DOC 2.3.3
Deep Learning

Deep learning.

dl4j csv_train_and_save_modeldl4j csv_load_modeldl4j csv_predictdl4j showdl4j existdl4j deletedl n_bayesian create_train_filedl n_bayesian showdl n_bayesian existdl n_bayesian create_modeldl n_bayesian loaddl n_bayesian predictdl n_bayesian deletedl bayesian showdl bayesian existdl bayesian createdl bayesian add_sentencedl bayesian initdl bayesian predictdl bayesian deletedl csv execute_configdl csv load_networkdl csv predictdl img step1 create_trainingdl img step2 add_imagedl img step3 create_hidden_layerdl img step4 create_or_load_networkdl img step5 train_networkdl img step6 predictdl img step7 close_filedl img execute_configdl img load_networkdl img predict

dl4j csv_train_and_save_model <json_config>

Description

    To Train and save a model from a CSV file.

Parameters

    json_config:   The JSON configuration - string - required
admin
#------------------------------------------; # START GLOBAL PARAMETERS ; #------------------------------------------; json load "dl4j_config" "{}"; json iobject "dl4j_config" / "dataTrainFile" "demo/dl4j_iris.csv" STR; #The CSV source file; json iobject "dl4j_config" / "batchSizeTraining" "150" STR; #The number of line to train; json iobject "dl4j_config" / "csvTest" "demo/dl4j_iris_test.csv" STR; #The CSV source file; json iobject "dl4j_config" / "testBatchSize" "9" STR; #The number of line to test; json iobject "dl4j_config" / "pathToSaveModel" "demo/dl4j_iris.model" STR; #Save the model; json iobject "dl4j_config" / "pathToSaveNormalizer" "demo/dl4j_iris.normz" STR; #Save the normalizer; json iobject "dl4j_config" / "numClasses" "3" STR; #The number of classes; json iobject "dl4j_config" / "labelIndex" "4" STR; #Where is the position of the label index (starts with 0); json iobject "dl4j_config" / "iterations" "3000" STR; #The number of iterations; json iobject "dl4j_config" / "epochs" "1" STR; #The number of fit; json iobject "dl4j_config" / "seed" "123" STR; #Random number; json iobject "dl4j_config" / "learningRate" 0.01 STR; json iobject "dl4j_config" / "l2" 1e-4 STR; json iobject "dl4j_config" / "regularization" true STR; json iobject "dl4j_config" / "activation" "TANH" STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; json iobject "dl4j_config" / "weightInit" "XAVIER" STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #------------------------------------------; # ADDITIONAL GLOBAL PARAMETERS ; #------------------------------------------; #json iobject "dl4j_config" / "optimizationAlgo" null STR; #STOCHASTIC_GRADIENT_DESCENT|LINE_GRADIENT_DESCENT|LBFGS|HESSIAN_FREE|CONJUGATE_GRADIENT; #json iobject "dl4j_config" / "biasInit" null STR; #json iobject "dl4j_config" / "biasLearningRate" null STR; #json iobject "dl4j_config" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_config" / "lrPolicyDecayRate" null STR; #json iobject "dl4j_config" / "lrPolicyPower" null STR; #json iobject "dl4j_config" / "lrPolicySteps" null STR; #json iobject "dl4j_config" / "l1" null STR; #json iobject "dl4j_config" / "l1Bias" null STR; #json iobject "dl4j_config" / "l2Bias" null STR; #json iobject "dl4j_config" / "maxNumLineSearchIterations" null STR; #json iobject "dl4j_config" / "miniBatch" null STR; #json iobject "dl4j_config" / "minimize" null STR; #json iobject "dl4j_config" / "useDropConnect" null STR; #json iobject "dl4j_config" / "dropOut" null STR; #json iobject "dl4j_config" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_config" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_config" / "cacheMode" null STR; #NONE|HOST|DEVICE; #json