RBM | Restricted Boltzmann Machine in R | Machine Learning library

by TimoMatzen R Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | RBM Summary

RBM is a R library typically used in Artificial Intelligence, Machine Learning applications. RBM has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Restricted Boltzmann Machine in R

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Quality

Security

License

Reuse

Support

RBM has a low active ecosystem.

It has 7 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 166 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of RBM is current.

Quality

RBM has 0 bugs and 0 code smells.

Security

RBM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

RBM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RBM does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

RBM releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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RBM Key Features

No Key Features are available at this moment for RBM.

RBM Examples and Code Snippets

Train the RBM .

python

Lines of Code : 18

License : No License

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def fit(self, X, epochs=1, batch_sz=100, show_fig=False):
        N, D = X.shape
        n_batches = N // batch_sz

        costs = []
        print("training rbm: %s" % self.id)
        for i in range(epochs):
            print("epoch:", i)

Run RBM .

python

Lines of Code : 9

License : No License

Copy

def main():
    A = load_npz("Atrain.npz")
    A_test = load_npz("Atest.npz")
    mask = (A > 0) * 1.0
    mask_test = (A_test > 0) * 1.0

    N, M = A.shape
    rbm = RBM(M, 50, 10)
    rbm.fit(A, mask, A_test, mask_test)

Run RBM .

python

Lines of Code : 7

License : No License

Copy

def main():
    A = load_npz("Atrain.npz")
    A_test = load_npz("Atest.npz")

    N, M = A.shape
    rbm = RBM(M, 50, 10)
    rbm.fit(A, A_test)

Community Discussions

Trending Discussions on RBM

Generating data from restricted Boltzmann machine

IndexError: The shape of the mask [1, 1682] at index 0 does not match the shape of the indexed tensor [100, 1682] at index 0

QUESTION

Generating data from restricted Boltzmann machine

Asked 2021-Mar-30 at 19:36

My understanding is that to generate new data in RBM I would need to pass in real data. Is there a way to get generated data without real data? Like how VAE and GAN samples latent variable from prior distribution to generate data.

If so, in the case of labeled dataset like MNIST, how can I generate data from a specific class? Do I need to train 10 different RBM models for each digit?

...

ANSWER

Answered 2021-Mar-30 at 19:36

My understanding is that to generate new data in RBM I would need to pass in real data. Is there a way to get generated data without real data? Like how VAE and GAN samples latent variable from prior distribution to generate data.

Yes, of course. This is actually the process that is happening in the negative phase of the training. You're sampling from a joint distribution, therefore letting the network "dream" of what it has been trained for. I guess this depends on your implementation, but I've been able to do that by initializing inputs as zeros and running Gibbs sampling for a few iterations. The result, as I interpret it, is that I should see "number-looking things" in the visible nodes, not necessarily numbers from your dataset.

This is an example I like, trained on MNIST, and sampled without any nodes clamped:

To your second question:

If so, in the case of labeled dataset like MNIST, how can I generate data from a specific class? Do I need to train 10 different RBM models for each digit?

What you can do when using labeled data is to use your labels as additional visible nodes. Check "Training Restricted Boltzmann Machines: An Introduction" Figure 2.

Also, for both these cases I'm thinking that using other sampling techniques that gradually lower the sampling temperature (e.g. Simulated Annealing) , will give you better results.

Source https://stackoverflow.com/questions/66450661

QUESTION

IndexError: The shape of the mask [1, 1682] at index 0 does not match the shape of the indexed tensor [100, 1682] at index 0

Asked 2020-May-28 at 17:25

I'm currently doing an DeepLearning course in Udemy. I am currently designing an Restricted Boltzmann machine in which my training runs perfectly, but I ended up with this error while testing

IndexError: The shape of the mask [1, 1682] at index 0 does not match the shape of the indexed tensor [100, 1682] at index 0

...

ANSWER

Answered 2020-May-28 at 17:25

In the test loop, you're re-using a variable of the training phase, which I think (you didn't provide the full stacktrace) is just a typo:

Source https://stackoverflow.com/questions/62069963

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install RBM

Lets start by installing my package from GitHub, this can be done with the following code snippet (installing the package can take a couple of minutes):.

Support

If you read this entire document and feel that you are still interested in RBMs and DBNs, here are some links that I found very helpful :). A good blog on RBMs by Edward Chen: RBM Chen. A helpful video on RBMs and DBNs by Geoffrey Hinton: RBM Hinton; DBN Hinton. Or the video by Hugo Larochelle on the RBM and contrastive divergence: RBM and CD.

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