Java PoolingType.SUM属性代码示例

public static INDArray maskedPoolingTimeSeries(PoolingType poolingType, INDArray toReduce, INDArray mask,
                int pnorm) {
    if (toReduce.rank() != 3) {
        throw new IllegalArgumentException("Expect rank 3 array: got " + toReduce.rank());
    }
    if (mask.rank() != 2) {
        throw new IllegalArgumentException("Expect rank 2 array for mask: got " + mask.rank());
    }

    //Sum pooling: easy. Multiply by mask, then sum as normal
    //Average pooling: as above, but do a broadcast element-wise divi by mask.sum(1)
    //Max pooling: set to -inf if mask is 0, then do max as normal

    switch (poolingType) {
        case MAX:
            //TODO This is ugly - replace it with something better... Need something like a Broadcast CAS op
            INDArray negInfMask = Transforms.not(mask);
            BooleanIndexing.replaceWhere(negInfMask, Double.NEGATIVE_INFINITY, Conditions.equals(1.0));

            INDArray withInf = Nd4j.createUninitialized(toReduce.shape());
            Nd4j.getExecutioner().exec(new BroadcastAddOp(toReduce, negInfMask, withInf, 0, 2));
            //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op

            return withInf.max(2);
        case AVG:
        case SUM:
            INDArray masked = Nd4j.createUninitialized(toReduce.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(toReduce, mask, masked, 0, 2));
            INDArray summed = masked.sum(2);
            if (poolingType == PoolingType.SUM) {
                return summed;
            }

            INDArray maskCounts = mask.sum(1);
            summed.diviColumnVector(maskCounts);
            return summed;
        case PNORM:
            //Similar to average and sum pooling: there's no N term here, so we can just set the masked values to 0
            INDArray masked2 = Nd4j.createUninitialized(toReduce.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(toReduce, mask, masked2, 0, 2));

            INDArray abs = Transforms.abs(masked2, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = abs.sum(2);

            return Transforms.pow(pNorm, 1.0 / pnorm);
        default:
            throw new UnsupportedOperationException("Unknown or not supported pooling type: " + poolingType);
    }
}
 

public static INDArray maskedPoolingEpsilonTimeSeries(PoolingType poolingType, INDArray input, INDArray mask,
                INDArray epsilon2d, int pnorm) {

    if (input.rank() != 3) {
        throw new IllegalArgumentException("Expect rank 3 input activation array: got " + input.rank());
    }
    if (mask.rank() != 2) {
        throw new IllegalArgumentException("Expect rank 2 array for mask: got " + mask.rank());
    }
    if (epsilon2d.rank() != 2) {
        throw new IllegalArgumentException("Expected rank 2 array for errors: got " + epsilon2d.rank());
    }

    //Mask: [minibatch, tsLength]
    //Epsilon: [minibatch, vectorSize]

    switch (poolingType) {
        case MAX:
            //TODO This is ugly - replace it with something better... Need something like a Broadcast CAS op
            INDArray negInfMask = Transforms.not(mask);
            BooleanIndexing.replaceWhere(negInfMask, Double.NEGATIVE_INFINITY, Conditions.equals(1.0));

            INDArray withInf = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastAddOp(input, negInfMask, withInf, 0, 2));
            //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op

            INDArray isMax = Nd4j.getExecutioner().execAndReturn(new IsMax(withInf, 2));

            return Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(isMax, epsilon2d, isMax, 0, 1));
        case AVG:
        case SUM:
            //if out = sum(in,dims) then dL/dIn = dL/dOut -> duplicate to each step and mask
            //if out = avg(in,dims) then dL/dIn = 1/N * dL/dOut
            //With masking: N differs for different time series

            INDArray out = Nd4j.createUninitialized(input.shape(), 'f');

