AI News, Visualizing neural networks in R –update
- On 30. september 2018
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Visualizing neural networks in R –update
In my last post I said I wasn’t going to write anymore about neural networks (i.e., multilayer feedforward perceptron, supervised ANN, etc.).
Additionally, I’ve added a new option for plotting a raw weight vector to allow use with neural networks created elsewhere.
The nnet function can take separate (or combined) x and y inputs as data frames or as a formula, the neuralnet function can only use a formula as input, and the mlp function can only take a data frame as combined or separate variables as input.
As far as I know, the neuralnet function is not capable of modelling multiple response variables, unless the response is a categorical variable that uses one node for each outcome.
The documentation about bias layers for this function is lacking, although I have noticed that the model object returned by mlp does include information about ‘unitBias’
I could not find any reference to the original variable names in the mlp object, so generic names returned by the function are used.
These include options to remove bias layers, remove variable labels, supply your own variable labels, and include the network architecture if using weights directly as input.
I thought the easiest way to use the plotting function with your own weights was to have the input weights as a numeric vector, including bias layers.
Note that wts.in is a numeric vector with length equal to the expected given the architecture (i.e., for 8 10 2 network, 100 connection weights plus 12 bias weights).
The weight vector shows the weights for each hidden node in sequence, starting with the bias input for each node, then the weights for each output node in sequence, starting with the bias input for each output node.
I’ll show the correct order of the weights using an example with plot.nn from the neuralnet package since the weights are included directly on the plot.
I’ve now modified the function to plot multiple hidden layers for networks created using the mlp function in the RSNNS package and neuralnet in the neuralnet package.
Update 3: The color vector argument (circle.col) for the nodes was changed to allow a separate color vector for the input layer.
- On 22. september 2020
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