Multi-Layer Perceptron (MLP)

The Multi-Layer Perceptron (MLP) is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, one or more hidden layers, and an output layer. Except for the input nodes, each node is a neuron that uses a non-linear activation function.

MLPs are capable of modeling complex relationships between inputs and outputs, making them suitable for both classification and regression tasks. They are fully connected networks, meaning each neuron in one layer is connected to every neuron in the next layer.

Key characteristics of MLPs include:

• Fully connected layers
• Use of non-linear activation functions (like ReLU, Sigmoid, or Tanh)
• Trained using backpropagation and gradient descent

Structure of a Multi-Layer Perceptron (MLP)

Here’s a simple implementation of a Multi-Layer Perceptron (MLP) using TensorFlow/Keras:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create an MLP model
model = Sequential([
    Dense(64, activation='relu', input_shape=(100,)),
    Dense(32, activation='relu'),
    Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Model summary
model.summary()

This MLP model has:

• An input layer of 100 features
• Two hidden layers with 64 and 32 neurons using ReLU
• An output layer with 10 neurons for multi-class classification