DataFrameに対するMachine LearningライブラリーであるMLを試す。入門PySpark Ch-06 ———————& […]
Author: anesth
Spark実践 #4 PySpark MLlib
PySparkの学習を通じて、Sparkの解析プロセスがRDDから、DataFrameへ移行していること、さらにMLlibの開発は終了し、MLに移行していることなどを学ぶ。 今回は、799万件の米国における2014年と2 […]
Spark実践 #3 PySpark DataFrames
PySparkに挑戦：テキストブックは、入門PySpark ————————— ~/.bash_profile […]
Spark実践 #2 Spark.mlによる分類の実装
「詳解Apache Spark」の例で毒キノコの外見判別ー決定木 ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー データは、UCIの機械学習さいとより、 https://archive.i […]
Spark実践 #1
自前のデータでSparkに挑戦してみる。 データは、昇圧剤の使用の有無と他の術前因子の相関を見る24010件のコード化した過去4年の麻酔データ ————— […]
Sparkling Water: Spark+H2O #5
前回のH2O DeepLearningモデル構築で最終的にDeepLearning_model.javaが出力される。
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/* Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0.html AUTOGENERATED BY H2O at 2018-10-28T16:28:21.908+09:00 3.10.5.4 Standalone prediction code with sample test data for DeepLearningModel named DeepLearning_model_1540691693623_1 How to download, compile and execute: mkdir tmpdir cd tmpdir curl http:/macbook-pro-5/***.***.***.***:54321/3/h2o-genmodel.jar > h2o-genmodel.jar curl http:/macbook-pro-5/***.***.***.***:54321/3/Models.java/DeepLearning_model_1540691693623_1 > DeepLearning_model_1540691693623_1.java javac -cp h2o-genmodel.jar -J-Xmx2g -J-XX:MaxPermSize=128m DeepLearning_model_1540691693623_1.java (Note: Try java argument -XX:+PrintCompilation to show runtime JIT compiler behavior.) */ import java.util.Map; import hex.genmodel.GenModel; import hex.genmodel.annotations.ModelPojo; @ModelPojo(name="DeepLearning_model_1540691693623_1", algorithm="deeplearning") public class DeepLearning_model_1540691693623_1 extends GenModel { public hex.ModelCategory getModelCategory() { return hex.ModelCategory.Binomial; } public boolean isSupervised() { return true; } public int nfeatures() { return 28; } public int nclasses() { return 2; } // Thread-local storage for input neuron activation values. final double[] NUMS = new double[28]; static class NORMMUL implements java.io.Serializable { public static final double[] VALUES = new double[28]; static { NORMMUL_0.fill(VALUES); } static final class NORMMUL_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 1.7687666758901264; sa[1] = 0.9913821723096016; sa[2] = 0.9936853236221495; sa[3] = 1.6666383037503143; sa[4] = 0.9937488864153216; sa[5] = 2.105437242840467; sa[6] = 0.9908361123051243; sa[7] = 0.9940879578054577; sa[8] = 0.9729178470795813; sa[9] = 2.0007082148981183; sa[10] = 0.9908480722586916; sa[11] = 0.9938660006765665; sa[12] = 0.9528949618561464; sa[13] = 2.0508575278770245; sa[14] = 0.9912345684749029; sa[15] = 0.9937069184546701; sa[16] = 0.8377686617849256; sa[17] = 1.9768892962453906; sa[18] = 0.9926596305568416; sa[19] = 0.9937642381155177; sa[20] = 0.7141691868612743; sa[21] = 1.4816770107928496; sa[22] = 2.62612559064267; sa[23] = 6.074042423656259; sa[24] = 2.5162755660661866; sa[25] = 1.9042873091548915; sa[26] = 2.7391280258211443; sa[27] = 3.1929106654741926; } } } static class NORMSUB implements java.io.Serializable { public static final double[] VALUES = new double[28]; static { NORMSUB_0.fill(VALUES); } static final class NORMSUB_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.991609543911382; sa[1] = -6.172673222856738E-5; sa[2] = 2.1854967778855427E-4; sa[3] = 0.9984508227147362; sa[4] = 2.7703720484945748E-5; sa[5] = 0.9908871828929133; sa[6] = -8.849292707005366E-5; sa[7] = -1.54437948956086E-4; sa[8] = 0.9999179533939873; sa[9] = 0.9927406409576117; sa[10] = -1.6275628806378383E-4; sa[11] = 7.234230467006848E-5; sa[12] = 1.0002384149332444; sa[13] = 0.992170569563542; sa[14] = 4.1840079250049434E-5; sa[15] = 3.615676062458686E-5; sa[16] = 0.999843197801924; sa[17] = 0.9861267051297294; sa[18] = 3.768967267508312E-5; sa[19] = 1.6822728241595495E-4; sa[20] = 1.0001443952117024; sa[21] = 1.0343311905419041; sa[22] = 1.0247880896606023; sa[23] = 1.0505599836914452; sa[24] = 1.0096686212679917; sa[25] = 0.9729231063653536; sa[26] = 1.0329730880581618; sa[27] = 0.9597638460111044; } } } // Workspace for categorical offsets. public static final int[] CATOFFSETS = {0}; // Hidden layer dropout ratios. public static final double[] HIDDEN_DROPOUT_RATIOS = {0.5,0.5,0.5}; // Number of neurons for each layer. public static final int[] NEURONS = {28,500,500,500,2}; // Thread-local storage for neuron activation values. final double[][] ACTIVATION = new double[][] { /* Input */ DeepLearning_model_1540691693623_1_Activation_0.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_1.