iobject "dl4j_config" / "convolutionMode" null STR; #Truncate|Strict|Same; #json iobject "dl4j_config" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_config" / "gradientNormalizationThreshold" null STR; #------------------------------------------; # BUILD LAYERS ; #------------------------------------------; json iobject "dl4j_config" / "layers" "[]" ARRAY; #------------------------------------------; json load "dl4j_hidden_layer01" "{}"; json iobject "dl4j_hidden_layer01" / "type" "DenseLayer" STR; json iobject "dl4j_hidden_layer01" / "nIn" "4" STR; json iobject "dl4j_hidden_layer01" / "nOut" "3" STR; json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer01") OBJ; #------------------------------------------; json load "dl4j_hidden_layer02" "{}"; json iobject "dl4j_hidden_layer02" / "type" "DenseLayer" STR; json iobject "dl4j_hidden_layer02" / "nIn" "3" STR; json iobject "dl4j_hidden_layer02" / "nOut" "3" STR; json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer02") OBJ; #------------------------------------------; json load "dl4j_hidden_layer03" "{}"; json iobject "dl4j_hidden_layer03" / "type" "OutputLayer" STR; json iobject "dl4j_hidden_layer03" / "nIn" "3" STR; json iobject "dl4j_hidden_layer03" / "nOut" "3" STR; json iobject "dl4j_hidden_layer03" / "activation" "SOFTMAX" STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; json iobject "dl4j_hidden_layer03" / "lossFunction" "NEGATIVELOGLIKELIHOOD" STR; #XENT|SQUARED_LOSS|SQUARED_HINGE|RMSE_XENT|RECONSTRUCTION_CROSSENTROPY|POISSON|NEGATIVELOGLIKELIHOOD|MSE|MEAN_SQUARED_LOGARITHMIC_ERROR|MEAN_ABSOLUTE_PERCENTAGE_ERROR|MEAN_ABSOLUTE_ERROR|MCXENT|L2|L1|KL_DIVERGENCE|HINGE|CUSTOM|EXPLL|COSINE_PROXIMITY; json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer03") OBJ; #------------------------------------------; # EXAMPLE LAYERS ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "SubsamplingLayer" STR; #json iobject "dl4j_hidden_layer00" / "kernelSize" "5:5" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "stride" "5:5" STR; #json iobject "dl4j_hidden_layer00" / "padding" "5:5" STR; #json iobject "dl4j_hidden_layer00" / "convolutionMode" null STR; #Truncate|Strict|Same; #json iobject "dl4j_hidden_layer00" / "eps" null STR; #json iobject "dl4j_hidden_layer00" / "pnorm" null STR; #json iobject "dl4j_hidden_layer00" / "poolingType" null STR; #SUM|PNORM|NONE|MAX|AVG; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "RBM" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "hiddenUnit" null STR; #SOFTMAX|RECTIFIED|IDENTITY|GAUSSIAN|BINARY; #json iobject "dl4j_hidden_layer00" / "k" null STR; #json iobject "dl4j_hidden_layer00" / "lossFunction" null STR; #XENT|SQUARED_LOSS|SQUARED_HINGE|RMSE_XENT|RECONSTRUCTION_CROSSENTROPY|POISSON|NEGATIVELOGLIKELIHOOD|MSE|MEAN_SQUARED_LOGARITHMIC_ERROR|MEAN_ABSOLUTE_PERCENTAGE_ERROR|MEAN_ABSOLUTE_ERROR|MCXENT|L2|L1|KL_DIVERGENCE|HINGE|CUSTOM|EXPLL|COSINE_PROXIMITY; #json iobject "dl4j_hidden_layer00" / "preTrainIterations" null STR; #json iobject "dl4j_hidden_layer00" / "sparsity" null STR; #json iobject "dl4j_hidden_layer00" / "visibleBiasInit" null STR; #json iobject "dl4j_hidden_layer00" / "visibleUnit" null STR; #SOFTMAX|LINEAR|IDENTITY|GAUSSIAN|BINARY; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "LSTM" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "forgetGateBiasInit" null STR; #json iobject "dl4j_hidden_layer00" / "gateActivationFunction" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "LocalResponseNormalization" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "alpha" null STR; #json iobject "dl4j_hidden_layer00" / "beta" null STR; #json iobject "dl4j_hidden_layer00" / "k" null STR; #json iobject "dl4j_hidden_layer00" / "n" null STR; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "GravesLSTM" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "forgetGateBiasInit" null STR; #json iobject "dl4j_hidden_layer00" / "gateActivationFunction" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "GravesBidirectionalLSTM" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "forgetGateBiasInit" null STR; #json iobject "dl4j_hidden_layer00" / "gateActivationFunction" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "AutoEncoder" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "corruptionLevel" null STR; #json iobject "dl4j_hidden_layer00" / "preTrainIterations" null STR; #json iobject "dl4j_hidden_layer00" / "sparsity" null STR; #json iobject "dl4j_hidden_layer00" / "visibleBiasInit" null STR; #json iobject "dl4j_hidden_layer00" / "lossFunction" null STR; #XENT|SQUARED_LOSS|SQUARED_HINGE|RMSE_XENT|RECONSTRUCTION_CROSSENTROPY|POISSON|NEGATIVELOGLIKELIHOOD|MSE|MEAN_SQUARED_LOGARITHMIC_ERROR|MEAN_ABSOLUTE_PERCENTAGE_ERROR|MEAN_ABSOLUTE_ERROR|MCXENT|L2|L1|KL_DIVERGENCE|HINGE|CUSTOM|EXPLL|COSINE_PROXIMITY; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "BatchNormalization" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "eps" null STR; #json iobject "dl4j_hidden_layer00" / "beta" null STR; #json iobject "dl4j_hidden_layer00" / "decay" null STR; #json iobject "dl4j_hidden_layer00" / "gamma" null STR; #json iobject "dl4j_hidden_layer00" / "lockGammaBeta" null STR; #Boolean; #json iobject "dl4j_hidden_layer00" / "minibatch" null STR; #Boolean; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "GlobalPoolingLayer" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "poolingType" null STR; #SUM|PNORM|NONE|MAX|AVG; #json iobject "dl4j_hidden_layer00" / "pnorm" null STR; #json iobject "dl4j_hidden_layer00" / "collapseDimensions" null STR; #Boolean; #json iobject "dl4j_hidden_layer00" / "poolingDimensions" null STR; #3:3; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "EmbeddingLayer" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "DropoutLayer" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "ActivationLayer" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "DenseLayer" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "ConvolutionLayer" STR; #json iobject "dl4j_hidden_layer00" / "kernelSize" "5:5" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "stride" "5:5" STR; #json iobject "dl4j_hidden_layer00" / "padding" "5:5" STR; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "epsilon" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "momentum" null STR; #json iobject "dl4j_hidden_layer00" / "rho" null STR; #json iobject "dl4j_hidden_layer00" / "rmsDecay" null STR; #json iobject "dl4j_hidden_layer00" / "convolutionMode" null STR; #Truncate|Strict|Same; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "adamMeanDecay" null STR; #json iobject "dl4j_hidden_layer00" / "adamVarDecay" null STR; #json iobject "dl4j_hidden_layer00" / "cudnnAlgoMode" null STR; #USER_SPECIFIED|PREFER_FASTEST|NO_WORKSPACE; #json iobject "dl4j_hidden_layer00" / "cudnnBwdDataMode" null STR; #WINOGRAD_NONFUSED|WINOGRAD|FFT_TILING|FFT|COUNT|ALGO_1|ALGO_0; #json iobject "dl4j_hidden_layer00" / "cudnnBwdFilterMode" null STR; #WINOGRAD_NONFUSED|WINOGRAD|FFT_TILING|FFT|COUNT|ALGO_3|ALGO_1|ALGO_0; #json iobject "dl4j_hidden_layer00" / "cudnnFwdMode" null