Deep Learning using Deep Belief Network Part-1

Summary

This tutorial provides an in-depth explanation of constructing a Deep Belief Network (DBN) using Restricted Boltzmann Machines (RBMs). It covers the basics of DBNs, how they are built by stacking multiple RBMs, and the greedy training approach used to train each layer. The video delves into the contrastive divergence method, explaining both the positive and negative phases of weight updates. A simple example with a dataset of a person's game preferences is used to demonstrate the training process. The tutorial aims to offer a clear understanding of DBN architecture and its training mechanisms.

Takeaways

• 😀 Deep Belief Networks (DBN) are generative graphical models designed to solve the vanishing gradient problem in deep learning.
• 😀 DBNs are constructed by stacking multiple Restricted Boltzmann Machines (RBMs), and the number of stacks depends on the specific requirements of the task.
• 😀 The training of a DBN involves a greedy approach where each RBM is trained individually using contrastive divergence.
• 😀 Contrastive divergence is a method that uses Gibbs sampling to update the weights of the network, iterating between positive and negative phases.
• 😀 The first stack of the DBN uses the input data as visible units and the target classes as hidden units for training the RBM.
• 😀 After training the first RBM, the weights are frozen, and a new hidden layer is added to the model to form the next stack of RBM.
• 😀 The training of each subsequent RBM involves using the output of the previous RBM’s hidden layers as input to the next RBM.
• 😀 Fine-tuning is applied after stacking all RBMs to adjust the weights of the entire network and optimize performance.
• 😀 A simple example dataset was used to illustrate the DBN construction, focusing on a person’s interest in three different games (football, hockey, chess) and classifying them as indoor or outdoor games.
• 😀 The contrastive divergence method calculates the conditional probabilities for updating the weights of hidden and visible units using a sigmoid function.
• 😀 The process of updating the weights is iterative, with the weights being adjusted until the model reaches an acceptable level of accuracy.

Q & A

What is a Deep Belief Network (DBN)?

A Deep Belief Network (DBN) is a generative graphical model constructed by stacking multiple Restricted Boltzmann Machines (RBMs). It is used to address the vanishing gradient problem, which is common in training deep neural networks, by utilizing a greedy layer-wise training approach.

How is a Deep Belief Network (DBN) constructed?

A DBN is constructed by stacking multiple RBMs. The number of layers stacked depends on the requirements of the model. Each RBM is trained using a greedy approach, where the output of one RBM becomes the input for the next layer, and each layer is trained using contrastive divergence.

What is the role of Restricted Boltzmann Machines (RBMs) in a DBN?

RBMs serve as the building blocks in a DBN. Each RBM extracts features from the input data and reconstructs it. These extracted features are then used in subsequent layers of the DBN to further refine the model's learning.

What is the contrastive divergence method in the training of RBMs?

The contrastive divergence method is used to train RBMs. It involves two phases: the positive phase, where the weights are updated based on visible data and hidden unit activation, and the negative phase, where the weights are updated based on reconstructed data. This method helps in adjusting the weights to minimize the difference between the data distribution and the model’s learned distribution.

What are the steps in training a DBN using the greedy layer-wise approach?

The steps in the greedy training approach include: 1) Train the first RBM using the training data, 2) Freeze the learned weights of the first RBM, 3) Use the activations of the hidden layer from the first RBM as input to train the next RBM, 4) Repeat the process for additional layers, 5) Fine-tune the entire network once all layers are trained.

How does the DBN handle the stacking of RBMs?

The stacking of RBMs is done by adding new hidden layers to the model. Each new RBM takes the output of the previous RBM's hidden layer as its input. The training process is repeated for each layer, and once the network is fully stacked, fine-tuning is applied to adjust all weights for optimal performance.

What is the role of Gibbs sampling in contrastive divergence?

Gibbs sampling is used to update the states of visible and hidden units during the training process. It involves running two phases: the positive phase (updating hidden units based on visible units) and the negative phase (updating visible units based on hidden units). This iterative process helps optimize the weights in the RBM.

What is the significance of the sigmoid function in the contrastive divergence algorithm?

The sigmoid function is used to calculate the probability of the activation of hidden or visible units. In the contrastive divergence method, it is applied to compute the conditional probabilities of hidden or visible units being activated given the state of the other units, which helps in updating the weights.

What is the final step after training each layer of an RBM in a DBN?

After training each RBM layer in a DBN, the weights are frozen, and the network proceeds to the next layer. Once all layers are trained, fine-tuning is performed, where the entire network's weights are optimized using the backpropagation technique to refine the model's predictions.

Can you explain the process of weight updates in the training of an RBM?

In the training of an RBM, weight updates are made based on the difference between the positive and negative phases. During the positive phase, the hidden units' activations are updated, while in the negative phase, the visible units' activations are updated. The weights are adjusted using the formula that incorporates the learning rate, the difference between positive and negative statistics, and the current state of the units.