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_2.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Activation_3.VALUES, /* Softmax */ DeepLearning_model_1540691693623_1_Activation_4.VALUES }; // Neuron bias values. public static final double[][] BIAS = new double[][] { /* Input */ DeepLearning_model_1540691693623_1_Bias_0.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_1.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_2.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Bias_3.VALUES, /* Softmax */ DeepLearning_model_1540691693623_1_Bias_4.VALUES }; // Connecting weights between neurons. public static final float[][] WEIGHT = new float[][] { /* Input */ DeepLearning_model_1540691693623_1_Weight_0.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_1.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_2.VALUES, /* RectifierDropout */ DeepLearning_model_1540691693623_1_Weight_3.VALUES, /* Softmax */ DeepLearning_model_1540691693623_1_Weight_4.VALUES }; // Names of columns used by model. public static final String[] NAMES = NamesHolder_DeepLearning_model_1540691693623_1.VALUES; // Number of output classes included in training data response column. public static final int NCLASSES = 2; // Column domains. The last array contains domain of response column. public static final String[][] DOMAINS = new String[][] { /* features0 */ null, /* features1 */ null, /* features2 */ null, /* features3 */ null, /* features4 */ null, /* features5 */ null, /* features6 */ null, /* features7 */ null, /* features8 */ null, /* features9 */ null, /* features10 */ null, /* features11 */ null, /* features12 */ null, /* features13 */ null, /* features14 */ null, /* features15 */ null, /* features16 */ null, /* features17 */ null, /* features18 */ null, /* features19 */ null, /* features20 */ null, /* features21 */ null, /* features22 */ null, /* features23 */ null, /* features24 */ null, /* features25 */ null, /* features26 */ null, /* features27 */ null, /* label */ DeepLearning_model_1540691693623_1_ColInfo_28.VALUES }; // Prior class distribution public static final double[] PRIOR_CLASS_DISTRIB = {0.4701428794437793,0.5298571205562207}; // Class distribution used for model building public static final double[] MODEL_CLASS_DISTRIB = null; public DeepLearning_model_1540691693623_1() { super(NAMES,DOMAINS); } public String getUUID() { return Long.toString(-5314795359721044940L); } // Pass in data in a double[], pre-aligned to the Model's requirements. // Jam predictions into the preds[] array; preds[0] is reserved for the // main prediction (class for classifiers or value for regression), // and remaining columns hold a probability distribution for classifiers. public final double[] score0( double[] data, double[] preds ) { java.util.Arrays.fill(preds,0); java.util.Arrays.fill(NUMS,0); int i = 0, ncats = 0; final int n = data.length; for(; i<n; ++i) { NUMS[i] = Double.isNaN(data[i]) ? 0 : (data[i] - NORMSUB.VALUES[i])*NORMMUL.VALUES[i]; } java.util.Arrays.fill(ACTIVATION[0],0); for (i=0; i<NUMS.length; ++i) { ACTIVATION[0][CATOFFSETS[CATOFFSETS.length-1] + i] = Double.isNaN(NUMS[i]) ? 0 : NUMS[i]; } for (i=1; i<ACTIVATION.length; ++i) { java.util.Arrays.fill(ACTIVATION[i],0); int cols = ACTIVATION[i-1].length; int rows = ACTIVATION[i].length; int extra=cols-cols%8; int multiple = (cols/8)*8-1; int idx = 0; float[] a = WEIGHT[i]; double[] x = ACTIVATION[i-1]; double[] y = BIAS[i]; double[] res = ACTIVATION[i]; for (int row=0; row<rows; ++row) { double psum0 = 0, psum1 = 0, psum2 = 0, psum3 = 0, psum4 = 0, psum5 = 0, psum6 = 0, psum7 = 0; for (int col = 0; col < multiple; col += 8) { int off = idx + col; psum0 += a[off ] * x[col ]; psum1 += a[off + 1] * x[col + 1]; psum2 += a[off + 2] * x[col + 2]; psum3 += a[off + 3] * x[col + 3]; psum4 += a[off + 4] * x[col + 4]; psum5 += a[off + 5] * x[col + 5]; psum6 += a[off + 6] * x[col + 6]; psum7 += a[off + 7] * x[col + 7]; } res[row] += psum0 + psum1 + psum2 + psum3; res[row] += psum4 + psum5 + psum6 + psum7; for (int col = extra; col < cols; col++) res[row] += a[idx + col] * x[col]; res[row] += y[row]; idx += cols; } if (i<ACTIVATION.length-1) { for (int r=0; r<ACTIVATION[i].length; ++r) { ACTIVATION[i][r] = Math.max(0, ACTIVATION[i][r]); if (i<ACTIVATION.length-1) { ACTIVATION[i][r] *= 1 - HIDDEN_DROPOUT_RATIOS[i-1]; } } } if (i == ACTIVATION.length-1) { double max = ACTIVATION[i][0]; for (int r=1; r<ACTIVATION[i].length; r++) { if (ACTIVATION[i][r]>max) max = ACTIVATION[i][r]; } double