What is Keras

Keras is an open-source, high-level deep learning API written in Python. It is designed to simplify the process of building and training neural networks. Keras acts as a user-friendly interface for complex machine learning libraries like TensorFlow, Theano, or CNTK, though today it is fully integrated into TensorFlow 2.x.

Keras allows developers and researchers to build deep learning models with just a few lines of code. It supports common neural network layers (dense, convolutional, recurrent, etc.) and techniques like dropout, batch normalization, and activation functions. Its modular design makes it easy to build sequential models (a stack of layers) or more complex models using its Functional API.

Keras has Built-in tools for model training, evaluation, and prediction.

Example

Creates a simple feedforward neural network


from keras.models import Sequential
from keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(100,)),
    Dense(1, activation='sigmoid')
])

This example creates a simple feedforward neural network.

set_random_seed Function

keras.utils.set_random_seed(42) is used to make your machine learning experiment reproducible by setting a random seed.

🔍 What It Does: It sets the random seed for:

  • NumPy
  • Python’s built-in random module
  • TensorFlow

This ensures that all random operations (like weight initialization, data shuffling, dropout, etc.) behave the same way every time you run the code.

🔒 Why Use It? Machine learning involves randomness — for example:

  • Initializing model weights
  • Shuffling data during training
  • Splitting datasets randomly

Without a fixed seed, you’ll get slightly different results each time. Setting the seed makes results reproducible, which is critical for:

  • Debugging
  • Experiment comparison
  • Scientific reliability

🧠 What is 42? The number 42 is just a commonly used seed value (a nod to The Hitchhiker’s Guide to the Galaxy). You can use any integer.

âś… Summary: Ensures consistent behavior across runs by fixing the random seed used by Keras/TensorFlow and related libraries.