Examples with TensorList com.simiacryptus.mindseye.lang.TensorList used on opensource projects

Example 96 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class LinearActivationLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result in0 = inObj[0];
    final TensorList inData = in0.getData();
    in0.addRef();
    inData.addRef();
    final int itemCnt = inData.length();
    final double scale = weights.get(0);
    final double bias = weights.get(1);
    weights.addRef();
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = inData.get(dataIndex);
        @Nullable Tensor map = input.map(v -> scale * v + bias);
        input.freeRef();
        return map;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            IntStream.range(0, delta.length()).forEach(dataIndex -> {
                @Nullable Tensor deltaT = delta.get(dataIndex);
                @Nullable Tensor inputT = inData.get(dataIndex);
                @Nullable final double[] deltaData = deltaT.getData();
                @Nullable final double[] inputData = inputT.getData();
                @Nonnull final Tensor weightDelta = new Tensor(weights.getDimensions());
                for (int i = 0; i < deltaData.length; i++) {
                    weightDelta.add(0, deltaData[i] * inputData[inputData.length == 1 ? 0 : i]);
                    weightDelta.add(1, deltaData[i]);
                }
                buffer.get(LinearActivationLayer.this, weights.getData()).addInPlace(weightDelta.getData()).freeRef();
                inputT.freeRef();
                deltaT.freeRef();
                weightDelta.freeRef();
            });
        }
        if (in0.isAlive()) {
            @Nonnull final TensorList tensorList = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                @Nullable Tensor tensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = tensor.getData();
                @Nonnull final Tensor passback = new Tensor(inData.getDimensions());
                for (int i = 0; i < passback.length(); i++) {
                    passback.set(i, deltaData[i] * weights.getData()[0]);
                }
                tensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            in0.accumulate(buffer, tensorList);
        }
    }) {

        @Override
        public boolean isAlive() {
            return in0.isAlive() || !isFrozen();
        }

        @Override
        protected void _free() {
            weights.freeRef();
            inData.freeRef();
            in0.freeRef();
        }
    };
}

Example 97 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class LoggingWrapperLayer method eval.

@Override
public Result eval(@Nonnull final Result... inObj) {
    final Result[] wrappedInput = IntStream.range(0, inObj.length).mapToObj(i -> {
        final Result inputToWrap = inObj[i];
        inputToWrap.addRef();
        return new Result(inputToWrap.getData(), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
            @Nonnull final String formatted = data.stream().map(x -> {
                String str = x.prettyPrint();
                x.freeRef();
                return str;
            }).reduce((a, b) -> a + "\n" + b).get();
            log.info(String.format("Feedback Output %s for layer %s: \n\t%s", i, getInner().getName(), formatted.replaceAll("\n", "\n\t")));
            data.addRef();
            inputToWrap.accumulate(buffer, data);
        }) {

            @Override
            protected void _free() {
                inputToWrap.freeRef();
            }

            @Override
            public boolean isAlive() {
                return inputToWrap.isAlive();
            }
        };
    }).toArray(i -> new Result[i]);
    for (int i = 0; i < inObj.length; i++) {
        final TensorList tensorList = inObj[i].getData();
        @Nonnull final String formatted = tensorList.stream().map(x -> {
            String str = x.prettyPrint();
            x.freeRef();
            return str;
        }).reduce((a, b) -> a + "\n" + b).get();
        log.info(String.format("Input %s for layer %s: \n\t%s", i, getInner().getName(), formatted.replaceAll("\n", "\n\t")));
    }
    @Nullable final Result output = getInner().eval(wrappedInput);
    Arrays.stream(wrappedInput).forEach(ReferenceCounting::freeRef);
    {
        final TensorList tensorList = output.getData();
        @Nonnull final String formatted = tensorList.stream().map(x -> {
            String str = x.prettyPrint();
            x.freeRef();
            return str;
        }).reduce((a, b) -> a + "\n" + b).get();
        log.info(String.format("Output for layer %s: \n\t%s", getInner().getName(), formatted.replaceAll("\n", "\n\t")));
    }
    return new Result(output.getData(), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        @Nonnull final String formatted = data.stream().map(x -> {
            String str = x.prettyPrint();
            x.freeRef();
            return str;
        }).reduce((a, b) -> a + "\n" + b).get();
        log.info(String.format("Feedback Input for layer %s: \n\t%s", getInner().getName(), formatted.replaceAll("\n", "\n\t")));
        data.addRef();
        output.accumulate(buffer, data);
    }) {

        @Override
        protected void _free() {
            output.freeRef();
        }

        @Override
        public boolean isAlive() {
            return output.isAlive();
        }
    };
}