scale = 0; for (int r=0; r<ACTIVATION[i].length; r++) { ACTIVATION[i][r] = Math.exp(ACTIVATION[i][r] - max); scale += ACTIVATION[i][r]; } for (int r=0; r<ACTIVATION[i].length; r++) { if (Double.isNaN(ACTIVATION[i][r])) throw new RuntimeException("Numerical instability, predicted NaN."); ACTIVATION[i][r] /= scale; preds[r+1] = ACTIVATION[i][r]; } } } preds[0] = hex.genmodel.GenModel.getPrediction(preds, PRIOR_CLASS_DISTRIB, data, 0.44319463906785384); return preds; } } // Neuron activation values for Input layer class DeepLearning_model_1540691693623_1_Activation_0 implements java.io.Serializable { public static final double[] VALUES = new double[28]; static { DeepLearning_model_1540691693623_1_Activation_0_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Activation_0_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.0; sa[1] = 0.0; sa[2] = 0.0; sa[3] = 0.0; sa[4] = 0.0; sa[5] = 0.0; sa[6] = 0.0; sa[7] = 0.0; sa[8] = 0.0; sa[9] = 0.0; sa[10] = 0.0; sa[11] = 0.0; sa[12] = 0.0; sa[13] = 0.0; sa[14] = 0.0; sa[15] = 0.0; sa[16] = 0.0; sa[17] = 0.0; sa[18] = 0.0; sa[19] = 0.0; sa[20] = 0.0; sa[21] = 0.0; sa[22] = 0.0; sa[23] = 0.0; sa[24] = 0.0; sa[25] = 0.0; sa[26] = 0.0; sa[27] = 0.0; } } } // Neuron activation values for RectifierDropout layer class DeepLearning_model_1540691693623_1_Activation_1 implements java.io.Serializable { public static final double[] VALUES = new double[500]; static { DeepLearning_model_1540691693623_1_Activation_1_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Activation_1_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.0; sa[1] = 0.0; sa[2] = 0.0; sa[3] = 0.0; sa[4] = 0.0; sa[5] = 0.0; ...... sa[499] = 0.0; } } } // Neuron activation values for RectifierDropout layer class DeepLearning_model_1540691693623_1_Activation_2 implements java.io.Serializable { public static final double[] VALUES = new double[500]; static { DeepLearning_model_1540691693623_1_Activation_2_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Activation_2_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.0; sa[1] = 0.0; sa[2] = 0.0; sa[3] = 0.0; sa[4] = 0.0; sa[5] = 0.0; ...... sa[499] = 0.0; } } } // Neuron activation values for RectifierDropout layer class DeepLearning_model_1540691693623_1_Activation_3 implements java.io.Serializable { public static final double[] VALUES = new double[500]; static { DeepLearning_model_1540691693623_1_Activation_3_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Activation_3_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.0; sa[1] = 0.0; sa[2] = 0.0; sa[3] = 0.0; sa[4] = 0.0; sa[5] = 0.0; ...... sa[499] = 0.0; } } } // Neuron activation values for Softmax layer class DeepLearning_model_1540691693623_1_Activation_4 implements java.io.Serializable { public static final double[] VALUES = new double[2]; static { DeepLearning_model_1540691693623_1_Activation_4_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Activation_4_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.0; sa[1] = 0.0; } } } // Neuron bias values for Input layer class DeepLearning_model_1540691693623_1_Bias_0 implements java.io.Serializable { public static final double[] VALUES = null; } // Neuron bias values for RectifierDropout layer class DeepLearning_model_1540691693623_1_Bias_1 implements java.io.Serializable { public static final double[] VALUES = new double[500]; static { DeepLearning_model_1540691693623_1_Bias_1_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Bias_1_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = -0.6082208509410506; sa[1] = -0.7866761395746675; sa[2] = -0.7902771837144533; sa[3] = -1.078600190576397; sa[4] = -1.1325559100264349; sa[5] = -1.3141680956557276; ...... sa[499] = -1.1605339237438876; } } } // Neuron bias values for RectifierDropout layer class DeepLearning_model_1540691693623_1_Bias_2 implements java.io.Serializable { public static final double[] VALUES = new double[500]; static { DeepLearning_model_1540691693623_1_Bias_2_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Bias_2_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = 0.02034537567535967; sa[1] = 0.2056398687484795; sa[2] = -0.6811208002656562; sa[3] = 0.34906780212313926; sa[4] = 0.1994718812435918; sa[5] = -0.4389162411792007; ...... sa[499] = -0.19825444966679512; } } } // Neuron bias values for RectifierDropout layer class DeepLearning_model_1540691693623_1_Bias_3 implements java.io.Serializable { public static final double[] VALUES = new double[500]; static { DeepLearning_model_1540691693623_1_Bias_3_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Bias_3_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = -0.37334331766490575; sa[1] = -0.2192842820590019; sa[2] = -0.48037506449229245; sa[3] = -0.27829081354485186; sa[4] = -0.5348837641304954; ........ sa[499] = -0.38094144337852304; } } } // Neuron bias values for Softmax layer class DeepLearning_model_1540691693623_1_Bias_4 implements java.io.Serializable { public static final double[] VALUES = new double[2]; static { DeepLearning_model_1540691693623_1_Bias_4_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Bias_4_0 implements java.io.Serializable { static final void fill(double[] sa) { sa[0] = -0.11833918432774448; sa[1] = 0.08029026554573974; } } } class DeepLearning_model_1540691693623_1_Weight_0 implements java.io.Serializable { public static final float[] VALUES = null; } // Neuron weights connecting Input and RectifierDropout layer class DeepLearning_model_1540691693623_1_Weight_1 implements java.io.Serializable { public static final float[] VALUES = new float[14000]; static { DeepLearning_model_1540691693623_1_Weight_1_0.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_1_1.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_1_2.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_1_3.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_1_4.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Weight_1_0 implements java.io.Serializable { static final void fill(float[] sa) { sa[0] = -0.06823527f; sa[1] = 0.01967861f; sa[2] = -0.03716086f; sa[3] = -0.011376046f; sa[4] = -0.019753613f; sa[5] = -0.19310619f; ...... sa[13999] = -0.12823965f; } } } // Neuron weights connecting RectifierDropout and RectifierDropout layer class DeepLearning_model_1540691693623_1_Weight_2 implements java.io.Serializable { public static final float[] VALUES = new float[250000]; static { DeepLearning_model_1540691693623_1_Weight_2_0.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_1.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_2.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_3.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_4.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_5.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_6.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_7.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_8.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_9.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_10.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_11.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_12.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_13.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_14.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_15.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_16.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_17.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_18.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_19.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_20.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_21.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_22.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_23.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_24.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_25.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_26.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_27.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_28.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_29.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_30.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_31.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_32.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_33.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_34.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_35.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_36.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_37.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_38.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_39.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_40.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_41.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_42.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_43.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_44.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_45.