution to Keras 3

The latest iteration, Keras 3, represents a significant rewrite, transforming Keras into a low-level, cross-framework language. This version allows developers to create custom components—such as layers, models, or metrics—that can be utilized across various platforms, including JAX, TensorFlow, and PyTorch, all from a single codebase .

TensorFlow: Google's Open-Source Powerhouse -Development and Release

TensorFlow was developed by the Google Brain team and released as an open-source project in November 2015. It evolved from DistBelief, an earlier internal deep learning framework used by Google . TensorFlow was designed to facilitate the development and deployment of machine learning models across various platforms.

In May 2016, Google introduced the Tensor Processing Unit (TPU), a custom ASIC designed to accelerate machine learning workloads, particularly those using TensorFlow.

The Synergy Between Keras and TensorFlow

The integration of Keras into TensorFlow marked a significant milestone, combining Keras's ease of use with TensorFlow's comprehensive capabilities. This synergy has made it more accessible for developers and researchers to build, train, and deploy deep learning models efficiently.

The collaboration between Keras and TensorFlow has democratized deep learning, making it more approachable for developers and researchers worldwide. As both frameworks continue to evolve, they remain at the forefront of machine learning innovation, driving advancements across various industries.

Setting up a deep learning workspace

Setting up a deep learning workspace is the first step to building, training, and deploying powerful AI models. Whether you're a beginner setting up your first environment or an experienced developer optimizing your workflow, the right setup makes all the difference.

Here’s a comprehensive guide to setting up a deep learning workspace, tailored for personal projects, research, or production-ready pipelines.

Decide on Your Computing Environment

Deep learning can be computationally intensive, especially when training on large datasets or deep neural networks. Here are your options:

Local Machine (CPU/GPU)

• Good for small to medium-sized projects.

• Easy to iterate and test rapidly.

• Limited resources; not ideal for large models or datasets.

  
  

GPU Setup: Use an NVIDIA GPU with CUDA support for optimal performance.

Cloud Platforms

• Google Colab (free GPUs, easy setup)

• Kaggle Kernels

• AWS / GCP / Azure

• Paperspace Gradient / Lambda Labs

  
  

Cloud platforms offer scalable GPU/TPU access and are ideal for larger workloads.

Jupyter notebooks: The preferred way to run deep-learning experiments

Jupyter Notebooks have become the de facto standard for developing and experimenting with deep learning models. With their interactive cell-based execution, seamless integration with Python libraries, and support for inline visualizations, Jupyter provides a flexible and intuitive environment for machine learning practitioners. Whether you're prototyping neural networks, analyzing training metrics, or documenting your findings with code and commentary in one place, Jupyter makes it easy to iterate quickly and collaborate effectively. It’s no surprise that researchers, educators, and developers alike turn to Jupyter as their preferred deep learning workspace.

Using Colaboratory: Your Cloud-Based Deep Learning Playground

Google Colaboratory, or Colab, is a free, cloud-hosted Jupyter Notebook environment that enables anyone to write and execute Python code through the browser—no setup required. It’s especially popular in the deep learning community due to its free access to GPUs and TPUs, support for TensorFlow, and seamless integration with Google Drive.

Whether you’re training a convolutional neural network or just exploring machine learning concepts, Colab provides a powerful and accessible platform to experiment, collaborate, and share work with others.

First Steps with Colaboratory

To get started:

1. Go to https://colab.research.google.com.

2. Sign in with your Google account.

3. Choose one of the following:

   • New Notebook to start from scratch.

   • Upload a .ipynb file.

   • Open from GitHub or Google Drive.

Each Colab notebook is essentially a Jupyter Notebook, with additional Colab-specific features like commenting, file uploads, and hardware acceleration.

Installing Packages with pip

Colab comes preloaded with many popular Python packages such as TensorFlow, Keras, NumPy, and Pandas. But you can install any additional libraries using pip commands directly in a notebook cell:

!pip install transformers
!pip install scikit-learn

• Use ! to run shell commands.

• Installed packages are available immediately in subsequent cells.

• Keep in mind: all installations are ephemeral—you’ll need to reinstall packages each time you reconnect or start a new session.

Using the GPU Runtime

Deep learning tasks can be very computationally intensive. Thankfully, Colab offers free access to NVIDIA GPUs and TPUs to speed up model training.

To enable GPU support:

1. Go to Runtime > Change runtime type.

2. Select GPU under the Hardware accelerator dropdown.

3. Click Save.

   
   

You can verify the GPU is available with:

import tensorflow as tf
print("Num GPUs Available:", len(tf.config.list_physical_devices('GPU')))

This setup makes Colab an ideal starting point for students, hobbyists, and professionals looking to quickly dive into deep learning experiments without worrying about hardware limitations or local configurations.