            //Broadcast copy op, then divide and mask to 0 as appropriate
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(out, epsilon2d, out, 0, 1));
            Nd4j.getExecutioner().exec(new BroadcastMulOp(out, mask, out, 0, 2));

            if (poolingType == PoolingType.SUM) {
                return out;
            }

            INDArray nEachTimeSeries = mask.sum(1); //[minibatchSize,tsLength] -> [minibatchSize,1]
            Nd4j.getExecutioner().exec(new BroadcastDivOp(out, nEachTimeSeries, out, 0));

            return out;

        case PNORM:
            //Similar to average and sum pooling: there's no N term here, so we can just set the masked values to 0
            INDArray masked2 = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(input, mask, masked2, 0, 2));

            INDArray abs = Transforms.abs(masked2, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = Transforms.pow(abs.sum(2), 1.0 / pnorm);

            INDArray numerator;
            if (pnorm == 2) {
                numerator = input.dup();
            } else {
                INDArray absp2 = Transforms.pow(Transforms.abs(input, true), pnorm - 2, false);
                numerator = input.mul(absp2);
            }

            INDArray denom = Transforms.pow(pNorm, pnorm - 1, false);
            denom.rdivi(epsilon2d);
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(numerator, denom, numerator, 0, 1));
            Nd4j.getExecutioner().exec(new BroadcastMulOp(numerator, mask, numerator, 0, 2)); //Apply mask

            return numerator;
        default:
            throw new UnsupportedOperationException("Unknown or not supported pooling type: " + poolingType);
    }
}
 

public static INDArray maskedPoolingConvolution(PoolingType poolingType, INDArray toReduce, INDArray mask,
                boolean alongHeight, int pnorm) {
    // [minibatch, depth, h=1, w=X] or [minibatch, depth, h=X, w=1] data
    // with a mask array of shape [minibatch, X]

    //If masking along height: broadcast dimensions are [0,2]
    //If masking along width: broadcast dimensions are [0,3]

    int[] dimensions = (alongHeight ? CNN_DIM_MASK_H : CNN_DIM_MASK_W);

    switch (poolingType) {
        case MAX:
            //TODO This is ugly - replace it with something better... Need something like a Broadcast CAS op
            INDArray negInfMask = Transforms.not(mask);
            BooleanIndexing.replaceWhere(negInfMask, Double.NEGATIVE_INFINITY, Conditions.equals(1.0));

            INDArray withInf = Nd4j.createUninitialized(toReduce.shape());
            Nd4j.getExecutioner().exec(new BroadcastAddOp(toReduce, negInfMask, withInf, dimensions));
            //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op

            return withInf.max(2, 3);
        case AVG:
        case SUM:
            INDArray masked = Nd4j.createUninitialized(toReduce.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(toReduce, mask, masked, dimensions));

            INDArray summed = masked.sum(2, 3);
            if (poolingType == PoolingType.SUM) {
                return summed;
            }
            INDArray maskCounts = mask.sum(1);
            summed.diviColumnVector(maskCounts);
            return summed;

        case PNORM:
            //Similar to average and sum pooling: there's no N term here, so we can just set the masked values to 0
            INDArray masked2 = Nd4j.createUninitialized(toReduce.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(toReduce, mask, masked2, dimensions));

            INDArray abs = Transforms.abs(masked2, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = abs.sum(2, 3);

            return Transforms.pow(pNorm, 1.0 / pnorm);
        default:
            throw new UnsupportedOperationException("Unknown or not supported pooling type: " + poolingType);
    }
}
 

public static INDArray maskedPoolingEpsilonCnn(PoolingType poolingType, INDArray input, INDArray mask,
                INDArray epsilon2d, boolean alongHeight, int pnorm) {

    // [minibatch, depth, h=1, w=X] or [minibatch, depth, h=X, w=1] data
    // with a mask array of shape [minibatch, X]

