Deep Belief Network — Explanation, Application & How To Get Started In TensorFlow

by | Feb 10, 2023 | Machine Learning, Natural Language Processing

How does the Deep Belief Network algorithm work? Common applications. Is it a supervised or unsupervised learning method? And how do they compare to CNNs? And how to create an implementation in Python using TensorFlow.

What is a Deep Belief Network?

Deep Belief Networks (DBNs) are a type of artificial neural network that is used for unsupervised and supervised learning tasks. They are composed of several layers of Restricted Boltzmann Machines (RBMs), which are shallow neural networks that can be trained using unsupervised learning. The output of the RBMs is then used as input to the next layer of the network, until the final layer is reached. The final layer of the DBN is typically a classifier that is trained using supervised learning.

DBNs are effective in several applications, such as image recognition, speech recognition, and natural language processing. They are also known for their ability to learn hierarchical representations of the data, which is useful in solving complex problems in artificial intelligence and machine learning.

In recent years, DBNs have been widely adopted in the deep learning community due to their ability to handle high-dimensional data, good scalability, and their ability to model complex, non-linear relationships in the data.

Deep Belief Network can handle high-dimensional complex data

DBNs can handle high-dimensional complex data.

How does the algorithm work?

The Deep Belief Network (DBN) algorithm consists of two main steps:

  1. Training: In this step, the DBN is trained using unsupervised learning, layer by layer. A Restricted Boltzmann Machine (RBM), an energy-based model that can be used for dimensionality reduction and feature learning, is used to train each layer. The RBMs’ weights are studied during the training phase and then used to produce representations of the input data. The DBN can learn more complex data features because each RBM’s outputs are used as inputs in the following layer.
  2. Fine-tuning: The topmost layer of the DBN is subjected to supervised learning following the training stage. Using backpropagation and gradient descent, the weights of the entire network are updated during this fine-tuning stage. The accuracy of the network is supposed to be improved even more by fine-tuning the weights based on the labelled training data.

The algorithm for training a DBN can be summarized as follows:

  1. Initialize the network with random weights.
  2. Start with the first layer and work your way to the last layer as you use unsupervised learning to train each layer of the network.
  3. Fine-tune the entire network using supervised learning and backpropagation.
  4. Repeat steps 2 and 3 until the network has converged.

In contrast to the fine-tuning step, repeated numerous times until the network converges, the training step is done once. The training step of the DBN algorithm is crucial because it enables the network to recognize essential data characteristics without interference from the labelled training data.

Is a Deep Belief Network supervised or unsupervised?

A Deep Belief Network (DBN) is mainly an unsupervised learning model but can also do supervised learning.

During the training phase of a DBN, Restricted Boltzmann Machines are used to train each layer of the network using unsupervised learning. The network learns how to create representations of the input data during this step without using labelled data.

Following the training phase, the topmost layer is subjected to a fine-tuning degree using supervised learning. Backpropagation and labelled data are used in this step to update the weights across the entire network. The network can get even more accurate during the fine-tuning stage by adding the labelled data.

To summarise, a DBN combines supervised and unsupervised learning to make a deep learning model that can handle complex data and make accurate predictions.

Applications of Deep Belief Networks

Deep Belief Networks (DBNs) have been applied in a variety of fields, including:

  1. Computer vision: DBNs have been used for tasks like recognizing objects in pictures or putting pictures into different groups.
  2. Speech recognition: DBNs have been used for speech recognition tasks, such as transcribing speech into text.
  3. Natural language processing: DBNs have been used for natural languages processing tasks, such as sentiment analysis and text classification.
  4. Recommender systems: DBNs have been used in recommender systems, which give users suggestions based on what they like and how they act.
  5. In bioinformatics, DBNs have been used to predict how proteins will interact with each other, look at how genes are expressed, and find new drugs.
  6. Financial analysis: DBNs have been used to predict the stock market and figure out how risky something is.

These are just a few of the numerous uses for DBNs. In addition, DBNs are advantageous for various tasks involving learning from large and complex data sets because of their adaptability.

It’s important to note, though, that in many of these applications, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have primarily taken the place of DBNs.

