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Keras for Machine Learning Engineers With 10 Simple Steps

TensorFlow, Numpy, Keras, Machine Learning for Engineers - for ML and Data Science Engineers, Scientists & Engineers - Owned & Managed by Factober

Written by bitcolon

Editor & Administrator (Part of Factober Blogging Network)

September 6, 2020

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1. Setup Development Environment (System Requirements)

The tutorial is performed on:

  • Ubuntu 20.04
  • Python 3

Install Tensorflow [We will import Keras from Tensorflow]

# Requires the latest pip
pip install –upgrade pip

# Current stable release for CPU and GPU
pip install tensorflow

# Or try the preview build (unstable)
pip install tf-nightly


    2. Vectorized and standardized data

    Neural Networks don’t process raw data. So, well prepared and organized data should be supplied to them in vectors and standardized representations.


    1. Read the text files into string tensors.
    2. Split those strings into words.
    3. Index the words.
    4. Turn the indexed words into integer tensors.


    1. Read the image files.
    2. Then, decoded them into integer tensors.
    3. Then, convert them into floating-points.
    4. Then, Normalize them into small values (usually between 0 and 1).

    CSV (Comma Separated Values)

    1. Parse the CSV data
    2. Convert the numerical features to floating point tensors.
    3. Index the categorical features.
    4. Then conver them to integer tensors.
    5. Then normalize each feature to zero-mean and unit-variance.

    3. Loading the Data

    Keras models accept data in the following formats:

    NumPy Array

    1. Read the text files into string tensors.
    2. Split those strings into words.
    3. Index the words.
    4. Turn the indexed words into integer tensors. is is a tensorflow API that allows to build complex input pipelines from simple, reusable pieces. Read more about API here.

    Python Generators

    Python generators are the functions that return an object (iterator) which we can iterate over (one value at a time).

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