committing changes for repo relocation

*.asv

*.mat

slprj

__pycache__

.ipynb_checkpoints

.vscode

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gitdir: C:/Users/ahmedi/Developer/bare_repos/.datadrivenmodels

{

	"folders": [

		{

			"path": "."

		},

		{

			"path": "..\\pyTorchBridge"

		},

		{

			"path": "..\\pyStateSpace"

		}

	],

	"settings": {}

}

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% Training an LSTM network to predict the next number

% from an input sequence.

% 

% Input:  1,2,3

% Output: 4 

N = 1000;

NTest = 200;

seqLen = 10;

seq = reshape([1:N], seqLen, [])';

testSeq = reshape([N+1:N+NTest], seqLen, [])';

% The input variables are normalized to have 0 mean

% and 1 variance.

mu = mean(seq(:));

sig = std(seq(:));

stdSeq = (seq - mu) / sig;

stdTest = (testSeq - mu) / sig;

% Preparing input data as a cell array of row vectors.

XTrain = mat2cell(stdSeq(1:end-1,:),...

                  ones(1, N/seqLen - 1));

XTest = mat2cell(stdTest(1:end-1,:),...

                  ones(1, NTest/seqLen - 1));

% For format of the response, see

% https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html

% The labels are a matrix of the next number for each

% training sequence.

YTrain = seq(2:end,1);

YTest = testSeq(2:end,1);

% Defining an LSTM network. An LSTM layer takes as

% input `numHiddenUnits` which is the number of time

% steps that an LSTM cell "memorizes" i.e. the window

% over which the LSTM layer operates.

layers = [...

    sequenceInputLayer(1)

    lstmLayer(10,'OutputMode','last')

    fullyConnectedLayer(1)

    regressionLayer];

options = trainingOptions('adam', ...

    'MaxEpochs',150, ...

    'MiniBatchSize', 5, ...

    'GradientThreshold',1, ...

    'InitialLearnRate',1.0, ...

    'LearnRateSchedule','piecewise', ...

    'LearnRateDropPeriod', 20, ...

    'LearnRateDropFactor',0.5, ...

    'Verbose',0, ...

    'Plots','training-progress');

net = trainNetwork(XTrain, YTrain, layers, options);

YPred = int32(predict(net, XTrain));

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