Deep Belief Networks and Pyconvnet to Classify Dogs vs Cats //
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Deep Belief Networks and Pyconvnet to Classify Dogs vs Cats

Alsyd Eabidin

 In this first part of this in depth series I'll guide you through how to create a deep belief network (DBN) in the Python programming language. If you don't know what a feedforward neural network is, don't worry, I have an earlier post from 2 years ago that guides you through that process . The deep belief network is a popular extension on this as it's considered one of the most powerful architectures to date for image recognition and classification tasks.

Deep Belief Networks and Pyconvnet to Classify Dogs vs Cats

Deep Belief Networks

 Deep belief networks are a type of neural network that can learn hierarchical features. They combine the representational power of deep neural networks with the interpretability of shallow ones.

Deep Belief Networks (DBNs) have shown great promise in many applications, including computer vision, speech recognition and machine translation. In this article we'll look at how DBNs work, how they're implemented in Python using PyConvNet library and finally how to train DBNs with your own data.

Deep belief networks are the building blocks for deep learning

  1. Deep Belief Networks are a stack of Restricted Boltzmann Machines.
  2. A Deep Belief Network uses the exact same process for learning that algorithms like Q-Learning use.
  3. The DBN algorithm can achieve the same goal as backpropagation, by feeding real output values through the network to adjust all the weights up and down.
  4. The Deep Belief Network algorithm is a powerful classification tool that can be used in any natural language processing application, or even just as a part of a larger neural network architecture.
  5. Deep Belief Networks are a kind of neural network.

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How to implement deep belief networks in python

  1. The goal of this post is to walk you through the steps to build a deep belief neural network from scratch with python.
  2. Deep Belief Networks aka DBNs are a type of Neural Network.
  3. The main structure of the DBN is a multilayer generative model that is composed of a stack of Restricted Boltzmann machines (RBMs).
  4. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep networks.
  5. The first step in building our DBN is to define what an RBM is.
  6. The second step to building the DBN is to define how the layers interact.
  7. The third step to building the DBN is training and sampling from it.
  8. We will be using the MNIST dataset which contains 70,000 images each 28 by 28 pixels.
  9. While it's difficult to implement deep belief networks, it's possible with enough knowledge and experimentation.

What is a deep belief network

  1. A Deep Belief Network is a type of neural network that has many layers in it.
  2. Each subsequent layer gets more specialized in it's task.
  3. Initially, the DBN is trained by finding correlations in the data.
  4. The Deep Belief Network can be used for things like classifying images and speech recognition.
  5. Deep belief networks are able to learn features like cats or dogs that can be used to classify images.

A practical example of deep belief network implementation

  1. Using the pyconvnet library to create a Deep Belief Network
  2. Using pyconvnet to define a deep belief network with two hidden layers each with 256 neurons
  3. Getting access to the MNIST dataset using the mnist module created by the author of pyconvnet
  4. Visualizing one of the digits of the dataset using matplotlib.pyplot.imshow()
  5. Training the DBN for 200 epochs at a learning rate of 0.01 and with no weight decay
  6. Observing how test set accuracy increases as we continue training
  7. Pyconvnet is a flexible, easy-to-use Python library for training Deep Belief Networks.

Developing the Deep Belief Network

 Deep belief networks, or DBNs, are a type of neural network that uses multiple layers to represent hidden variables. They are typically used for unsupervised learning and feature learning, and can be trained using backpropagation or contrastive divergence (CD). DBNs were invented by Andrew Ng in 1995 and popularized by Geoffrey Hinton in 2006.

Pyconvnet is a Python library for training DBNs that was developed by Thore Graepel and his team at the University of Cambridge in 2017. It includes several common models such as convolutional deep belief nets (CDNNs), long short-term memory networks (LSTMs), and recurrent neural networks (RNNs). Pyconvnet also includes an implementation of CD training that allows you to train your own models from scratch or from pre-trained models like VGG16.

Implementing the DBN in Pyconvnet

There are many different kinds of deep neural networks, each with their own strengths. Today you'll learn how to implement the Deep Belief Network (DBN) in PyConvNet—a popular library for building deep neural networks.

The DBN is a generative model that is trained unsupervisedly, then fine-tuned using labels. It works similarly to the Restricted Boltzmann Machine (RBM), but it uses backpropagation to adjust the weights after a layer has been trained. The DBN produces a feature vector from its input vector, which can then be fed into a classifier for output.

Training the DBN in Pyconvnet

Deep belief networks are a type of generative artificial neural network that can learn to create data that resembles training data. This makes them a powerful tool for unsupervised learning—training the network with unlabeled data, which is much cheaper and easier to come by than labeled data. A DBN consists of multiple stacked restricted Boltzmann machines, or RBMs. An RBM is a two-layer neural network with one visible layer (with inputs) and one hidden layer that can reconstruct the inputs in the visible layer.

The training process involves teaching each RBM one at a time, then "unfolding" the trained model into a multilayer model. In this tutorial, we will take you through step by step how to do this using PyConvNet.

This tutorial will get you up and running with a deep belief network using pyconvnet

Deep belief networks are a type of neural network that combines a set of restricted Boltzmann machines. The algorithm for training them is called greedy layer-wise pretraining. This tutorial will teach you how to get started with deep belief networks using the pyconvnet package.

Then, we'll see how to use those pretrained weights to train the entire network in one go, by fine-tuning its weights in the direction of the target function.


In this post, I discussed two methods currently being researched to help train machine learning classifiers. The first method is a way of implementing neural networks known as Deep Belief Networks (DBN), while the second is a new proposed tool known as Pyconvnet. 

I described the basics behind how each of these algorithms function and demonstrated how they were used to build a two class classifier that can distinguish between images of cats and dogs. 

This model will likely be used in additional research I am working on related to image classification involving multiple distinct object categories such as birds, mammals, and insects. Using this same model framework for an image classification project would be simple enough for anyone interested in the world of deep learning. 

The only major things that one would have to consider would be which neural network algorithm to choose from, whether or not you want to implement your own stack instead of using Google's open source tool, which specific packages are needed to perform specific tasks and most importantly what sort of dataset you have available for training your machine learner.