Keras MLP Example Tutorial

In this tutorial, we will go through a simple example of building a Multi-Layer Perceptron (MLP) using Keras, a high-level neural networks API written in Python.

Overview

• What is Keras? Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
• What is MLP? MLP stands for Multi-Layer Perceptron, which is a class of feedforward artificial neural networks.

Setup

Before we start, make sure you have the following installed:

• Python
• TensorFlow
• Keras

You can install TensorFlow and Keras using pip:

pip install tensorflow
pip install keras

Step-by-Step Guide

1. Import Libraries

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

2. Create the dataset

# Generate some random data
x_train = np.random.random((1000, 20))
y_train = np.random.randint(2, size=(1000, 1))

# Normalize the data
x_train = x_train / 255.0

3. Build the model

model = Sequential()
model.add(Dense(64, activation='relu', input_dim=20))
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

4. Compile the model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

5. Train the model

model.fit(x_train, y_train, epochs=10, batch_size=32)

6. Evaluate the model

loss, accuracy = model.evaluate(x_train, y_train)
print(f"Loss: {loss}, Accuracy: {accuracy}")

7. Make predictions

predictions = model.predict(x_train)
print(predictions)

Further Reading

For more information on Keras and MLPs, you can check out the following resources:

• Keras Documentation
• Understanding MLPs

Conclusion

This tutorial provided a basic overview of building an MLP using Keras. You can now experiment with different architectures and datasets to improve your model's performance.

# Image for Keras
<center><img src="https://cloud-image.ullrai.com/q/Keras/" alt="Keras"/></center>