Educational

Installing TensorFlow Keras Included

—

by

Saim Khalid
in

1. Introduction

TensorFlow is one of the world’s most widely used deep learning frameworks. Developed and maintained by Google, it is a powerful library for building machine learning models, training neural networks, and deploying AI systems at scale. One of TensorFlow’s most attractive features is that it includes Keras, a high-level neural network API that simplifies deep learning development.

This means you do not need to install Keras separately. Once TensorFlow is installed, the entire Keras API becomes available through tf.keras, giving users immediate access to advanced deep learning tools.

In this comprehensive 3000-word guide, you will learn:

  • What TensorFlow is
  • What Keras is
  • Why Keras is included in TensorFlow
  • System requirements
  • How to install TensorFlow using pip
  • How to verify installation
  • How to troubleshoot installation issues
  • How to use Keras inside TensorFlow
  • And how to start building deep learning models

This article is perfect for beginners, students, researchers, and professionals who want to set up TensorFlow properly and understand how its integration with Keras works.

2. What Is TensorFlow?

TensorFlow is an open-source deep learning and numerical computation framework created by Google Brain. It allows developers to build and train machine learning models using computational graphs and a highly optimized backend.

2.1 Key Features of TensorFlow

  • Highly scalable
  • GPU and TPU support
  • Large community and ecosystem
  • Ready-to-use datasets
  • Industrial-grade deployment tools
  • Works on Windows, macOS, Linux
  • Supports training, testing, and deploying AI models

TensorFlow is widely used in:

  • Computer vision
  • Natural language processing
  • Time-series forecasting
  • Robotics
  • Medical imaging
  • Reinforcement learning
  • And much more

3. What Is Keras?

Keras is a user-friendly deep learning API that simplifies the process of building neural networks. It was originally developed as an independent library but later integrated directly into TensorFlow.

3.1 Why Keras Is Popular

  • Easy to read and write
  • Beginner-friendly
  • High-level abstraction
  • Quick prototyping
  • Runs seamlessly on TensorFlow backend

Its philosophy is “Simple, Flexible, and Powerful.”

4. Why Keras Is Now Included Inside TensorFlow

Before TensorFlow 2.x, developers had to install Keras separately. This created compatibility issues because different versions of TensorFlow and Keras did not always work well together.

To solve this problem, Google integrated Keras directly into TensorFlow as the official high-level API.

4.1 Benefits of Keras Integration

  • No separate installation required
  • No version mismatch issues
  • Simplified development workflow
  • Better performance and optimization
  • Unified documentation
  • Built-in support for GPUs

Instead of installing Keras separately using pip install keras, you now access everything through:

import tensorflow as tf
from tensorflow import keras

This ensures complete compatibility, stability, and optimal performance.

5. System Requirements for Installing TensorFlow

Before installing TensorFlow, make sure your system meets the requirements.

5.1 Python Version

TensorFlow supports:

  • Python 3.8
  • Python 3.9
  • Python 3.10
  • Python 3.11

Older Python versions like 2.x or early 3.x are not supported.

5.2 Operating System Requirements

Windows

  • Windows 10 or later
  • 64-bit required

macOS

  • macOS 11 or later
  • Apple Silicon (M1/M2/M3 chips) supported
  • Intel Macs also supported

Linux

  • Ubuntu or other mainstream distros
  • Virtual environment recommended

5.3 Hardware Requirements

For CPU Installation

  • No GPU required
  • Works on any standard processor

For GPU Installation

Requirements are stricter:

  • NVIDIA GPU
  • CUDA Toolkit
  • cuDNN

GPU installation is more complex and requires additional setup. Most beginners start with CPU installation and later upgrade to GPU support.

6. Step-by-Step Installation of TensorFlow

TensorFlow is installed using the Python package manager pip. Once TensorFlow is installed, Keras becomes available automatically.

Below is the complete installation guide for Windows, macOS, and Linux.

7. Recommended: Create a Virtual Environment

Creating a virtual environment helps avoid conflicts between different Python packages.

7.1 Create Virtual Environment

Use:

python -m venv tfenv

7.2 Activate Virtual Environment

Windows

tfenv\Scripts\activate

macOS/Linux

source tfenv/bin/activate

You will now see (tfenv) at the beginning of your terminal prompt, indicating the environment is active.

8. Installing TensorFlow (Keras Included)

TensorFlow installation is extremely simple.

