Neural network builder interface (C Deeply)

Fast (1-second) neural network generator with architecture search.

cdeeply-m-file

MATLAB / Octave interface to C Deeply's neural network generators.

Put cdeeply_neural_network.m into a reachable directory, then:

1. Define a class instance, e.g. myNN = cdeeply_neural_network.
2. Call myNN.tabular_regressor(...) or myNN.tabular_encoder(...) to train a neural network in supervised or unsupervised mode. This step requires an internet connection! as the training is done server-side.
3. Call myNN.runSample(...) as many times as you want to process new data samples -- one sample per function call.

Function definitions:

sampleOutputs = tabular_regressor(trainingSamples, sampleTableTranspose, outputRowOrColumnList, importances,
maxWeights, maxHiddenNeurons, maxLayers, maxLayerSkips,
ifNNhasBias, ifAllowingInputOutputConnections)

Generates a x->y prediction network using supervised training on trainingSamples.

• trainingSamples is a matrix having dimensions numFeatures and numSamples, containing both inputs and target outputs.
  • Set sampleTableTranspose to "FEATURE_SAMPLE_ARRAY" for trainingSamples(feature, sample) array ordering, or "SAMPLE_FEATURE_ARRAY" for trainingSamples(sample, feature) array ordering.
  • The rows/columns in trainingSamples corresponding to the target outputs are specified by outputRowOrColumnList.
• The optional importances argument weights the cost function of the target outputs. Pass as a matrix having dimensions numTargetOutputs and numSamples (ordered according to sampleTableTranspose), or [] if this parameter isn't being used.
• Optional parameters maxWeights, maxHiddenNeurons and maxLayers limit the size of the neural network, and maxLayerSkips limits the depth of layer-to-layer connections. Set unused parameters to "NO_MAX".
• Set ifNNhasBias to "HAS_BIAS" unless you don't want to allow a bias (i.e. constant) term in each neuron's input, in which case set this to "NO_BIAS".
• Set ifAllowingInputOutputConnections to "ALLOW_IO_CONNECTIONS" or "NO_IO_CONNECTIONS" depending on whether to allow the input layer to feed directly into the output layer. (Outliers in new input data might cause wild outputs).
• sampleOutputs is a matrix having dimensions numTargetOutputs and numSamples, to which the training output as calculated by the server will be written. This is mainly a check that the data went through the pipes OK. If you don't care, ignore the return value.

sampleOutputs = tabular_encoder(trainingSamples, sampleTableTranspose, importances,
ifDoEncoder, ifDoDecoder, numEncodingFeatures, numVariationalFeatures, variationalDist,
maxWeights, maxHiddenNeurons, maxLayers, maxLayerSkips, ifNNhasBias)

Generates an autoencoder (or an encoder or decoder) using unsupervised training on trainingSamples.

• trainingSamples is a matrix having dimensions numFeatures and numSamples, in the order determined by sampleTableTranspose.
• sampleTableTranspose and importances are set the same way as for tabular_regressor(...).
• The size of the encoding is determined by numEncodingFeatures.
  • So-called variational features are extra randomly-distributed inputs used by the decoder, analogous to the extra degrees of freedom a variational autoencoder generates.
  • variationalDist is set to "UNIFORM_DIST" if the variational inputs are uniformly-(0, 1)-distributed, or "NORMAL_DIST" if they are normally distributed (zero mean, unit variance).
• Set ifDoEncoder to either "DO_ENCODER" or "NO_ENCODER", the latter being for a decoder-only network.
• Set ifDoDecoder to either "DO_DECODER" or "NO_DECODER", the latter being for an encoder-only network.
• The last 5 parameters are set the same way as for tabular_regressor(...).

sampleOutput = runSample(sampleInput [, sampleVariationalInput])

Runs the neural network on a single sample, and returns the network output.

• If it is an autoencoder or decoder with variational features, sample the variational features from the appropriate distribution and pass them as a second argument.
• The return value is simply a copy of the last layer of the network myNN.y[myNN.numLayers].