    //If masking along height: broadcast dimensions are [0,2]
    //If masking along width: broadcast dimensions are [0,3]

    int[] dimensions = (alongHeight ? CNN_DIM_MASK_H : CNN_DIM_MASK_W);

    switch (poolingType) {
        case MAX:
            //TODO This is ugly - replace it with something better... Need something like a Broadcast CAS op
            INDArray negInfMask = Transforms.not(mask);
            BooleanIndexing.replaceWhere(negInfMask, Double.NEGATIVE_INFINITY, Conditions.equals(1.0));

            INDArray withInf = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastAddOp(input, negInfMask, withInf, dimensions));
            //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op

            INDArray isMax = Nd4j.getExecutioner().execAndReturn(new IsMax(withInf, 2, 3));

            return Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(isMax, epsilon2d, isMax, 0, 1));
        case AVG:
        case SUM:
            //if out = sum(in,dims) then dL/dIn = dL/dOut -> duplicate to each step and mask
            //if out = avg(in,dims) then dL/dIn = 1/N * dL/dOut
            //With masking: N differs for different time series

            INDArray out = Nd4j.createUninitialized(input.shape(), 'f');

            //Broadcast copy op, then divide and mask to 0 as appropriate
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(out, epsilon2d, out, 0, 1));
            Nd4j.getExecutioner().exec(new BroadcastMulOp(out, mask, out, dimensions));

            if (poolingType == PoolingType.SUM) {
                return out;
            }

            //Note that with CNNs, current design is restricted to [minibatch, depth, 1, W] ot [minibatch, depth, H, 1]
            INDArray nEachTimeSeries = mask.sum(1); //[minibatchSize,tsLength] -> [minibatchSize,1]
            Nd4j.getExecutioner().exec(new BroadcastDivOp(out, nEachTimeSeries, out, 0));

            return out;

        case PNORM:
            //Similar to average and sum pooling: there's no N term here, so we can just set the masked values to 0
            INDArray masked2 = Nd4j.createUninitialized(input.shape());
            Nd4j.getExecutioner().exec(new BroadcastMulOp(input, mask, masked2, dimensions));

            INDArray abs = Transforms.abs(masked2, true);
            Transforms.pow(abs, pnorm, false);
            INDArray pNorm = Transforms.pow(abs.sum(2, 3), 1.0 / pnorm);

            INDArray numerator;
            if (pnorm == 2) {
                numerator = input.dup();
            } else {
                INDArray absp2 = Transforms.pow(Transforms.abs(input, true), pnorm - 2, false);
                numerator = input.mul(absp2);
            }

            INDArray denom = Transforms.pow(pNorm, pnorm - 1, false);
            denom.rdivi(epsilon2d);
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(numerator, denom, numerator, 0, 1));
            Nd4j.getExecutioner().exec(new BroadcastMulOp(numerator, mask, numerator, dimensions)); //Apply mask

            return numerator;
        default:
            throw new UnsupportedOperationException("Unknown or not supported pooling type: " + poolingType);

    }
}
 

@Test
public void testMaskingCnnDim3_SingleExample() {
    //Test masking, where mask is along dimension 3

    int minibatch = 1;
    int depthIn = 2;
    int depthOut = 2;
    int nOut = 2;
    int height = 3;
    int width = 6;

    PoolingType[] poolingTypes =
                    new PoolingType[] {PoolingType.SUM, PoolingType.AVG, PoolingType.MAX, PoolingType.PNORM};

    for (PoolingType pt : poolingTypes) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER)
                        .convolutionMode(ConvolutionMode.Same).seed(12345L).list()
                        .layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(depthOut).kernelSize(height, 2)
                                        .stride(height, 1).activation(Activation.TANH).build())
                        .layer(1, new org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder().poolingType(pt)
                                        .build())
                        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                        .activation(Activation.SOFTMAX).nIn(depthOut).nOut(nOut).build())
                        .pretrain(false).backprop(true).build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});

        //Shape for mask: [minibatch, width]
        INDArray maskArray = Nd4j.create(new double[] {1, 1, 1, 1, 1, 0});

