Examples with CustomOp org.nd4j.linalg.api.ops.CustomOp used on opensource projects

Example 1 with CustomOp

use of org.nd4j.linalg.api.ops.CustomOp in project nd4j by deeplearning4j.

the class LossBinaryXENT method computeGradient.

@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
    if (labels.size(1) != preOutput.size(1)) {
        throw new IllegalArgumentException("Labels array numColumns (size(1) = " + labels.size(1) + ") does not match output layer" + " number of outputs (nOut = " + preOutput.size(1) + ") ");
    }
    INDArray output = activationFn.getActivation(preOutput.dup(), true);
    if (clipEps > 0.0) {
        CustomOp op = DynamicCustomOp.builder("clipbyvalue").addInputs(output).callInplace(true).addFloatingPointArguments(clipEps, 1.0 - clipEps).build();
        Nd4j.getExecutioner().exec(op);
    }
    INDArray numerator = output.sub(labels);
    // output * (1-output)
    INDArray denominator = Nd4j.getExecutioner().execAndReturn(new TimesOneMinus(output));
    INDArray dLda = numerator.divi(denominator);
    if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
        // For *most* activation functions: we don't actually need to mask dL/da in addition to masking dL/dz later
        // but: some, like softmax, require both (due to dL/dz_i being a function of dL/da_j, for i != j)
        // We could add a special case for softmax (activationFn instanceof ActivationSoftmax) but that would be
        // error prone - but buy us a tiny bit of performance
        LossUtil.applyMask(dLda, mask);
    }
    // TODO activation functions with weights
    INDArray grad = activationFn.backprop(preOutput, dLda).getFirst();
    // Weighted loss function
    if (weights != null) {
        if (weights.length() != output.size(1)) {
            throw new IllegalStateException("Weights vector (length " + weights.length() + ") does not match output.size(1)=" + output.size(1));
        }
        grad.muliRowVector(weights);
    }
    if (mask != null) {
        LossUtil.applyMask(grad, mask);
    }
    return grad;
}

Example 2 with CustomOp

use of org.nd4j.linalg.api.ops.CustomOp in project nd4j by deeplearning4j.

the class CustomOpsTests method testNoOp1.

@Test
// it's noop, we dont care anymore
@Ignore
public void testNoOp1() throws Exception {
    val arrayX = Nd4j.create(10, 10);
    val arrayY = Nd4j.create(5, 3);
    arrayX.assign(3.0);
    arrayY.assign(1.0);
    val expX = Nd4j.create(10, 10).assign(3.0);
    val expY = Nd4j.create(5, 3).assign(1.0);
    CustomOp op = DynamicCustomOp.builder("noop").addInputs(arrayX, arrayY).addOutputs(arrayX, arrayY).build();
    Nd4j.getExecutioner().exec(op);
    assertEquals(expX, arrayX);
    assertEquals(expY, arrayY);
}

Example 3 with CustomOp

use of org.nd4j.linalg.api.ops.CustomOp in project nd4j by deeplearning4j.

the class CustomOpsTests method testFloor.

@Test
public void testFloor() throws Exception {
    val arrayX = Nd4j.create(10, 10);
    arrayX.assign(3.0);
    val exp = Nd4j.create(10, 10).assign(3.0);
    CustomOp op = DynamicCustomOp.builder("floor").addInputs(arrayX).addOutputs(arrayX).build();
    Nd4j.getExecutioner().exec(op);
    assertEquals(exp, arrayX);
}

Example 4 with CustomOp

use of org.nd4j.linalg.api.ops.CustomOp in project nd4j by deeplearning4j.

the class CustomOpsTests method testOutputShapes1.

@Test
public void testOutputShapes1() {
    // some random array with +ve numbers
    val array0 = Nd4j.rand('f', 5, 2).addi(1);
    val array1 = array0.dup().addi(5);
    // array1 is always bigger than array0 except at 0,0
    array1.put(0, 0, 0);
    // expected value of maxmerge
    val exp = array1.dup();
    exp.putScalar(0, 0, array0.getDouble(0, 0));
    CustomOp op = DynamicCustomOp.builder("mergemax").addInputs(array0, array1).build();
    val shapes = Nd4j.getExecutioner().calculateOutputShape(op);
    assertEquals(1, shapes.size());
    assertArrayEquals(new int[] { 5, 2 }, shapes.get(0));
}

Example 5 with CustomOp

use of org.nd4j.linalg.api.ops.CustomOp in project nd4j by deeplearning4j.

the class CustomOpsTests method testInplaceOp1.

@Test
public void testInplaceOp1() throws Exception {
    val arrayX = Nd4j.create(10, 10);
    val arrayY = Nd4j.create(10, 10);
    arrayX.assign(4.0);
    arrayY.assign(2.0);
    val exp = Nd4j.create(10, 10).assign(6.0);
    CustomOp op = DynamicCustomOp.builder("add").addInputs(arrayX, arrayY).callInplace(true).build();
    Nd4j.getExecutioner().exec(op);
    assertEquals(exp, arrayX);
}