STR; #WINOGRAD_NONFUSED|WINOGRAD|IMPLICIT_PRECOMP_GEMM|IMPLICIT_GEMM|GEMM|FFT_TILING|FFT|DIRECT|COUNT; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "OutputLayer" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "lossFunction" null STR; #XENT|SQUARED_LOSS|SQUARED_HINGE|RMSE_XENT|RECONSTRUCTION_CROSSENTROPY|POISSON|NEGATIVELOGLIKELIHOOD|MSE|MEAN_SQUARED_LOGARITHMIC_ERROR|MEAN_ABSOLUTE_PERCENTAGE_ERROR|MEAN_ABSOLUTE_ERROR|MCXENT|L2|L1|KL_DIVERGENCE|HINGE|CUSTOM|EXPLL|COSINE_PROXIMITY; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "CenterLossOutputLayer" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "alpha" null STR; #json iobject "dl4j_hidden_layer00" / "lambda" null STR; #json iobject "dl4j_hidden_layer00" / "gradientCheck" null STR; #Boolean; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "lossFunction" null STR; #XENT|SQUARED_LOSS|SQUARED_HINGE|RMSE_XENT|RECONSTRUCTION_CROSSENTROPY|POISSON|NEGATIVELOGLIKELIHOOD|MSE|MEAN_SQUARED_LOGARITHMIC_ERROR|MEAN_ABSOLUTE_PERCENTAGE_ERROR|MEAN_ABSOLUTE_ERROR|MCXENT|L2|L1|KL_DIVERGENCE|HINGE|CUSTOM|EXPLL|COSINE_PROXIMITY; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; #json load "dl4j_hidden_layer00" "{}"; #json iobject "dl4j_hidden_layer00" / "type" "RnnOutputLayer" STR; #json iobject "dl4j_hidden_layer00" / "nIn" "3" STR; #json iobject "dl4j_hidden_layer00" / "nOut" "3" STR; #json iobject "dl4j_hidden_layer00" / "dropOut" "0.25" STR; #json iobject "dl4j_hidden_layer00" / "activation" null STR; #TANH|SOFTSIGN|SOFTPLUS|SOFTMAX|SIGMOID|SELU|RRELU|RELU|RECTIFIEDTANH|RATIONALTANH|LEAKYRELU|IDENTITY|HARDTANH|HARDSIGMOID|ELU|CUBE; #json iobject "dl4j_hidden_layer00" / "weightInit" null STR; #ZERO|XAVIER_UNIFORM|XAVIER_LEGACY|XAVIER_FAN_IN|XAVIER|UNIFORM|SIGMOID_UNIFORM|RELU_UNIFORM|RELU|DISTRIBUTION; #json iobject "dl4j_hidden_layer00" / "dist" null STR; #BinomialDistribution:0:1|NormalDistribution:0:1|UniformDistribution:0:1; #json iobject "dl4j_hidden_layer00" / "biasInit" null STR; #json iobject "dl4j_hidden_layer00" / "biasLearningRate" null STR; #json iobject "dl4j_hidden_layer00" / "l1" null STR; #json iobject "dl4j_hidden_layer00" / "l1Bias" null STR; #json iobject "dl4j_hidden_layer00" / "l2" null STR; #json iobject "dl4j_hidden_layer00" / "l2Bias" null STR; #json iobject "dl4j_hidden_layer00" / "gradientNormalization" null STR; #RenormalizeL2PerParamType|RenormalizeL2PerLayer|None|ClipL2PerParamType|ClipL2PerLayer|ClipElementWiseAbsoluteValue; #json iobject "dl4j_hidden_layer00" / "gradientNormalizationThreshold" null STR; #json iobject "dl4j_hidden_layer00" / "learningRate" null STR; #json iobject "dl4j_hidden_layer00" / "learningRateDecayPolicy" null STR; #TorchStep|Step|Sigmoid|Score|Schedule|Poly|None|Inverse|Exponential; #json iobject "dl4j_hidden_layer00" / "updater" null STR; #SGD|RMSPROP|NONE|NESTEROVS|NADAM|ADAMAX|ADAM|ADAGRAD|ADADELTA; #json iobject "dl4j_hidden_layer00" / "lossFunction" null STR; #XENT|SQUARED_LOSS|SQUARED_HINGE|RMSE_XENT|RECONSTRUCTION_CROSSENTROPY|POISSON|NEGATIVELOGLIKELIHOOD|MSE|MEAN_SQUARED_LOGARITHMIC_ERROR|MEAN_ABSOLUTE_PERCENTAGE_ERROR|MEAN_ABSOLUTE_ERROR|MCXENT|L2|L1|KL_DIVERGENCE|HINGE|CUSTOM|EXPLL|COSINE_PROXIMITY; #json iarray "dl4j_config" /layers (json doc "dl4j_hidden_layer00") OBJ; #------------------------------------------; # END GLOBAL PARAMETERS ; #------------------------------------------; json iobject "dl4j_config" / "backprop" true STR; json iobject "dl4j_config" / "pretrain" false STR; #------------------------------------------; # Train and save the model/serializer ; #------------------------------------------; dl4j csv_train_and_save_model (json doc "dl4j_config");
mentdb
1