Use this command:

pip install tensorflow

When installation is complete, you automatically get:

  • TensorFlow Core
  • TensorFlow Datasets
  • Keras API
  • TensorFlow Estimators
  • TensorFlow Lite tools

There is no need to install Keras separately.

9. Verifying TensorFlow Installation

Once installation completes, verify by running:

import tensorflow as tf
print(tf.__version__)

If TensorFlow is installed correctly, it will print the version number, such as:

2.16.0

Next, test Keras:

from tensorflow import keras
print(keras.__version__)

If this runs without errors, Keras is successfully installed through TensorFlow.

10. Testing TensorFlow Operations

Test a simple computation:

import tensorflow as tf

x = tf.constant([2.0, 4.0, 6.0])
y = tf.constant([1.0, 1.0, 1.0])

print(x + y)

If you see a tensor output, your installation works fine.

11. Using Keras After Installing TensorFlow

Since Keras is included with TensorFlow, you can immediately start building models.

Example:

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([

layers.Dense(32, activation='relu'),
layers.Dense(1)
])

model.compile(

optimizer='adam',
loss='mse'
)

print(model.summary())

The code above creates a simple neural network without needing to install anything other than TensorFlow.

12. Installing TensorFlow with GPU Support (Optional)

If you want GPU acceleration, use:

pip install tensorflow[and-cuda]

This automatically installs:

  • CUDA Toolkit
  • cuDNN
  • Required GPU libraries

No manual setup required for most systems.

To verify GPU availability:

print(tf.config.list_physical_devices('GPU'))

If a GPU is detected, your TensorFlow installation is fully GPU-enabled.

13. Common Installation Issues and Fixes

Installing TensorFlow is usually smooth, but sometimes issues occur. Below are the most common problems and solutions.

13.1 Problem: Pip Not Recognized

Fix:

  • Add Python to PATH
  • Reinstall Python

13.2 Problem: Outdated Pip

TensorFlow requires an updated pip.

Run:

pip install --upgrade pip

13.3 Problem: Incompatible Python Version

Solution:

  • Install a supported version (3.8–3.11)

13.4 Problem: GPU Drivers Not Detected

Fix:

  • Update NVIDIA drivers
  • Install correct CUDA version

14. Why You Should NOT Install Keras Separately Anymore

Before TensorFlow 2.x, developers used:

pip install keras

However, this is not recommended now because:

  • Separate Keras package may cause version conflicts
  • Some features are unsupported
  • TensorFlow now maintains the Keras API internally

Better approach:

from tensorflow import keras

This ensures stability and compatibility.

15. Building a Simple Deep Learning Model Using TensorFlow + Keras

Once TensorFlow is installed, you can train your first model.

Here’s an example for binary classification:

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([

layers.Dense(16, activation='relu'),
layers.Dense(8, activation='relu'),
layers.Dense(1, activation='sigmoid')
])

model.compile(

optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)

Train the model:

model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))

This demonstrates how easy it is to use Keras after installing TensorFlow.

16. How TensorFlow and Keras Work Together

Keras acts as the high-level interface, while TensorFlow handles the backend operations.

16.1 Keras Provides

  • Model building
  • Layers
  • Loss functions
  • Metrics
  • Callbacks

16.2 TensorFlow Provides

  • Computation engine
  • GPU acceleration
  • Optimization algorithms
  • Large-scale deployment tools

17. Advantages of Installing TensorFlow with Keras Included

✔ 1. Easy installation

Only one pip command.

✔ 2. Perfect compatibility

No conflicting versions.

✔ 3. Faster development

Intuitive Keras interface.

✔ 4. Access to advanced tools

TensorFlow Addons, TensorFlow Hub, etc.

✔ 5. Industry standard

Used in research labs and Fortune 500 companies.

18. Best Practices After Installation

18.1 Keep Virtual Environment Clean

Install only required packages.

18.2 Update TensorFlow Periodically

Use:

pip install --upgrade tensorflow

18.3 Test GPU Regularly (if using)

GPU compatibility can break after OS updates.

18.4 Try sample models from TF tutorials

Google provides thousands of official examples.

19. Frequently Asked Questions

Q1: Do I need to install Keras separately?

No. Keras is included with TensorFlow.

Q2: What is the correct import method?

Use:

from tensorflow import keras

Q3: Can TensorFlow run without a GPU?

Yes, CPU-only TensorFlow works perfectly.

Q4: Do I need Anaconda?

Optional, not required.

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