First Steps with TensorFlow

 Whether you're working with image recognition, natural language processing, or time-series forecasting, TensorFlow offers powerful tools and a flexible API to get you started quickly. Here’s how you can take your first steps with TensorFlow and begin building models with ease.

Step 1: Installing TensorFlow

If you're using a local machine or a Jupyter environment (like Colab), install TensorFlow using pip:

pip install tensorflow

Step 2: Import TensorFlow

Begin by importing the library:

import tensorflow as tf
print("TensorFlow version:", tf.__version__)

Step 3: Creating Your First Tensor

# Create a constant tensor
hello = tf.constant("Hello, TensorFlow!")
print(hello)

Output:
tf.Tensor(b'Hello, TensorFlow!', shape=(), dtype=string)

Constant Tensors and Variables in TensorFlow

In TensorFlow, tensors represent all kinds of data—from input features and model weights to predictions and gradients. Among these, constants and variables are two important types of tensors that serve different purposes in deep learning workflows.

Let’s take a closer look at what they are, how to create them, and when to use each.

tf.constant() – Immutable Tensors

A constant tensor is an immutable tensor. Once created, its values cannot be changed. These are useful for:

• Fixed configuration values

• Embedding tables that don’t change

• Defining static data (e.g., image sizes or label arrays)

import tensorflow as tf

c = tf.constant([[1, 2], [3, 4]])
print(c)

Output:
tf.Tensor(
[[1 2]
[3 4]], shape=(2, 2), dtype=int32)

tf.Variable() – Tensors That Can Change

A TensorFlow variable is a mutable tensor. It is typically used to store trainable parameters of a model such as:

• Weights and biases of a neural network

• Embedding matrices

• Any quantity that is updated during training

x = tf.zeros(shape=(2, 1))
print(x)

Output:
tf.Tensor(
[[0.]
 [0.]], shape=(2, 1), dtype=float32)

# Assigning a value to a TensorFlow variable
v.assign(tf.ones((3, 1)))

Output:
<tf.Variable 'UnreadVariable' shape=(3, 1) dtype=float32, numpy=
array([[1.],
       [1.],
       [1.]], dtype=float32)>

A few basic math operations

TensorFlow makes basic math operations straightforward. Here's a glimpse:

a = tf.ones((2, 2))     # Create a 2x2 tensor filled with ones
b = tf.square(a)        # Square each element of 'a' (remains [[1. 1.], [1. 1.]])
c = tf.sqrt(a)          # Calculate the square root of each element    	of 'a' (remains [[1. 1.], [1. 1.]])
d = b + c               # Element-wise addition of 'b' and 'c' (results in [[2. 2.], [2. 2.]])
e = tf.matmul(a, b)     # Matrix multiplication of 'a' and 'b' (results in [[2. 2.], [2. 2.]])
e *= d                 # Element-wise multiplication of 'e' and 'd'

Using the GradientTape in tensorflow

Use of tf.GradientTape for calculating gradients in TensorFlow .

import tensorflow as tf

x = tf.Variable(3.0)
y = tf.Variable(2.0)

with tf.GradientTape(persistent=True) as tape:
    tape.watch(x)  # Ensure x is watched
    tape.watch(y)  # Ensure y is watched
    z = x**2 + tf.sin(y)

dz_dx = tape.gradient(z, x)
dz_dy = tape.gradient(z, y)

print(f"dz/dx: {dz_dx.numpy()}")
print(f"dz/dy: {dz_dy.numpy()}")

del tape

Output:
dz/dx: 6.0
dz/dy: -0.416146844625473

Explanation:

1. Import TensorFlow: We start by importing the TensorFlow library.

2. Define Variables: We create TensorFlow Variable objects x and y. tf.Variable is specifically designed to hold values that can change during training, and they are automatically tracked by GradientTape.

3. Open tf.GradientTape() Context: The with tf.GradientTape() as tape: block sets up a context where operations performed on tensors are recorded. This allows TensorFlow to later compute gradients.

4. tape.watch() (Optional but Recommended for Variables): While GradientTape automatically watches tf.Variable objects, explicitly using tape.watch() can be useful if you're working with tf.Tensor objects that you want gradients with respect to.

5. Define Computation: Inside the GradientTape block, we define a mathematical operation z that involves our variables x and y. In this case, z = x**2 + tf.sin(y).

6. Calculate Gradients:

   • tape.gradient(z, x) calculates the gradient of z with respect to x (dxdz​).

   • tape.gradient(z, y) calculates the gradient of z with respect to y (dydz​).

7. Print Results: We then print the calculated gradient values.

Mathematical Breakdown of the Example:

• Function: z=x**2+sin(y)

• Partial derivative with respect to x (∂z/∂x​): 2x

• Partial derivative with respect to y (∂z/∂y​): cos(y)