dl4j csv_load_model <dl4jId> <json_config>

Description

    Load a DL4J model and normalizer into the memory

Parameters

    dl4jId:   The memory key to get the DL4J model - string - required
    json_config:   The JSON configuration - string - required
admin
#------------------------------------------; # Load the model and the normalizer ; #------------------------------------------; dl4j csv_load_model "dl4jId1" (json doc "dl4j_config");
mentdb
1

dl4j csv_predict <dl4jId> <json_config> <csv_file> <nb_line_to_predict>

Description

    Make a prediction from a DL4J model

Parameters

    dl4jId:   The memory key to get the DL4J model - string - required
    json_config:   The JSON configuration - string - required
    csv_file:   The CSV file to predict - string - required
    nb_line_to_predict:   The number of line to predict - number - required
admin
#------------------------------------------; # Make predictions ; #------------------------------------------; dl4j csv_predict "dl4jId1" (json doc "dl4j_config") "demo/dl4j_iris_test.csv" 3;
mentdb
[ { "probs": [ 0.97952205, 0.01892847, 0.0015495354 ], "prob_class": "class1", "prob_index": 0 }, { "probs": [ 0.9793347, 0.01910426, 0.0015610401 ], "prob_class": "class1", "prob_index": 0 }, { "probs": [ 0.97940457, 0.019038359, 0.0015570737 ], "prob_class": "class1", "prob_index": 0 } ]

dl4j show

Description

    To show all DL4J networks.

admin
dl4j show
mentdb
[]

dl4j exist <dl4jId>

Description

    To check if a DL4J network was already loaded.

Parameters

    dl4jId:   The dl4j id - string - required
admin
dl4j exist "dl4jId1";
mentdb
1

dl4j delete <dl4jId>

Description

    To delete a DL4J network from the memory.

Parameters

    dl4jId:   The dl4j id - string - required
admin
dl4j delete "dl4jId1";
mentdb
1

dl n_bayesian create_train_file

Description

    To get a train file.

admin
file create "demo/train.txt" "transfer VIR RECU 854526,VIR RECU 8545269324 DE: OCTO TECHNOLOGY MOTIF: VIREMENT SALAIRES JUIN 11 withdrawal CARTE X2052 RETRAI,CARTE X2052 RETRAIT DAB 22/06 21H37 CIC PARIS SAINT ROCH 10859A00 payment CARTE X2052 21/06 ,CARTE X2052 21/06 PHIE LA BOETIE fees OPTION TRANQUILLIT,OPTION TRANQUILLITE fees COTISATION JAZZ,COTISATION JAZZ atmfees FRAIS PAIEMENT HOR,FRAIS PAIEMENT HORS ZONE EURO 1 PAIEMENT A 1.00 EUR NT 38.06 EUR A 2.70 atmfees FRAIS PAIEMENT HOR,FRAIS PAIEMENT HORS ZONE EURO 1 PAIEMENT A 1.00 EUR NT 3.33 EUR A";
mentdb

dl n_bayesian show

Description

    To show all Naive Bayesian networks.

admin
dl n_bayesian show
mentdb
[]

dl n_bayesian exist <key>

Description

    To check if a Naive Bayesian network already exist.

