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updated helpfiles and examples for MLP(Regressor | Classifier)

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nix
Pierre Alexandre Tremblay 6 years ago
parent d6b5ebd686
commit 28728e79e8
3 changed files with 95 additions and 73 deletions
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2
release-packaging/HelpSource/Classes/FluidKNNRegressor.schelp
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@ -85,7 +85,7 @@ d = Dictionary.with(
~testdata.collect{|x, i| [i.asString, [x]]}.flatten)])
);
~targetdata.plot
~targetdata.plot;
~source.print;
~target.print;
~test.print;

68
release-packaging/HelpSource/Classes/FluidMLPClassifier.schelp
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@ -1,84 +1,80 @@
TITLE:: FluidMLPClassifier
summary:: Classification with a neural network
categories:: Undocumented classes
summary:: Classification with a multi-layer perceptron
categories:: Machine learning
related:: Classes/FluidMLPRegressor, Classes/FluidDataSet
DESCRIPTION::
Perform regression between link::Classes/FluidDataSet::s using a Multilayer Perception neural network.
CLASSMETHODS::
METHOD:: new
(describe method here)
Creates a new instance on the server.
ARGUMENT:: server
(describe argument here)
The link::Classes/Server:: on which to run this model.
ARGUMENT:: hidden
(describe argument here)
An link::Classes/Array:: that gives the sizes of any hidden layers in the network (default is two hidden layers of three units each).
ARGUMENT:: activation
(describe argument here)
The activation function to use for the hidden layer units.
ARGUMENT:: maxIter
(describe argument here)
The maximum number of iterations to use in training.
ARGUMENT:: learnRate
(describe argument here)
The learning rate of the network. Start small, increase slowly.
ARGUMENT:: momentum
(describe argument here)
The training momentum, default 0.9
ARGUMENT:: batchSize
(describe argument here)
The training batch size.
ARGUMENT:: validation
(describe argument here)
returns:: (describe returnvalue here)
The fraction of the DataSet size to hold back during training to validate the network against.
METHOD:: identity, relu, sigmoid, tanh
A set of convinience constants for the available activation functions.
INSTANCEMETHODS::
METHOD:: predictPoint
(describe method here)
ARGUMENT:: sourceBuffer
(describe argument here)
ARGUMENT:: action
(describe argument here)
returns:: (describe returnvalue here)
PRIVATE:: init, uid
METHOD:: fit
(describe method here)
Train the network to map between a source link::Classes/FluidDataSet:: and a target link::Classes/FluidLabelSet::
ARGUMENT:: sourceDataSet
(describe argument here)
Source data
ARGUMENT:: targetLabelSet
(describe argument here)
Target data
ARGUMENT:: action
(describe argument here)
Function to run when training is complete
returns:: (describe returnvalue here)
returns:: The training loss, or -1 if training failed
METHOD:: predict
(describe method here)
Apply the learned mapping to a DataSet (given a trained network)
ARGUMENT:: sourceDataSet
(describe argument here)
Input data
ARGUMENT:: targetLabelSet
(describe argument here)
Output data
ARGUMENT:: action
(describe argument here)
Function to run when complete
METHOD:: predictPoint
Apply the learned mapping to a single data point in a link::Classes/Buffer::
returns:: (describe returnvalue here)
ARGUMENT:: sourceBuffer
Input point
ARGUMENT:: action
A function to run when complete
EXAMPLES::

