is there any way I can display an image or diagram of my neural net using h20 in R. Also, I went through the h20 documentation but couldn't figure out to extract weights from the neural net object.
In h2o.deeplearning() set export_weights_and_biases=T and then once your model has finished building you can extract the weights with h2o.weights(). H2O doesn't provide methods to display a diagram for your neural net.
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I'm trying to build a graph neural network to solve a tabular binary classification problem ( same as heart desease prediction ) but unfortunately I couldn't find any source or explanation about it all what I could find was super advance that I couldn't understand anything from it.
Hello stackoverflow community,
Im working on a uni-project in which we try to create Bayesian Network Classifier from data in R.
Ideally the classifier should be based on a General Bayesian Network (GNB) or a BN Augmented Naive Bayes(BAN).
Unfortunately Im yet to find a suitabel package to create either of those nets in R.
My research led me to the following two packages:
bnclassify, the most prominent package for BN classification, doesnt include GNBs or BANs at all.
bnlearn offers the possibility to learn GNBs but according to the creator the learning is focused on returning the correct dependence structure rather than maximizing the predictive accuracy for classification. I've tried to use them for my classification problem nonetheless but the result was underwhelming.
So my question is if anyone knows a R package to classify with GNBs or BANs
OR how to work with the GNBs fron bnlearn to improve their predictive accuracy for classification problems.
Thanks you for your help in advance.
Best Regards
The documentation for Keras in R has shown multiple methods to initialise weights of a neural network. However, the method of customising the initialisation is not clear.
For example, I have a 3*3 neural network (or LSTM), I want to initialise weights by using my own 3*3 matrix. How can I do that in R?
I would like to train a neural network in R and use it in Excel. Would it be possible to export a neural network to Excel?
Currently I have made a lineair regression model in R and I can use the coefficients in Excel formulas. I am looking for a similar method with a neural network.
Thanks.
I want to build an autoencoder model with the Caret package with the following features:
1) Build an unsupervised neural network model using deep learning autoencoders
2) Using the autoencoder model in (1) as a pre-training input for a supervised model.
Online examples on using autoencoder in caret are quite few and far in between, offering no real insight into practical use cases.
I'm under data privacy and resource constraints so I'm unable to use H2o or Keras for neural networks.
Sample data for the model can be found at:
https://www.kaggle.com/nodarokroshiashvili/credit-card-fraud/data
An example of this in H2o is at this link:
https://shiring.github.io/machine_learning/2017/05/01/fraud
Any help or pointers in the right direction in this regard will be appreciated.
EDIT:
Thanks to Lauren and Erin, staff at H20 commenting that data privacy should not be a concern because H20 creates a cluster which is located on premise and not in an 'H20.cloud'