Parameters

    key:   The network key - string - required
admin
dl n_bayesian exist "bayesian1";
mentdb
1

dl n_bayesian create_model <lang> <train_file_path> <iterations_param> <model_file_path_to_save>

Description

    To create a new Naive Bayesian network model.

Parameters

    lang:   The language - string - required
    train_file_path:   The train file path - string - required
    iterations_param:   The iterations param - number (ex: 10) - required
    model_file_path_to_save:   The model file path to save - string - required
admin
dl n_bayesian create_model "en" "demo/train.txt" 10 "demo/model.bin";
mentdb
1

dl n_bayesian load <key> <model_file_path>

Description

    To load a Naive Bayesian network model.

Parameters

    key:   The network key - string - required
    model_file_path:   The model file path - string - required
admin
dl n_bayesian load "bayesian1" "demo/model.bin";
mentdb
1

dl n_bayesian predict <key> <sentence>

Description

    To predict a sentence.

Parameters

    key:   The network key - string - required
    sentence:   The sentence - string - required
admin
dl n_bayesian predict "bayesian1" "21/06 PHIE LA BOETIE";
mentdb
{ "input": "I\u0027m happy", "prediction": "positif", "best_percent": "66,666667 %", "best_index": 0, "probabilities": [ { "prob_double": 0.6666666666666666, "index": 0, "prob_percent": "66,666667 %", "key": "positif" }, { "prob_double": 0.3333333333333333, "index": 1, "prob_percent": "33,333333 %", "key": "negatif" } ], "best_double": 0.6666666666666666 }

dl n_bayesian delete <key>

Description

    To delete a Naive Bayesian network.

Parameters

    key:   The network key - string - required
admin
dl n_bayesian delete "bayesian1";
mentdb
1

dl bayesian show

Description

    To show all Bayesian networks.

admin
dl bayesian show
mentdb
[]

dl bayesian exist <key>

Description

    To check if a Bayesian network already exist.

Parameters

    key:   The network key - string - required
admin
dl bayesian exist "bayesian1";
mentdb
1

dl bayesian create <key> <cats>

Description

    To create a new Bayesian network.

Parameters

    key:   The network key - string - required
    cats:   The categories (JSON array) - string - required
admin
dl bayesian create "bayesian1" "[\"positif\", \"negatif\"]";
mentdb
1

dl bayesian add_sentence <key> <cat> <sentence>

Description

    To create a new Bayesian network.

Parameters

    key:   The network key - string - required
    cat:   The category key - string - required
    sentence:   The sentence - string - required
admin
dl bayesian add_sentence "bayesian1" "positif" "I'm happy";
mentdb
1

dl bayesian init <key> <laplace_int>

Description

    To init a Bayesian network.

Parameters

    key:   The network key - string - required
    laplace_int:   The Laplace int - number - required
admin
dl bayesian init "bayesian1" 1;
mentdb
1

dl bayesian predict <key> <sentence>

Description

    To predict a sentence.

Parameters

    key:   The network key - string - required
    sentence:   The sentence - string - required
admin
dl bayesian predict "bayesian1" "I'm happy";
mentdb
{ "input": "I\u0027m happy", "prediction": "positif", "best_percent": "66,666667 %", "best_index": 0, "probabilities": [ { "prob_double": 0.6666666666666666, "index": 0, "prob_percent": "66,666667 %", "key": "positif" }, { "prob_double": 0.3333333333333333, "index": 1, "prob_percent": "33,333333 %", "key": "negatif" } ], "best_double": 0.6666666666666666 }

dl bayesian delete <key>

Description

    To delete a Bayesian network.