98
release-packaging/HelpSource/Classes/FluidMLPRegressor.schelp
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@ -1,7 +1,7 @@
TITLE:: FluidMLPRegressor
summary:: Regression with a multi-layer perceptron
categories:: Machine learning
related:: Classes/FluidDataSet
related:: Classes/FluidMLPClassifier, Classes/FluidDataSet
DESCRIPTION::
Perform regression between link::Classes/FluidDataSet::s using a Multilayer Perception neural network.
@ -18,25 +18,25 @@ ARGUMENT:: hidden
An link::Classes/Array:: that gives the sizes of any hidden layers in the network (default is two hidden layers of three units each).
ARGUMENT:: activation
Ativation function to use for the hidden layer units.
The activation function to use for the hidden layer units.
ARGUMENT:: maxIter
Maximum number of iterations to use in training.
The maximum number of iterations to use in training.
ARGUMENT:: learnRate
The learning rate of the network. Start small, increase slowly.
ARGUMENT:: momentum
Training momentum, default 0.9
The training momentum, default 0.9
ARGUMENT:: batchSize
Training batch size.
The training batch size.
ARGUMENT:: validation
The fraction of the DataSet size to hold back during training to validate the network against.
METHOD:: identity, relu, sigmoid, tanh
Convinience constants for the available activation functions.
A set of convinience constants for the available activation functions.
INSTANCEMETHODS::
@ -57,7 +57,7 @@ Function to run when training is complete
returns:: The training loss, or -1 if training failed
METHOD:: predict
Apply the learned mapping to a DataSet (given a trained network)
Apply the learned mapping to a link::Classes/FluidDataSet:: (given a trained network)
ARGUMENT:: sourceDataSet
Input data
@ -90,41 +90,67 @@ EXAMPLES::
code::
//Make a simple mapping between a ramp and a sine cycle, test with an exponentional ramp
(
{
~source = FluidDataSet.new(s,"mlpregressor_source");
~target = FluidDataSet.new(s,"mlpregressor_target");
~dest = FluidDataSet.new(s,"mlpregressor_dest");
~datapoint = Buffer.alloc(s,2);
~destpoint = Buffer.new(s);
~regressor = FluidMLPRegressor(s) ;
s.sync;
~source.read("/tmp/test_reg_source_200_lin.json");
~source.print;
~target.read("/tmp/test_reg_target_200_lin.json");
~target.print;
}.fork
~source = FluidDataSet(s,\mlp_regressor_source);
~target = FluidDataSet(s,\mlp_regressor_target);
~test = FluidDataSet(s,\mlp_regressor_dest);
~output = FluidDataSet(s,\mlp_regress_out);
~tmpbuf = Buffer.alloc(s,1);
~regressor = FluidMLPRegressor(s,[2],FluidMLPRegressor.tanh,1000,0.1,0.1,1,0);
)
//Train network to map source to target. fit() returns loss. If this is -1, then training has failed
//Make source, target and test data
(
~regressor.fit(~source,~target,action: {|x|
if(x != -1) {("MLP trained with loss"+x).postln;}{"Training failed. Try again (perhaps with a lower learning rate)".postln;}
~sourcedata = 128.collect{|i|i/128};
~targetdata = 128.collect{|i| sin(2*pi*i/128) };
~testdata = 128.collect{|i|(i/128)**2};
~source.load(
Dictionary.with(
*[\cols -> 1,\data -> Dictionary.newFrom(
~sourcedata.collect{|x, i| [i.asString, [x]]}.flatten)]);
);
~target.load(
d = Dictionary.with(
*[\cols -> 1,\data -> Dictionary.newFrom(
~targetdata.collect{|x, i| [i.asString, [x]]}.flatten)]);
);
~test.load(
Dictionary.with(
*[\cols -> 1,\data -> Dictionary.newFrom(
~testdata.collect{|x, i| [i.asString, [x]]}.flatten)]);
);
~targetdata.plot;
~source.print;
~target.print;
~test.print;
)
// Now make a regressor and fit it to the source and target, and predict against test
//grab the output data whilst we're at it, so we can inspect
// run this to train the network for up to 1000(max epochs to map source to target. fit() returns loss. If this is -1, then training has failed. Run until the printed error is satisfactory to you
~regressor.fit(~source, ~target, {|x|x.postln;});
//you can change parameters of the MLPregressor with setters
~regressor.learnRate = 0.01;
~regressor.momentum = 0;
~regressor.validation= 0.2;
(
~outputdata = Array(128);
~regressor.predict(~test, ~output, action:{
~output.dump{|x| 128.do{|i|
~outputdata.add(x["data"][i.asString][0])
}};
});
)
//Batch predict takes a FluidDataSet source, a FluidDataSet to write netwotk output to, and layer to read from
~regressor.predict(~source,~dest,2);
~dest.dump
//Single point predict uses Buffers rater than FluidDataSet:
{
~datapoint.setn(0,[1,1]);
~regressor.predictPoint(~datapoint,~destpoint,2);
s.sync;
~destpoint.loadToFloatArray(0,action:{|a|
a.postln;
});
}.fork
//We should see a single cycle of a chirp. if not,
~outputdata.plot;
::

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