Keras - Installation

This chapter explains about how to install Keras on your machine. Before moving to installation, let us go through the basic requirements of Keras.
You must satisfy the following requirements −
Any kind of OS (Windows, Linux or Mac)
Python version 3.5 or higher.
Keras is python based neural network library so python must be installed on your machine. If python is properly installed on your machine, then open your terminal and type python, you could see the response similar as specified below,
Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18) 
[MSC v.1900 64 bit (AMD64)] on win32 
Type "help", "copyright", "credits" or "license" for more information. 
>>>

As of now the latest version is ‘3.7.2’. If Python is not installed, then visit the official python link - www.python.org and download the latest version based on your OS and install it immediately on your system.
Keras installation is quite easy. Follow below steps to properly install Keras on your system.
Virtualenv is used to manage Python packages for different projects. This will be helpful to avoid breaking the packages installed in the other environments. So, it is always recommended to use a virtual environment while developing Python applications.
Linux/Mac OS
Linux or mac OS users, go to your project root directory and type the below command to create virtual environment,
python3 -m venv kerasenv

After executing the above command, “kerasenv” directory is created with bin,lib and include folders in your installation location.
Windows
Windows user can use the below command,
py -m venv keras

This step will configure python and pip executables in your shell path.
Linux/Mac OS
Now we have created a virtual environment named “kerasvenv”. Move to the folder and type the below command,
$ cd kerasvenv kerasvenv $ source bin/activate

Windows
Windows users move inside the “kerasenv” folder and type the below command,
.\env\Scripts\activate

Keras depends on the following python libraries.
Numpy
Pandas
Scikit-learn
Matplotlib
Scipy
Seaborn
Hopefully, you have installed all the above libraries on your system. If these libraries are not installed, then use the below command to install one by one.
numpy
pip install numpy

you could see the following response,
Collecting numpy 
   Downloading 
https://files.pythonhosted.org/packages/cf/a4/d5387a74204542a60ad1baa84cd2d3353c330e59be8cf2d47c0b11d3cde8/ 
   numpy-3.1.1-cp36-cp36m-macosx_10_6_intel.
macosx_10_9_intel.macosx_10_9_x86_64. 
   macosx_10_10_intel.macosx_10_10_x86_64.whl (14.4MB) 
      |████████████████████████████████| 14.4MB 2.8MB/s

pandas
pip install pandas

We could see the following response,
Collecting pandas 
   Downloading 
https://files.pythonhosted.org/packages/cf/a4/d5387a74204542a60ad1baa84cd2d3353c330e59be8cf2d47c0b11d3cde8/ 
pandas-3.1.1-cp36-cp36m-macosx_10_6_intel.
macosx_10_9_intel.macosx_10_9_x86_64. 
   macosx_10_10_intel.macosx_10_10_x86_64.whl (14.4MB) 
      |████████████████████████████████| 14.4MB 2.8MB/s

matplotlib
pip install matplotlib

We could see the following response,
Collecting matplotlib 
   Downloading 
https://files.pythonhosted.org/packages/cf/a4/d5387a74204542a60ad1baa84cd2d3353c330e59be8cf2d47c0b11d3cde8/ 
matplotlib-3.1.1-cp36-cp36m-macosx_10_6_intel.
macosx_10_9_intel.macosx_10_9_x86_64. 
   macosx_10_10_intel.macosx_10_10_x86_64.whl (14.4MB) 
      |████████████████████████████████| 14.4MB 2.8MB/s

scipy
pip install scipy

We could see the following response,
Collecting scipy 
   Downloading 
https://files.pythonhosted.org/packages/cf/a4/d5387a74204542a60ad1baa84cd2d3353c330e59be8cf2d47c0b11d3cde8 
/scipy-3.1.1-cp36-cp36m-macosx_10_6_intel.
macosx_10_9_intel.macosx_10_9_x86_64. 
   macosx_10_10_intel.macosx_10_10_x86_64.whl (14.4MB) 
      |████████████████████████████████| 14.4MB 2.8MB/s

scikit-learn
It is an open source machine learning library. It is used for classification, regression and clustering algorithms. Before moving to the installation, it requires the following −
Python version 3.5 or higher
NumPy version 1.11.0 or higher
SciPy version 0.17.0 or higher
joblib 0.11 or higher.
Now, we install scikit-learn using the below command −
pip install -U scikit-learn

Seaborn
Seaborn is an amazing library that allows you to easily visualize your data. Use the below command to install −
pip install seaborn

You could see the message similar as specified below −
Collecting seaborn 
   Downloading 
https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc 
/seaborn-0.9.0-py3-none-any.whl (208kB) 100% 
   |████████████████████████████████| 215kB 4.0MB/s 
Requirement already satisfied: numpy> = 1.9.3 in 
./lib/python3.7/site-packages (from seaborn) (1.17.0) 
Collecting pandas> = 0.15.2 (from seaborn) 
   Downloading 
https://files.pythonhosted.org/packages/39/b7/441375a152f3f9929ff8bc2915218ff1a063a59d7137ae0546db616749f9/ 
pandas-0.25.0-cp37-cp37m-macosx_10_9_x86_64.
macosx_10_10_x86_64.whl (10.1MB) 100% 
   |████████████████████████████████| 10.1MB 1.8MB/s 
Requirement already satisfied: scipy>=0.14.0 in 
./lib/python3.7/site-packages (from seaborn) (1.3.0) 
Collecting matplotlib> = 1.4.3 (from seaborn) 
   Downloading 
https://files.pythonhosted.org/packages/c3/8b/af9e0984f
5c0df06d3fab0bf396eb09cbf05f8452de4e9502b182f59c33b/ 
matplotlib-3.1.1-cp37-cp37m-macosx_10_6_intel.
macosx_10_9_intel.macosx_10_9_x86_64 
.macosx_10_10_intel.macosx_10_10_x86_64.whl (14.4MB) 100% 
   |████████████████████████████████| 14.4MB 1.4MB/s 
...................................... 
...................................... 
Successfully installed cycler-0.10.0 kiwisolver-1.1.0 
matplotlib-3.1.1 pandas-0.25.0 pyparsing-2.4.2 
python-dateutil-2.8.0 pytz-2019.2 seaborn-0.9.0

As of now, we have completed basic requirements for the installtion of Kera. Now, install the Keras using same procedure as specified below −
pip install keras

After finishing all your changes in your project, then simply run the below command to quit the environment −
deactivate

We believe that you have installed anaconda cloud on your machine. If anaconda is not installed, then visit the official link, www.anaconda.com/distribution and choose download based on your OS.
Launch anaconda prompt, this will open base Anaconda environment. Let us create a new conda environment. This process is similar to virtualenv. Type the below command in your conda terminal −
conda create --name PythonCPU

If you want, you can create and install modules using GPU also. In this tutorial, we follow CPU instructions.
To activate the environment, use the below command −
activate PythonCPU

Spyder is an IDE for executing python applications. Let us install this IDE in our conda environment using the below command −
conda install spyder

We have already known the python libraries numpy, pandas, etc., needed for keras. You can install all the modules by using the below syntax −
Syntax
conda install -c anaconda <module-name>

For example, you want to install pandas −
conda install -c anaconda pandas

Like the same method, try it yourself to install the remaining modules.
Now, everything looks good so you can start keras installation using the below command −
conda install -c anaconda keras

Finally, launch spyder in your conda terminal using the below command −
spyder

To ensure everything was installed correctly, import all the modules, it will add everything and if anything went wrong, you will get module not found error message.