20.2. Building Neural Networks with Keras and TensorFlow: Installation and environment configuration
Developing neural networks with Keras and TensorFlow is a task that requires a solid understanding of both libraries, as well as the correct configuration of the development environment. In this chapter, we will cover the steps necessary to install and configure the environment to build neural networks using Keras and TensorFlow in Python.
Introduction to TensorFlow and Keras
TensorFlow is an open source library developed by the Google Brain Team for numerical computation using data flow graphs. It is widely used for machine learning and deep learning applications due to its flexibility and ability to scale. Keras, on the other hand, is a high-level API that runs on top of TensorFlow, making the process of developing deep learning models more accessible and faster.
Python Installation
Before installing TensorFlow and Keras, you need to have Python installed on your machine. It is recommended to use the latest version of Python 3, which can be downloaded from the official Python website or through distributions like Anaconda, which come with many useful packages pre-installed.
Creating a Virtual Environment
It is good practice to create a virtual environment for your machine learning project. This allows you to manage project dependencies in isolation without affecting other Python installations on your system. To create a virtual environment, you can use the venv module that is built into Python 3:
python3 -m venv environment_name
To activate the virtual environment, use the command:
source environment_name/bin/activate
On Windows, the command is a little different:
environment_name\Scripts\activate
TensorFlow Installation
With the virtual environment enabled, you can install TensorFlow using the pip package manager:
pip install tensorflow
This will install the latest version of TensorFlow, including all its dependencies. If you need a specific version, you can specify it after the package name:
pip install tensorflow==2.x.x
If you are working with NVIDIA GPUs and want to take advantage of parallel processing power, you must install the version of TensorFlow that supports GPU:
pip install tensorflow-gpu
To do this, you will also need to have the CUDA Toolkit and cuDNN installed and configured correctly on your machine.
Keras Installation
Since version 2.3.0, Keras has been integrated with TensorFlow as tensorflow.keras. Therefore, when you install TensorFlow, you will already have Keras available. However, to ensure you have the latest version of Keras, you can install or update Keras separately:
pip install --upgrade keras
Testing the Installation
To verify that TensorFlow and Keras are installed correctly, you can run the following Python script:
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(keras.__version__)
If everything is correct, the script will print the versions of TensorFlow and Keras installed on your machine.
Development Environment Configuration
With the environment configured, you can start developing your neural networks. For efficient development, it is recommended to use an IDE (Integrated Development Environment) such as PyCharm, Visual Studio Code or Jupyter Notebooks, which offer useful features such as auto-completion, debugging and version control.
Conclusion
Building neural networks with Keras and TensorFlow requires careful configuration of the development environment. By following the steps outlined in this chapter, you will have a robust, isolated environment to develop your deep learning models. Remember that practice makes perfect, so don't hesitate to experiment and iterate on your templates to achieve the best results.
With the environment properly configured and tested, you are ready to dive into the fascinating world of deep learning, using the powerful tools that TensorFlow and Keras have to offer.
