Examples with Activation hex.deeplearning.DeepLearningModel.DeepLearningParameters.Activation used on opensource projects

Example 1 with Activation

use of hex.deeplearning.DeepLearningModel.DeepLearningParameters.Activation in project h2o-3 by h2oai.

the class DeepLearningIrisTest method runFraction.

void runFraction(float fraction) {
    long seed0 = 0xDECAF;
    int num_runs = 0;
    Frame frame = null;
    try {
        frame = parse_test_file(Key.make("iris.hex"), PATH);
        for (int repeat = 0; repeat < 5; ++repeat) {
            // Testing different things
            // Note: Microsoft reference implementation is only for Tanh + MSE.
            // Note: Rectifier and MCE are implemented by H2O.ai (trivial).
            // Note: Initial weight distributions are copied, but what is tested is the stability behavior.
            Activation[] activations = { Activation.Tanh, Activation.Rectifier };
            Loss[] losses = { Loss.Quadratic, Loss.CrossEntropy };
            InitialWeightDistribution[] dists = { InitialWeightDistribution.Normal, InitialWeightDistribution.Uniform, InitialWeightDistribution.UniformAdaptive };
            final long seed = seed0 + repeat;
            Random rng = new Random(seed);
            double[] initial_weight_scales = { 1e-4 + rng.nextDouble() };
            double[] holdout_ratios = { 0.1 + rng.nextDouble() * 0.8 };
            double[] momenta = { rng.nextDouble() * 0.99 };
            int[] hiddens = { 1, 2 + rng.nextInt(50) };
            int[] epochs = { 1, 2 + rng.nextInt(50) };
            double[] rates = { 0.01, 1e-5 + rng.nextDouble() * .1 };
            for (Activation activation : activations) {
                for (Loss loss : losses) {
                    for (InitialWeightDistribution dist : dists) {
                        for (double scale : initial_weight_scales) {
                            for (double holdout_ratio : holdout_ratios) {
                                for (double momentum : momenta) {
                                    for (int hidden : hiddens) {
                                        for (int epoch : epochs) {
                                            for (double rate : rates) {
                                                DeepLearningModel mymodel = null;
                                                Frame trainPredict = null;
                                                Frame testPredict = null;
                                                try {
                                                    num_runs++;
                                                    if (fraction < rng.nextFloat())
                                                        continue;
                                                    Log.info("");
                                                    Log.info("STARTING.");
                                                    Log.info("Running with " + activation.name() + " activation function and " + loss.name() + " loss function.");
                                                    Log.info("Initialization with " + dist.name() + " distribution and " + scale + " scale, holdout ratio " + holdout_ratio);
                                                    Log.info("Using " + hidden + " hidden layer neurons and momentum: " + momentum);
                                                    Log.info("Using seed " + seed);
                                                    Random rand;
                                                    int trial = 0;
                                                    do {
                                                        Log.info("Trial #" + ++trial);
                                                        if (_train != null)
                                                            _train.delete();
                                                        if (_test != null)
                                                            _test.delete();
                                                        rand = RandomUtils.getRNG(seed);
                                                        double[][] rows = new double[(int) frame.numRows()][frame.numCols()];
                                                        String[] names = new String[frame.numCols()];
                                                        for (int c = 0; c < frame.numCols(); c++) {
                                                            names[c] = "ColumnName" + c;
                                                            for (int r = 0; r < frame.numRows(); r++) rows[r][c] = frame.vecs()[c].at(r);
                                                        }
                                                        for (int i = rows.length - 1; i >= 0; i--) {
                                                            int shuffle = rand.nextInt(i + 1);
                                                            double[] row = rows[shuffle];
                                                            rows[shuffle] = rows[i];
                                                            rows[i] = row;
                                                        }
                                                        int limit = (int) (frame.numRows() * holdout_ratio);
                                                        _train = ArrayUtils.frame(names, water.util.ArrayUtils.subarray(rows, 0, limit));
                                                        _test = ArrayUtils.frame(names, water.util.ArrayUtils.subarray(rows, limit, (int) frame.numRows() - limit));
                                                        // Must have all output classes in training
                                                        // data (since that's what the reference
                                                        // implementation has hardcoded).  But count
                                                        // of classes is not known unless we visit
                                                        // all the response data - force that now.
                                                        String respname = _train.lastVecName();
                                                        Vec resp = _train.lastVec().toCategoricalVec();
                                                        _train.remove(respname).remove();
                                                        _train.add(respname, resp);
                                                        DKV.put(_train);
                                                        Vec vresp = _test.lastVec().toCategoricalVec();
                                                        _test.remove(respname).remove();
                                                        _test.add(respname, vresp);
                                                        DKV.put(_test);
                                                    } while (_train.lastVec().cardinality() < 3);
                                                    // use the same seed for the reference implementation
                                                    DeepLearningMLPReference ref = new DeepLearningMLPReference();
                                                    ref.init(activation, RandomUtils.getRNG(seed), holdout_ratio, hidden);
                                                    DeepLearningParameters p = new DeepLearningParameters();
                                                    p._train = _train._key;
                                                    p._response_column = _train.lastVecName();
                                                    assert _train.lastVec().isCategorical();
                                                    p._ignored_columns = null;
                                                    p._seed = seed;
                                                    p._hidden = new int[] { hidden };
                                                    p._adaptive_rate = false;
                                                    p._rho = 0;
                                                    p._epsilon = 0;
                                                    //adapt to (1-m) correction that's done inside (only for constant momentum!)
