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Examples of scripts

Gustavo Rosa edited this page Mar 15, 2017 · 10 revisions

The LibDEEP package contains a directory LibDEEP/examples, in which you can find some useful examples to work with our deep learning techniques.


RBM.c Usage

Usage: RBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)

GaussianRBM.c Usage

Usage: GaussianRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: variance value for gaussian

DropoutRBM.c Usage

Usage: DropoutRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)

DropoutGaussianRBM.c Usage

Usage: DropoutGaussianRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: variance value for gaussian

DropconnectRBM.c Usage

Usage: DropconnectRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)

DRBM.c Usage

Usage: DRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence

DropoutDRBM.c Usage

Usage: DropoutDRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence

GaussianDRBM.c Usage

Usage: GaussianDRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: variance value for gaussian

DropoutGaussianDRBM.c Usage

Usage: DropoutGaussianDRBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: variance value for gaussian


DBM.c Usage

Usage: DBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10> <P11>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBM layers
P11: output parameters file name

TDBM.c Usage

Usage: TDBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10> <P11> <P12>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBM layers
P11: temperature
P12: output parameters file name

DropoutDBM.c Usage

Usage: DropoutDBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10> <P11>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBM layers
P11: output parameters file name

DropconnectDBM.c Usage

Usage: DropconnectDBM <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBM layers
P11: output parameters file name


DBN.c Usage

Usage: DBN <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBN layers

TDBN.c Usage

Usage: TDBN <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10> <P11> <P12>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBN layers
P11: temperature
P12: output parameters file name

DropoutDBN.c Usage

Usage: DropoutDBN <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBN layers

DropconnectDBN.c Usage

Usage: DropconnectDBN <P1> <P2> <P3> <P4> <P5> <P6> <P7> <P8> <P9> <P10>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: output results file name
P4: cross-validation iteration number
P5: input parameters file name
P6: number of epochs
P7: batch size
P8: number of iterations for Contrastive Divergence
P9: training method (1 - CD | 2 - PCD | 3 - FPCD)
P10: number of DBN layers


EPNN.c Usage

Usage: EPNN <P1> <P2> <P3> <P4> <P5> <P6>

P1: training dataset in the OPF file format
P2: evaluating dataset in the OPF file format (only required in learning phase)
P3: testing dataset in the OPF file format
P4: output results file name
P5: input parameters file name
P6: best parameters output file name


Linear_Regression.c Usage

Usage: Linear_Regression <P1> <P2>

P1: training dataset in the OPF file format
P2: learning rate


Logistic_Regression.c Usage

Usage: Logistic_Regression <P1> <P2> <P3>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: learning rate


PCA.c Usage

Usage: PCA <P1> <P2> <P3>

P1: input file in the OPF file format
P2: output file
P3: percentage of the final number of dimensions


ANN-RBF.c Usage

Usage: ANN-RBF <P1> <P2> <P3> <P4>

P1: training dataset in the OPF file format
P2: testing dataset in the OPF file format
P3: 1 for OPF clustering or 2 for K-Means clustering
P4: kmax value for OPF or number of clusters (k) for K-Means