Deep Belief Network

How Deep Belief Networks Work

Input Layer: Receives raw data (e.g., images, numerical features, or text) and feeds it to the first Restricted Boltzmann Machine (RBM).
First RBM (Input to Hidden Layer): - Learns to model the probability distribution of the input data. - Activates hidden nodes based on learned patterns in the visible layer.
Subsequent RBMs (Stacked Hidden Layers): - Each RBM receives input from the hidden layer of the previous RBM. - Progressively learns more abstract and hierarchical representations of the data.
Fine-Tuning: - After pretraining, the entire network is fine-tuned using supervised backpropagation to minimize the error between predicted and actual outputs.
Probabilistic Modeling: - Models the relationships between nodes probabilistically, capturing patterns and dependencies in the data.
Output Layer: - Produces predictions or classifications by mapping the final abstract representation to the output.
Applications: - Dimensionality reduction. - Feature extraction. - Image recognition. - Time-series forecasting. - Data reconstruction.

DBN Code Example

Here's how we can define a Deep Belief Network (DBN) architecture:


import tensorflow as tf
from tensorflow.keras import layers, models

# Define Restricted Boltzmann Machine (RBM) Layer
class RBMLayer(layers.Layer):
    def __init__(self, num_hidden_units, **kwargs):
        super(RBMLayer, self).__init__(**kwargs)
        self.num_hidden_units = num_hidden_units

    def build(self, input_shape):
        self.num_visible_units = input_shape[-1]
        self.W = self.add_weight(
            shape=(self.num_visible_units, self.num_hidden_units),
            initializer="glorot_uniform",
            trainable=True
        )
        self.h_bias = self.add_weight(
            shape=(self.num_hidden_units,),
            initializer="zeros",
            trainable=True
        )
        self.v_bias = self.add_weight(
            shape=(self.num_visible_units,),
            initializer="zeros",
            trainable=True
        )

    def call(self, inputs):
        # Forward pass: visible to hidden probabilities
        hidden_probabilities = tf.nn.sigmoid(tf.matmul(inputs, self.W) + self.h_bias)
        return hidden_probabilities

# Define Deep Belief Network (DBN) Model
def build_dbn():
    model = models.Sequential([
        layers.Input(shape=(784,)),  # Input Layer (Flattened image)
        RBMLayer(512),  # RBM Layer 1
        RBMLayer(256),  # RBM Layer 2
        RBMLayer(128),  # RBM Layer 3
        layers.Dense(10, activation="softmax")  # Output Layer
    ])
    return model

# Instantiate the DBN Model
dbn_model = build_dbn()

Programming Ocean Academy

Deep Belief Network AI Architecture

How Deep Belief Networks Work

DBN Code Example