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_46.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_47.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_48.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_49.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_50.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_51.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_52.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_53.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_54.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_55.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_56.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_57.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_58.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_59.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_60.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_61.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_62.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_63.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_64.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_65.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_66.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_67.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_68.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_69.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_70.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_71.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_72.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_73.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_74.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_75.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_76.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_77.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_78.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_79.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_80.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_81.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_82.fill(VALUES); DeepLearning_model_1540691693623_1_Weight_2_83.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Weight_2_0 implements java.io.Serializable { static final void fill(float[] sa) { sa[0] = -0.24805546f; sa[1] = -0.06499913f; sa[2] = -0.14989536f; sa[3] = -0.06383444f; sa[4] = 0.14076568f; sa[5] = -0.16730806f; ...... sa[249999] = 0.005610808f; } } } // Neuron weights connecting RectifierDropout and Softmax layer class DeepLearning_model_1540691693623_1_Weight_4 implements java.io.Serializable { public static final float[] VALUES = new float[1000]; static { DeepLearning_model_1540691693623_1_Weight_4_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_Weight_4_0 implements java.io.Serializable { static final void fill(float[] sa) { sa[0] = 0.2789825f; sa[1] = 0.4297707f; sa[2] = -0.28039044f; sa[3] = -0.13956442f; sa[4] = -0.07300115f; sa[5] = 0.13860723f; ........ sa[999] = 0.043791886f; } } } // The class representing training column names class NamesHolder_DeepLearning_model_1540691693623_1 implements java.io.Serializable { public static final String[] VALUES = new String[28]; static { NamesHolder_DeepLearning_model_1540691693623_1_0.fill(VALUES); } static final class NamesHolder_DeepLearning_model_1540691693623_1_0 implements java.io.Serializable { static final void fill(String[] sa) { sa[0] = "features0"; sa[1] = "features1"; sa[2] = "features2"; sa[3] = "features3"; sa[4] = "features4"; sa[5] = "features5"; sa[6] = "features6"; sa[7] = "features7"; sa[8] = "features8"; sa[9] = "features9"; sa[10] = "features10"; sa[11] = "features11"; sa[12] = "features12"; sa[13] = "features13"; sa[14] = "features14"; sa[15] = "features15"; sa[16] = "features16"; sa[17] = "features17"; sa[18] = "features18"; sa[19] = "features19"; sa[20] = "features20"; sa[21] = "features21"; sa[22] = "features22"; sa[23] = "features23"; sa[24] = "features24"; sa[25] = "features25"; sa[26] = "features26"; sa[27] = "features27"; } } } // The class representing column label class DeepLearning_model_1540691693623_1_ColInfo_28 implements java.io.Serializable { public static final String[] VALUES = new String[2]; static { DeepLearning_model_1540691693623_1_ColInfo_28_0.fill(VALUES); } static final class DeepLearning_model_1540691693623_1_ColInfo_28_0 implements java.io.Serializable { static final void fill(String[] sa) { sa[0] = "0"; sa[1] = "1"; } } } |
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