Parameters

    key:   The network key - string - required
admin
dl bayesian delete "bayesian1";
mentdb
1

dl csv execute_config <jsonConfig>

Description

    Train a CSV file.

Parameters

    jsonConfig:   The train JSON configuration - string - required
admin
json load "csv_config" "{}"; json iobject "csv_config" / "filePath" "demo/iris.data.txt" STR; json iobject "csv_config" / "modelPath" "demo/iris.md" STR; json iobject "csv_config" / "helperPath" "demo/iris.hl" STR; json iobject "csv_config" / "nbLoop" "6" STR; json iobject "csv_config" / "validationPercent" "0.3" STR; json iobject "csv_config" / "shuffle" "true" STR; json iobject "csv_config" / "seed" "1001" STR; json iobject "csv_config" / "cols" "[]" ARRAY; json load "col" "{}"; json iobject "col" / "index" "0" STR; json iobject "col" / "title" "sepal-length" STR; json iobject "col" / "type" "in" STR; json iarray "csv_config" "/cols" (json doc "col") OBJ; json load "col" "{}"; json iobject "col" / "index" "1" STR; json iobject "col" / "title" "sepal-width" STR; json iobject "col" / "type" "in" STR; json iarray "csv_config" "/cols" (json doc "col") OBJ; json load "col" "{}"; json iobject "col" / "index" "2" STR; json iobject "col" / "title" "petal-length" STR; json iobject "col" / "type" "in" STR; json iarray "csv_config" "/cols" (json doc "col") OBJ; json load "col" "{}"; json iobject "col" / "index" "3" STR; json iobject "col" / "title" "petal-width" STR; json iobject "col" / "type" "in" STR; json iarray "csv_config" "/cols" (json doc "col") OBJ; json load "col" "{}"; json iobject "col" / "index" "4" STR; json iobject "col" / "title" "species" STR; json iobject "col" / "type" "out" STR; json iarray "csv_config" "/cols" (json doc "col") OBJ; dl csv execute_config (json doc "csv_config");
mentdb
1

dl csv load_network <modelFilePath> <helperFilePath>

Description

    Load the model and the helper into the memory.

Parameters

    modelFilePath:   The model file path - string - required
    helperFilePath:   The helper file path - string - required
admin
dl csv load_network "demo/iris.md" "demo/iris.hl";
mentdb
1

dl csv predict <jsonArrayInput>

Description

    Predict from the model.

Parameters

    jsonArrayInput:   The JSON array that contains input values - string - required
admin
json load "input" "[5.9, 3.0, 5.1, 1.8]"; dl csv predict (json doc "input");
mentdb
Iris-virginica
admin
json load "input" "[5.6, 2.9, 3.6, 1.3]"; dl csv predict (json doc "input");
mentdb
Iris-versicolor

dl img step1 create_training <writerId> <width> <height> <isRGB>

Description

    Create a training file.

Parameters

    writerId:   The writer id - string - required
    width:   The image width - number - required
    height:   The image height - number - required
    isRGB:   Is RGB ? (true, false) - bool - required
admin
#Create the training file; file writer_open "w1" "demo/animals/imgTrainConfig.txt" true TEXT "utf-8"; dl img step1 create_training "w1" 100 100 true;
mentdb
1

dl img step2 add_image <writerId> <imgPath> <identity>

Description

    Add image into the training file.

Parameters

    writerId:   The writer id - string - required
    imgPath:   The image path - string - required
    identity:   The image tag - string - required
admin
#Load input images; -> "[dir]" "demo/animals/english_springer"; -> "[id]" "english_springer"; json load "files" (file dir_list [dir]); -> "[nbFiles]" (json count "files" /); -> "[iFiles]" 0; for (-> "[i]" 0) (< [i] [nbFiles]) (++ "[i]") { -> "[cur_file]" (json select "files" (concat "/[" [i] "]")); if (string ends_with [cur_file] ".jpg") { dl img step2 add_image "w1" (concat [dir] "/" [cur_file]) [id]; ++ "[iFiles]"; }; }; file writer_flush "w1"; concat [iFiles] " files added."
mentdb
1

dl img step3 create_hidden_layer <writerId> <nbNeuron>

Description

    Create a hidden layer.