                                                    p._rate = rate / (1 - momentum);
                                                    p._activation = activation;
                                                    p._max_w2 = Float.POSITIVE_INFINITY;
                                                    p._input_dropout_ratio = 0;
                                                    //do not change - not implemented in reference
                                                    p._rate_annealing = 0;
                                                    p._l1 = 0;
                                                    p._loss = loss;
                                                    p._l2 = 0;
                                                    //reference only supports constant momentum
                                                    p._momentum_stable = momentum;
                                                    //do not change - not implemented in reference
                                                    p._momentum_start = p._momentum_stable;
                                                    //do not change - not implemented in reference
                                                    p._momentum_ramp = 0;
                                                    p._initial_weight_distribution = dist;
                                                    p._initial_weight_scale = scale;
                                                    p._valid = null;
                                                    p._quiet_mode = true;
                                                    //to be the same as reference
                                                    p._fast_mode = false;
                                                    //                            p._fast_mode = true; //to be the same as old NeuralNet code
                                                    //to be the same as reference
                                                    p._nesterov_accelerated_gradient = false;
                                                    //                            p._nesterov_accelerated_gradient = true; //to be the same as old NeuralNet code
                                                    //sync once per period
                                                    p._train_samples_per_iteration = 0;
                                                    p._ignore_const_cols = false;
                                                    p._shuffle_training_data = false;
                                                    //don't stop early -> need to compare against reference, which doesn't stop either
                                                    p._classification_stop = -1;
                                                    //keep just 1 chunk for reproducibility
                                                    p._force_load_balance = false;
                                                    p._overwrite_with_best_model = false;
                                                    p._replicate_training_data = false;
                                                    p._mini_batch_size = 1;
                                                    p._single_node_mode = true;
                                                    p._epochs = 0;
                                                    p._elastic_averaging = false;
                                                    mymodel = new DeepLearning(p).trainModel().get();
                                                    p._epochs = epoch;
                                                    Neurons[] neurons = DeepLearningTask.makeNeuronsForTraining(mymodel.model_info());
                                                    // use the same random weights for the reference implementation
                                                    Neurons l = neurons[1];
                                                    for (int o = 0; o < l._a[0].size(); o++) {
                                                        for (int i = 0; i < l._previous._a[0].size(); i++) {
                                                            //                                System.out.println("initial weight[" + o + "]=" + l._w[o * l._previous._a.length + i]);
                                                            ref._nn.ihWeights[i][o] = l._w.get(o, i);
                                                        }
                                                        ref._nn.hBiases[o] = l._b.get(o);
                                                    //                              System.out.println("initial bias[" + o + "]=" + l._b[o]);
                                                    }
                                                    l = neurons[2];
                                                    for (int o = 0; o < l._a[0].size(); o++) {
                                                        for (int i = 0; i < l._previous._a[0].size(); i++) {
                                                            //                                System.out.println("initial weight[" + o + "]=" + l._w[o * l._previous._a.length + i]);
                                                            ref._nn.hoWeights[i][o] = l._w.get(o, i);
                                                        }
                                                        ref._nn.oBiases[o] = l._b.get(o);
                                                    //                              System.out.println("initial bias[" + o + "]=" + l._b[o]);
                                                    }
                                                    // Train the Reference
                                                    ref.train((int) p._epochs, rate, p._momentum_stable, loss, seed);
                                                    // Train H2O
                                                    mymodel.delete();
                                                    DeepLearning dl = new DeepLearning(p);
                                                    mymodel = dl.trainModel().get();
                                                    Assert.assertTrue(mymodel.model_info().get_processed_total() == epoch * dl.train().numRows());
                                                    /**
                             * Tolerances (should ideally be super tight -> expect the same double/float precision math inside both algos)
                             */
                                                    final double abseps = 1e-6;
                                                    final double releps = 1e-6;
                                                    /**
                             * Compare weights and biases in hidden layer
                             */
                                                    //link the weights to the neurons, for easy access
                                                    neurons = DeepLearningTask.makeNeuronsForTesting(mymodel.model_info());
                                                    l = neurons[1];
                                                    for (int o = 0; o < l._a[0].size(); o++) {
                                                        for (int i = 0; i < l._previous._a[0].size(); i++) {
                                                            double a = ref._nn.ihWeights[i][o];
                                                            double b = l._w.get(o, i);
                                                            compareVal(a, b, abseps, releps);
                                                        //                                System.out.println("weight[" + o + "]=" + b);
                                                        }
                                                        double ba = ref._nn.hBiases[o];
                                                        double bb = l._b.get(o);
                                                        compareVal(ba, bb, abseps, releps);
                                                    }
                                                    Log.info("Weights and biases for hidden layer: PASS");
                                                    /**
                             * Compare weights and biases for output layer
                             */
                                                    l = neurons[2];
                                                    for (int o = 0; o < l._a[0].size(); o++) {
                                                        for (int i = 0; i < l._previous._a[0].size(); i++) {
                                                            double a = ref._nn.hoWeights[i][o];
                                                            double b = l._w.get(o, i);
                                                            compareVal(a, b, abseps, releps);
                                                        }
                                                        double ba = ref._nn.oBiases[o];
                                                        double bb = l._b.get(o);
                                                        compareVal(ba, bb, abseps, releps);
                                                    }
                                                    Log.info("Weights and biases for output layer: PASS");
                                                    /**
                             * Compare predictions
                             * Note: Reference and H2O each do their internal data normalization,
                             * so we must use their "own" test data, which is assumed to be created correctly.