        //Multiply the input by the mask array, to ensure the 0s in the mask correspond to 0s in the input vector
        // as would be the case in practice...
        Nd4j.getExecutioner().exec(new BroadcastMulOp(inToBeMasked, maskArray, inToBeMasked, 0, 3));


        net.setLayerMaskArrays(maskArray, null);

        INDArray outMasked = net.output(inToBeMasked);
        net.clearLayerMaskArrays();

        int numSteps = width - 1;
        INDArray subset = inToBeMasked.get(NDArrayIndex.interval(0, 0, true), NDArrayIndex.all(),
                        NDArrayIndex.all(), NDArrayIndex.interval(0, numSteps));
        assertArrayEquals(new int[] {1, depthIn, height, 5}, subset.shape());

        INDArray outSubset = net.output(subset);
        INDArray outMaskedSubset = outMasked.getRow(0);

        assertEquals(outSubset, outMaskedSubset);

        //Finally: check gradient calc for exceptions
        net.setLayerMaskArrays(maskArray, null);
        net.setInput(inToBeMasked);
        INDArray labels = Nd4j.create(new double[] {0, 1});
        net.setLabels(labels);

        net.computeGradientAndScore();
    }
}
 

@Test
public void testMaskingCnnDim2_SingleExample() {
    //Test masking, where mask is along dimension 2

    int minibatch = 1;
    int depthIn = 2;
    int depthOut = 2;
    int nOut = 2;
    int height = 6;
    int width = 3;

    PoolingType[] poolingTypes =
                    new PoolingType[] {PoolingType.SUM, PoolingType.AVG, PoolingType.MAX, PoolingType.PNORM};

    for (PoolingType pt : poolingTypes) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER)
                        .convolutionMode(ConvolutionMode.Same).seed(12345L).list()
                        .layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(depthOut).kernelSize(2, width)
                                        .stride(1, width).activation(Activation.TANH).build())
                        .layer(1, new org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder().poolingType(pt)
                                        .build())
                        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                        .activation(Activation.SOFTMAX).nIn(depthOut).nOut(nOut).build())
                        .pretrain(false).backprop(true).build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});

        //Shape for mask: [minibatch, width]
        INDArray maskArray = Nd4j.create(new double[] {1, 1, 1, 1, 1, 0});

        //Multiply the input by the mask array, to ensure the 0s in the mask correspond to 0s in the input vector
        // as would be the case in practice...
        Nd4j.getExecutioner().exec(new BroadcastMulOp(inToBeMasked, maskArray, inToBeMasked, 0, 2));


        net.setLayerMaskArrays(maskArray, null);

        INDArray outMasked = net.output(inToBeMasked);
        net.clearLayerMaskArrays();

        int numSteps = height - 1;
        INDArray subset = inToBeMasked.get(NDArrayIndex.interval(0, 0, true), NDArrayIndex.all(),
                        NDArrayIndex.interval(0, numSteps), NDArrayIndex.all());
        assertArrayEquals(new int[] {1, depthIn, 5, width}, subset.shape());

        INDArray outSubset = net.output(subset);
        INDArray outMaskedSubset = outMasked.getRow(0);

        assertEquals(outSubset, outMaskedSubset);

        //Finally: check gradient calc for exceptions
        net.setLayerMaskArrays(maskArray, null);
        net.setInput(inToBeMasked);
        INDArray labels = Nd4j.create(new double[] {0, 1});
        net.setLabels(labels);

        net.computeGradientAndScore();
    }
}
 

@Test
public void testMaskingCnnDim3() {
    //Test masking, where mask is along dimension 3

    int minibatch = 3;
    int depthIn = 3;
    int depthOut = 4;
    int nOut = 5;
    int height = 3;
    int width = 6;