Example 98 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class MaxDropoutNoiseLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result in0 = inObj[0];
    final TensorList data0 = in0.getData();
    final int itemCnt = data0.length();
    in0.addRef();
    data0.addRef();
    final Tensor[] mask = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = data0.get(dataIndex);
        @Nullable final Tensor output = input.map(x -> 0);
        final List<List<Coordinate>> cells = getCellMap_cached.apply(new IntArray(output.getDimensions()));
        cells.forEach(cell -> {
            output.set(cell.stream().max(Comparator.comparingDouble(c -> input.get(c))).get(), 1);
        });
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]);
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        Tensor inputData = data0.get(dataIndex);
        @Nullable final double[] input = inputData.getData();
        @Nullable final double[] maskT = mask[dataIndex].getData();
        @Nonnull final Tensor output = new Tensor(inputData.getDimensions());
        @Nullable final double[] outputData = output.getData();
        for (int i = 0; i < outputData.length; i++) {
            outputData[i] = input[i] * maskT[i];
        }
        inputData.freeRef();
        return output;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (in0.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nonnull final int[] dims = data0.getDimensions();
                @Nullable final double[] maskData = mask[dataIndex].getData();
                @Nonnull final Tensor passback = new Tensor(dims);
                for (int i = 0; i < passback.length(); i++) {
                    passback.set(i, maskData[i] * deltaData[i]);
                }
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            in0.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            in0.freeRef();
            data0.freeRef();
            Arrays.stream(mask).forEach(ReferenceCounting::freeRef);
        }

        @Override
        public boolean isAlive() {
            return in0.isAlive() || !isFrozen();
        }
    };
}

Example 99 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class SoftmaxActivationLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    final int itemCnt = inObj[0].getData().length();
    @Nonnull final double[] sumA = new double[itemCnt];
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    @Nonnull final Tensor[] expA = new Tensor[itemCnt];
    final Tensor[] outputA = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = inObj[0].getData().get(dataIndex);
        assert 1 < input.length() : "input.length() = " + input.length();
        @Nullable final Tensor exp;
        final DoubleSummaryStatistics summaryStatistics = DoubleStream.of(input.getData()).filter(x -> Double.isFinite(x)).summaryStatistics();
        final double max = summaryStatistics.getMax();
        // final double min = summaryStatistics.getMin();
        exp = input.map(x -> {
            double xx = Math.exp(x - max);
            return Double.isFinite(xx) ? xx : 0;
        });
        input.freeRef();
        assert Arrays.stream(exp.getData()).allMatch(Double::isFinite);
        assert Arrays.stream(exp.getData()).allMatch(v -> v >= 0);
        // assert exp.sum() > 0;
        final double sum = 0 < exp.sum() ? exp.sum() : 1;
        assert Double.isFinite(sum);
        expA[dataIndex] = exp;
        sumA[dataIndex] = sum;
        @Nullable Tensor result = exp.map(x -> x / sum);
        return result;
    }).toArray(i -> new Tensor[i]);
    assert Arrays.stream(outputA).flatMapToDouble(x -> Arrays.stream(x.getData())).allMatch(v -> Double.isFinite(v));
    return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (inObj[0].isAlive()) {
            final Tensor[] passbackA = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
                Tensor deltaTensor = data.get(dataIndex);
                @Nullable final double[] delta = deltaTensor.getData();
                @Nullable final double[] expdata = expA[dataIndex].getData();
                @Nonnull final Tensor passback = new Tensor(data.getDimensions());
                final int dim = expdata.length;
                double dot = 0;
                for (int i = 0; i < expdata.length; i++) {
                    dot += delta[i] * expdata[i];
                }
                final double sum = sumA[dataIndex];
                for (int i = 0; i < dim; i++) {
                    double value = 0;
                    value = (sum * delta[i] - dot) * expdata[i] / (sum * sum);
                    passback.set(i, value);
                }
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]);
            assert Arrays.stream(passbackA).flatMapToDouble(x -> Arrays.stream(x.getData())).allMatch(v -> Double.isFinite(v));
            @Nonnull TensorArray tensorArray = TensorArray.wrap(passbackA);
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            Arrays.stream(expA).forEach(ReferenceCounting::freeRef);
            Arrays.stream(inObj).forEach(ReferenceCounting::freeRef);
        }

        @Override
        public boolean isAlive() {
            return inObj[0].isAlive();
        }
    };
}

Example 100 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class StaticScalarLossLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    if (1 != inObj.length)
        throw new IllegalArgumentException();
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    // if (inObj[0].getData().length() != 1) throw new IllegalArgumentException();
    final Result in0 = inObj[0];
    TensorList indata = in0.getData();
    indata.addRef();
    return new Result(TensorArray.wrap(IntStream.range(0, indata.length()).parallel().mapToObj(dataIndex -> {
        @Nullable final Tensor a = indata.get(dataIndex);
        final double diff = Math.abs(a.get(0) - getTarget());
        a.freeRef();
        return new Tensor(new double[] { diff }, 1);
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (in0.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, data.length()).parallel().mapToObj(dataIndex -> {
                @Nullable final Tensor a = indata.get(dataIndex);
                Tensor tensor = data.get(dataIndex);
                final double deriv = tensor.get(0) * (a.get(0) - getTarget() < 0 ? -1 : 1);
                tensor.freeRef();
                a.freeRef();
                return new Tensor(new double[] { deriv }, 1);
            }).toArray(i -> new Tensor[i]));
            in0.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            indata.freeRef();
            Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
        }

        @Override
        public boolean isAlive() {
            return in0.isAlive();
        }
    };
}