AI News, Visualizing neural networks in R –update

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 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.

Neural Network Demo Animation

I created a demo in which you may see a multi-layer perceptron with dropout train on a dataset I created of hand drawn squares, circles, and triangles. This was ...

Neural Networks - Getting your matrix dimensions right

Deep Neural Networks Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer ...

Neural Network Calculation (Part 1): Feedforward Structure

From In this series we will see how a neural network actually calculates its values. This first video takes a look at the structure of ..

Backpropagation in 5 Minutes (tutorial)

Let's discuss the math behind back-propagation. We'll go over the 3 terms from Calculus you need to understand it (derivatives, partial derivatives, and the chain ...

3. Learning the Weights of a Logistic Output Neuron

Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:

Getting Started with Neural Network Toolbox

Use graphical tools to apply neural networks to data fitting, pattern recognition, clustering, and time series problems. Top 7 Ways to Get Started with Deep ...

Lecture 6 | Training Neural Networks I

In Lecture 6 we discuss many practical issues for training modern neural networks. We discuss different activation functions, the importance of data ...

Neural Networks 6: solving XOR with a hidden layer

Neural Networks Demystified [Part 4: Backpropagation]

Backpropagation as simple as possible, but no simpler. Perhaps the most misunderstood part of neural networks, Backpropagation of errors is the key step that ...

Convolutional Networks

This video is part of the Udacity course "Deep Learning". Watch the full course at