README.md

Deep-Belief-Networks-in-PyTorch

The aim of this repository is to create RBMs, EBMs and DBNs in generalized manner, so as to allow modification and variation in model types.

This repository has been executed and verified on 22nd September 2024 details below!

RBM:

Energy-Based Models are a set of deep learning models which utilize physics concept of energy. They determine dependencies between variables by associating a scalar value, which represents the energy to the complete system.

• It is a probabilistic, unsupervised, generative deep machine learning algorithm.
• It belongs to the energy-based model
• RBM is undirected and has only two layers, Input layer, and hidden layer
• No intralayer connection exists between the visible nodes.
• All visible nodes are connected to all the hidden nodes

In an RBM, we have a symmetric bipartite graph where no two units within the same group are connected. Multiple RBMs can also be stacked and can be fine-tuned through the process of gradient descent and back-propagation. Such a network is called a Deep Belief Network.

The above project allows one to train an RBM and a DBN in PyTorch on both CPU and GPU. Finally let us take a look at some of the reconstructed images.

Results - Restricted Boltzmann Machine:

Without Pre-Training:

epochs	test loss	train loss	test acc	train acc
1.0	1.793358325958252	1.7901512384414673	0.6681372549019607	0.672005772005772
2.0	1.703145980834961	1.6950132846832275	0.7593837535014005	0.7689033189033189
3.0	1.614591121673584	1.60787832736969	0.8499299719887955	0.8563492063492063
4.0	1.548156976699829	1.539191484451294	0.9173669467787114	0.9269119769119769
5.0	1.5276743173599243	1.5182831287384033	0.9369047619047619	0.9461760461760462

With Pre-Training:

epochs	test loss	train loss	test acc	train acc
1.0	1.5359452962875366	1.5310659408569336	0.9349439775910364	0.9391053391053391
2.0	1.514952540397644	1.5070991516113281	0.9525210084033613	0.9602813852813853
3.0	1.5090779066085815	1.4990419149398804	0.9563025210084034	0.9665584415584415
4.0	1.5039907693862915	1.4926044940948486	0.9602941176470589	0.9722943722943723
5.0	1.4975998401641846	1.4844372272491455	0.9669467787114846	0.9796536796536797

DBN - Deep Belief Networks:

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.

When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification.

DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a "visible" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer is a training set).

The observation that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms.Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, drug discovery).

Results - Deep Belief Networks:

Without Pre-Training:

epochs	test loss	train loss	test acc	train acc
1.0	1.7656803131103516	1.7620763778686523	0.7411764705882353	0.745021645021645
2.0	1.647426724433899	1.6424834728240967	0.8315826330532213	0.837049062049062
3.0	1.6079597473144531	1.6017967462539673	0.8528011204481792	0.8601731601731601
4.0	1.541589617729187	1.533886194229126	0.9266806722689076	0.9341269841269841
5.0	1.52533757686615	1.5153533220291138	0.9397759103641457	0.9494949494949495

With Pre-Training:

epochs	test loss	train loss	test acc	train acc
1.0	1.5659916400909424	1.563258409500122	0.9289915966386555	0.9310966810966811
2.0	1.5216224193572998	1.5138438940048218	0.9504901960784313	0.9579004329004329
3.0	1.5103492736816406	1.4991555213928223	0.9566526610644258	0.9685425685425686
4.0	1.5037704706192017	1.489931583404541	0.9618347338935574	0.9756132756132756
5.0	1.4998645782470703	1.4846107959747314	0.9647759103641457	0.9790764790764791

With Pre-Training and Input Binarization:

epochs	test loss	train loss	test acc	train acc
1.0	1.5000890493392944	1.4835282564163208	0.9633053221288516	0.9796176046176046
2.0	1.4980448484420776	1.4808478355407715	0.9640056022408964	0.9820707070707071
3.0	1.4971030950546265	1.4782592058181763	0.965546218487395	0.9841269841269841
4.0	1.4955949783325195	1.4761505126953125	0.9670868347338936	0.986002886002886
5.0	1.4929810762405396	1.474048376083374	0.9694677871148459	0.9876623376623377

Images - Restricted Boltzmann Machine:

Images - Deep Belief Networks:

Execution (Latest: 22nd September 2024)

The code is tested with the version of torch: v1.11.0

RBM:

With respect to RBM.py, load demo dataset through dataset = trial_dataset().

rbm = RBM(input_dim, hidden_dim, epochs=50, mode='bernoulli', lr=0.001, optimizer='adam', gpu=True, savefile='save_example.pt', early_stopping_patience=50)
rbm.train(dataset)

The above code saves the trained model to: save_example.pt. To load the dataset use the following code:

rbm.load_rbm('save_example.pt')
print("After Loading:", rbm)

DBN:

With respect to DBN.py, load demo dataset through dataset = trial_dataset().

layers = [7, 5, 2]
dbn = DBN(10, layers)
dbn.train_DBN(dataset)

The above code saves the trained model through the savefile argument.