    PoolingType[] poolingTypes =
                    new PoolingType[] {PoolingType.SUM, PoolingType.AVG, PoolingType.MAX, PoolingType.PNORM};

    for (PoolingType pt : poolingTypes) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER)
                        .convolutionMode(ConvolutionMode.Same).seed(12345L).list()
                        .layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(depthOut).kernelSize(height, 2)
                                        .stride(height, 1).activation(Activation.TANH).build())
                        .layer(1, new org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder().poolingType(pt)
                                        .build())
                        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                        .activation(Activation.SOFTMAX).nIn(depthOut).nOut(nOut).build())
                        .pretrain(false).backprop(true).build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});

        //Shape for mask: [minibatch, width]
        INDArray maskArray =
                        Nd4j.create(new double[][] {{1, 1, 1, 1, 1, 1}, {1, 1, 1, 1, 1, 0}, {1, 1, 1, 1, 0, 0}});

        //Multiply the input by the mask array, to ensure the 0s in the mask correspond to 0s in the input vector
        // as would be the case in practice...
        Nd4j.getExecutioner().exec(new BroadcastMulOp(inToBeMasked, maskArray, inToBeMasked, 0, 3));


        net.setLayerMaskArrays(maskArray, null);

        INDArray outMasked = net.output(inToBeMasked);
        net.clearLayerMaskArrays();

        for (int i = 0; i < minibatch; i++) {
            System.out.println(i);
            int numSteps = width - i;
            INDArray subset = inToBeMasked.get(NDArrayIndex.interval(i, i, true), NDArrayIndex.all(),
                            NDArrayIndex.all(), NDArrayIndex.interval(0, numSteps));
            assertArrayEquals(new int[] {1, depthIn, height, width - i}, subset.shape());

            INDArray outSubset = net.output(subset);
            INDArray outMaskedSubset = outMasked.getRow(i);

            assertEquals(outSubset, outMaskedSubset);
        }
    }
}
 

@Test
public void testMaskingCnnDim2() {
    //Test masking, where mask is along dimension 2

    int minibatch = 3;
    int depthIn = 3;
    int depthOut = 4;
    int nOut = 5;
    int height = 5;
    int width = 4;

    PoolingType[] poolingTypes =
                    new PoolingType[] {PoolingType.SUM, PoolingType.AVG, PoolingType.MAX, PoolingType.PNORM};

    for (PoolingType pt : poolingTypes) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER)
                        .convolutionMode(ConvolutionMode.Same).seed(12345L).list()
                        .layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(depthOut).kernelSize(2, width)
                                        .stride(1, width).activation(Activation.TANH).build())
                        .layer(1, new org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder().poolingType(pt)
                                        .build())
                        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                        .activation(Activation.SOFTMAX).nIn(depthOut).nOut(nOut).build())
                        .pretrain(false).backprop(true).build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});

        //Shape for mask: [minibatch, width]
        INDArray maskArray = Nd4j.create(new double[][] {{1, 1, 1, 1, 1}, {1, 1, 1, 1, 0}, {1, 1, 1, 0, 0}});

        //Multiply the input by the mask array, to ensure the 0s in the mask correspond to 0s in the input vector
        // as would be the case in practice...
        Nd4j.getExecutioner().exec(new BroadcastMulOp(inToBeMasked, maskArray, inToBeMasked, 0, 2));


        net.setLayerMaskArrays(maskArray, null);

        INDArray outMasked = net.output(inToBeMasked);
        net.clearLayerMaskArrays();

        for (int i = 0; i < minibatch; i++) {
            System.out.println(i);
            int numSteps = height - i;
            INDArray subset = inToBeMasked.get(NDArrayIndex.interval(i, i, true), NDArrayIndex.all(),
                            NDArrayIndex.interval(0, numSteps), NDArrayIndex.all());
            assertArrayEquals(new int[] {1, depthIn, height - i, width}, subset.shape());

            INDArray outSubset = net.output(subset);
            INDArray outMaskedSubset = outMasked.getRow(i);

            assertEquals(outSubset, outMaskedSubset);
        }
    }
}