                             */
                                                    // H2O predictions
                                                    //[0] is label, [1]...[4] are the probabilities
                                                    Frame fpreds = mymodel.score(_test);
                                                    try {
                                                        for (int i = 0; i < _test.numRows(); ++i) {
                                                            // Reference predictions
                                                            double[] xValues = new double[neurons[0]._a[0].size()];
                                                            System.arraycopy(ref._testData[i], 0, xValues, 0, xValues.length);
                                                            double[] ref_preds = ref._nn.ComputeOutputs(xValues);
                                                            // find the label
                                                            // do the same as H2O here (compare float values and break ties based on row number)
                                                            double[] preds = new double[ref_preds.length + 1];
                                                            for (int j = 0; j < ref_preds.length; ++j) preds[j + 1] = ref_preds[j];
                                                            preds[0] = GenModel.getPrediction(preds, null, xValues, 0.5);
                                                            // compare predicted label
                                                            Assert.assertTrue(preds[0] == (int) fpreds.vecs()[0].at(i));
                                                        //                                // compare predicted probabilities
                                                        //                                for (int j=0; j<ref_preds.length; ++j) {
                                                        //                                  compareVal((float)(ref_preds[j]), fpreds.vecs()[1+j].at(i), abseps, releps);
                                                        //                                }
                                                        }
                                                    } finally {
                                                        if (fpreds != null)
                                                            fpreds.delete();
                                                    }
                                                    Log.info("Predicted values: PASS");
                                                    /**
                             * Compare (self-reported) scoring
                             */
                                                    final double trainErr = ref._nn.Accuracy(ref._trainData);
                                                    final double testErr = ref._nn.Accuracy(ref._testData);
                                                    trainPredict = mymodel.score(_train);
                                                    testPredict = mymodel.score(_test);
                                                    hex.ModelMetrics mmtrain = hex.ModelMetrics.getFromDKV(mymodel, _train);
                                                    hex.ModelMetrics mmtest = hex.ModelMetrics.getFromDKV(mymodel, _test);
                                                    final double myTrainErr = mmtrain.cm().err();
                                                    final double myTestErr = mmtest.cm().err();
                                                    Log.info("H2O  training error : " + myTrainErr * 100 + "%, test error: " + myTestErr * 100 + "%");
                                                    Log.info("REF  training error : " + trainErr * 100 + "%, test error: " + testErr * 100 + "%");
                                                    compareVal(trainErr, myTrainErr, abseps, releps);
                                                    compareVal(testErr, myTestErr, abseps, releps);
                                                    Log.info("Scoring: PASS");
                                                    // get the actual best error on training data
                                                    float best_err = Float.MAX_VALUE;
                                                    for (ScoringInfo e : mymodel.scoring_history()) {
                                                        DeepLearningScoringInfo err = (DeepLearningScoringInfo) e;
                                                        //multi-class classification
                                                        best_err = Math.min(best_err, (float) (Double.isNaN(err.scored_train._classError) ? best_err : err.scored_train._classError));
                                                    }
                                                    Log.info("Actual best error : " + best_err * 100 + "%.");
                                                    // this is enabled by default
                                                    if (p._overwrite_with_best_model) {
                                                        Frame bestPredict = null;
                                                        try {
                                                            bestPredict = mymodel.score(_train);
                                                            hex.ModelMetrics mmbest = hex.ModelMetrics.getFromDKV(mymodel, _train);
                                                            final double bestErr = mmbest.cm().err();
                                                            Log.info("Best_model's error : " + bestErr * 100 + "%.");
                                                            compareVal(bestErr, best_err, abseps, releps);
                                                        } finally {
                                                            if (bestPredict != null)
                                                                bestPredict.delete();
                                                        }
                                                    }
                                                    Log.info("Parameters combination " + num_runs + ": PASS");
                                                } finally {
                                                    // cleanup
                                                    if (mymodel != null) {
                                                        mymodel.delete();
                                                    }
                                                    if (_train != null)
                                                        _train.delete();
                                                    if (_test != null)
                                                        _test.delete();
                                                    if (trainPredict != null)
                                                        trainPredict.delete();
                                                    if (testPredict != null)
                                                        testPredict.delete();
                                                }
                                            }
                                        }
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }
    } catch (Throwable t) {
        t.printStackTrace();
        throw new RuntimeException(t);
    } finally {
        if (frame != null)
            frame.delete();
    }
}