Deep Belief Network’s advantages

There are several advantages to using Deep Belief Networks (DBNs) for machine learning:

  1. Unsupervised training: DBNs learn representations of the input data through unsupervised training, which can increase the model’s accuracy. This is especially helpful when working with large and complicated data sets, where it can take effort to get enough labelled data to train the model using supervised learning alone.
  2. Deep architecture: Because of their deep architecture, DBNs can learn different levels of abstraction from the data, increasing the model’s precision. Because of their deep architecture, DBNs are suitable for tasks like image classification that need to learn from data with multiple levels of hierarchy.
  3. Non-linear transformations: DBNs can capture complex relationships in the data by using non-linear transformations in their hidden layers. DBNs are, therefore, advantageous for tasks where the input-output relationship is non-linear.
  4. Scalability: DBNs are scalable, which enables them to manage sizeable and intricate data sets. DBNs can be trained on large data sets quickly thanks to the parallelizability of the training stage.
  5. Handle missing data: Input missing data, a frequent issue in real-world data sets, can be handled by DBNs. This is because DBNs use probabilistic models, which can figure out the missing data from the already-seen data.

It’s important to note, though, that in many applications, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have primarily taken the place of DBNs.

Deep Belief Network vs CNN

Convolutional neural networks (CNNs) and deep belief networks (DBNs) are two examples of deep learning models applied to various machine learning tasks. But there are some significant variations between the two:

  1. Architecture: CNNs have a convolutional architecture, whereas DBNs have a deep feedforward architecture. CNNs are well suited for image classification tasks because of their convolutional architecture, which enables them to learn the spatial relationships between pixels. On the other hand, DBNs are more versatile and can be applied to a variety of tasks, including the classification of images.
  2. Pre-training: DBNs learn representations of the input data using unsupervised pre-training, whereas CNNs are typically trained using only supervised learning. When working with large and complex data sets, the pre-training step in DBNs can increase the model’s accuracy.
  3. Layer type: CNNs use convolutional and pooling layers, whereas DBNs use restricted Boltzmann machines (RBMs) in their hidden layers. CNNs can learn the spatial relationships between pixels thanks to the convolutional and pooling layers. On the other hand, RBMs in DBNs are probabilistic models that can estimate missing data based on the data they have seen.
  4. Input type: CNNs are usually only used for image data, but DBNs can be fed both continuous and discrete data.

DBNs are more versatile and can be used for various tasks, whereas CNNs are generally better suited for image classification tasks. But it’s important to note that convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have mostly replaced DBNs in many applications.

How to Implement a Deep Belief Network

Here’s an example of how a Deep Belief Network (DBN) could be used for a simple image classification task:

  1. Pre-processing: Pre-processing the input images is the first step. This could entail grayscale conversion, pixel value normalization, and resizing the images to a standard size.
  2. Training: The DBN is then trained using unsupervised learning, layer by layer. A Restricted Boltzmann Machine (RBM), which uses the pre-processed images as input and learns to produce representations of the images, is used to train the first layer. Once all the layers have been pre-trained, the outputs of the previous layer are then used as inputs for the subsequent layer.
  3. Fine-tuning: Using supervised learning, the topmost layer of the DBN is adjusted following pre-training. This entails updating the weights of the entire network based on the labelled training data using backpropagation and gradient descent.
  4. Testing: The new images are classified using the trained DBN. New images are pre-processed before being sent layer by layer through the network. A prediction about the image’s class is made using the output of the final layer.

This is a straightforward illustration of how a DBN might be applied to image classification. However, in practice, the network architecture and pre-processing steps may be more complicated depending on the task’s details.

Implementation in Python using TensorFlow

Here’s an example of how you could use TensorFlow and Python to build a Deep Belief Network (DBN):

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# Load the dataset 
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() 

# Normalize the data
x_train = x_train / 255.0
x_test = x_test / 255.0

# Flatten the data
x_train = x_train.reshape(-1, 784)
x_test = x_test.reshape(-1, 784)

# Build the DBN
input_layer = Input(shape=(784,))
hidden_layer = Dense(512, activation='relu')(input_layer)
output_layer = Dense(10, activation='softmax')(hidden_layer)

model = Model(input_layer, output_layer)

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

# Train the model
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# Evaluate the model
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)

In this example, we use the MNIST dataset, a well-known dataset for image classification. The data is first pre-processed and normalized, then the DBN is built using Keras. The DBN has two dense layers, one with 512 nodes and the other with 10 nodes. The model is then compiled, trained, and evaluated.

Conclusion

A type of deep learning architecture called Deep Belief Networks (DBNs) can be applied to various tasks, including speech and image recognition and natural language processing.

They are a particular class of generative models, and they are tuned using supervised learning after receiving unsupervised pre-training. As a result, DBNs can easily handle high-dimensional data, are scalable, and can learn hierarchical representations of the data, among other benefits.

Furthermore, programming languages like Python can be used to implement them using a variety of deep learning libraries, including TensorFlow, PyTorch, and Theano.

As a result, DBNs are an excellent way to solve complicated problems in artificial intelligence and machine learning.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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