Parameters

    writerId:   The writer id - string - required
    nbNeuron:   The number of neuron in the hidden layers - number - required
admin
dl img step3 create_hidden_layer "w1" "100" file writer_flush "w1";
mentdb
1

dl img step4 create_or_load_network <writerId> <activation> <saveNetworkPath>

Description

    Create or load a network.

Parameters

    writerId:   The writer id - string - required
    activation:   The activation function (ex: BiPolar|BipolarSteepenedSigmoid|ClippedLinear|Competitive|Elliott|ElliottSymmetric|Gaussian|Linear|LOG|Ramp|ReLU|Sigmoid|SIN|SoftMax|SteepenedSigmoid|Step|TANH) - string - required
    saveNetworkPath:   The path to save the network - string - required
admin
dl img step4 create_or_load_network "w1" "tanh" "demo/animals/network.eg"; file writer_flush "w1";
mentdb
1

dl img step5 train_network <writerId> <mode> <minutes> <strategyError> <strategyCycles> <saveNetworkPath>

Description

    Train a network.

Parameters

    writerId:   The writer id - string - required
    mode:   The mode (console|gui) - string - required
    minutes:   The number of minutes - number - required
    strategyError:   The strategy error (ex: 0.25) - number - required
    strategyCycles:   The strategy cycles (ex: 50) - number - required
    saveNetworkPath:   The path to save the network - string - required
admin
dl img step5 train_network "w1" "console" 1 0.25 50 "demo/animals/network.eg"; file writer_flush "w1";
mentdb
1

dl img step6 predict <writerId> <imgPath> <identity>

Description

    Predict an image from a neural network.

Parameters

    writerId:   The writer id - string - required
    imgPath:   The image path - string - required
    identity:   The image tag - string - required
admin
#Load input images; -> "[dir]" "demo/animals/english_springer_predict"; -> "[id]" "english_springer"; json load "files" (file dir_list [dir]); -> "[nbFiles]" (json count "files" /); -> "[iFiles]" 0; for (-> "[i]" 0) (< [i] [nbFiles]) (++ "[i]") { -> "[cur_file]" (json select "files" (concat "/[" [i] "]")); if (string ends_with [cur_file] ".jpg") { dl img step6 predict "w1" (concat [dir] "/" [cur_file]) [id]; ++ "[iFiles]"; }; }; file writer_flush "w1"; concat [iFiles] " files added.";
mentdb
1

dl img step7 close_file <writerId>

Description

    Close the config file.

Parameters

    writerId:   The writer id - string - required
admin
#Close the config file; file writer_close "w1";
mentdb
1

dl img execute_config <trainConfigFilePath>

Description

    Execute a config training file

Parameters

    trainConfigFilePath:   The train config file path - string - required
admin
in editor { dl img execute_config "demo/animals/imgTrainConfig.txt" };
mentdb
1

dl img load_network <networkPath>

Description

    Load a network into the memory

Parameters

    networkPath:   The network path - string - required
admin
dl img load_network "demo/animals/network.eg";
mentdb
1

dl img predict <imagePath> <isRGB> <width> <height> <jsonIdentity>

Description

    Predict an image from the network

Parameters

    imagePath:   The image path - string - required
    isRGB:   Is RGB ? (true|false) - string - required
    width:   The image width - string - required
    height:   The image height - string - required
    jsonIdentity:   The json identity - string - required
admin
dl img predict "dir/image.jpg" true 100 100 "{ \"0\": \"english_springer\" }";
mentdb
1


MentDB © 2012 - 2020 - Legal Notice