Examples with ConvolutionMode org.deeplearning4j.nn.conf.ConvolutionMode used on opensource projects

Example 1 with ConvolutionMode

use of org.deeplearning4j.nn.conf.ConvolutionMode in project deeplearning4j by deeplearning4j.

the class TestConvolutionModes method testStrictTruncateConvolutionModeOutput.

@Test
public void testStrictTruncateConvolutionModeOutput() {
    //Idea: with convolution mode == Truncate, input size shouldn't matter (within the bounds of truncated edge),
    // and edge data shouldn't affect the output
    //Use: 9x9, kernel 3, stride 3, padding 0
    // Should get same output for 10x10 and 11x11...
    Nd4j.getRandom().setSeed(12345);
    int[] minibatches = { 1, 3 };
    int[] inDepths = { 1, 3 };
    int[] inSizes = { 9, 10, 11 };
    for (boolean isSubsampling : new boolean[] { false, true }) {
        for (int minibatch : minibatches) {
            for (int inDepth : inDepths) {
                INDArray origData = Nd4j.rand(new int[] { minibatch, inDepth, 9, 9 });
                for (int inSize : inSizes) {
                    for (ConvolutionMode cm : new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Truncate }) {
                        INDArray inputData = Nd4j.rand(new int[] { minibatch, inDepth, inSize, inSize });
                        inputData.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 9), NDArrayIndex.interval(0, 9)).assign(origData);
                        Layer layer;
                        if (isSubsampling) {
                            layer = new SubsamplingLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).build();
                        } else {
                            layer = new ConvolutionLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).nOut(3).build();
                        }
                        MultiLayerNetwork net = null;
                        try {
                            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).list().layer(0, layer).layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build()).setInputType(InputType.convolutional(inSize, inSize, inDepth)).build();
                            net = new MultiLayerNetwork(conf);
                            net.init();
                            if (inSize > 9 && cm == ConvolutionMode.Strict) {
                                fail("Expected exception");
                            }
                        } catch (DL4JException e) {
                            if (inSize == 9 || cm != ConvolutionMode.Strict) {
                                e.printStackTrace();
                                fail("Unexpected exception");
                            }
                            //Expected exception
                            continue;
                        } catch (Exception e) {
                            e.printStackTrace();
                            fail("Unexpected exception");
                        }
                        INDArray out = net.output(origData);
                        INDArray out2 = net.output(inputData);
                        assertEquals(out, out2);
                    }
                }
            }
        }
    }
}

Example 2 with ConvolutionMode

use of org.deeplearning4j.nn.conf.ConvolutionMode in project deeplearning4j by deeplearning4j.

the class TestConvolutionModes method testGlobalLocalConfig.

@Test
public void testGlobalLocalConfig() {
    for (ConvolutionMode cm : new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Truncate }) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).list().layer(0, new ConvolutionLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(1, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Strict).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(2, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Truncate).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(3, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Same).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(4, new SubsamplingLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(5, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Strict).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(6, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Truncate).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(7, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Same).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(8, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build()).build();
        assertEquals(cm, ((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Strict, ((ConvolutionLayer) conf.getConf(1).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Truncate, ((ConvolutionLayer) conf.getConf(2).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Same, ((ConvolutionLayer) conf.getConf(3).getLayer()).getConvolutionMode());
        assertEquals(cm, ((SubsamplingLayer) conf.getConf(4).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Strict, ((SubsamplingLayer) conf.getConf(5).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Truncate, ((SubsamplingLayer) conf.getConf(6).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Same, ((SubsamplingLayer) conf.getConf(7).getLayer()).getConvolutionMode());
    }
}

Example 3 with ConvolutionMode

use of org.deeplearning4j.nn.conf.ConvolutionMode in project deeplearning4j by deeplearning4j.

the class TestConvolution method testCompareCudnnStandardOutputsVsMode.

@Test
public void testCompareCudnnStandardOutputsVsMode() throws Exception {
    ConvolutionMode[] cm = new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Same };
    for (ConvolutionMode c : cm) {
        for (boolean conv : new boolean[] { true, false }) {
            org.deeplearning4j.nn.conf.layers.Layer l;
            if (conv) {
                l = new ConvolutionLayer.Builder().nOut(4).kernelSize(4, 4).stride(2, 2).build();
            } else {
                l = new SubsamplingLayer.Builder().kernelSize(4, 4).stride(2, 2).build();
            }
            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).regularization(true).l2(0.0005).learningRate(.01).weightInit(WeightInit.XAVIER).convolutionMode(c).list().layer(0, l).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10).activation(Activation.SOFTMAX).build()).setInputType(//See note below
            InputType.convolutionalFlat(28, 28, 1)).backprop(true).pretrain(false).build();
            Nd4j.getRandom().setSeed(12345);
            MultiLayerNetwork net1 = new MultiLayerNetwork(conf);
            net1.init();
            net1.initGradientsView();
            Nd4j.getRandom().setSeed(12345);
            MultiLayerNetwork net2 = new MultiLayerNetwork(conf);
            net2.init();
            net2.initGradientsView();
            Layer layerCudnn = net1.getLayer(0);
            Layer layerStandard = net2.getLayer(0);
            Field f = layerStandard.getClass().getDeclaredField("helper");
            f.setAccessible(true);
            f.set(layerStandard, null);
            if (f.get(layerCudnn) == null)
                throw new RuntimeException();
            if (f.get(layerStandard) != null)
                throw new RuntimeException();
            //(20-4+0)/2 +1 = 9
            INDArray in = Nd4j.rand(new int[] { 1, 1, 20, 20 });
            INDArray outCudnn = layerCudnn.activate(in);
            INDArray outStd = layerStandard.activate(in);
            assertEquals(outStd, outCudnn);
            //Check backprop:
            INDArray epsilon = Nd4j.rand(outStd.shape());
            Pair<Gradient, INDArray> pCudnn = layerCudnn.backpropGradient(epsilon);
            Pair<Gradient, INDArray> pStd = layerStandard.backpropGradient(epsilon);
            System.out.println(Arrays.toString(pStd.getSecond().data().asFloat()));
            System.out.println(Arrays.toString(pCudnn.getSecond().data().asFloat()));
            INDArray epsOutStd = pStd.getSecond();
            INDArray epsOutCudnn = pCudnn.getSecond();
            assertTrue(epsOutStd.equalsWithEps(epsOutCudnn, 1e-4));
            INDArray gradStd = pStd.getFirst().gradient();
            INDArray gradCudnn = pCudnn.getFirst().gradient();
            assertTrue(gradStd.equalsWithEps(gradCudnn, 1e-4));
        }
    }
}

Example 4 with ConvolutionMode

use of org.deeplearning4j.nn.conf.ConvolutionMode in project deeplearning4j by deeplearning4j.

the class KerasLayer method getConvolutionModeFromConfig.

/**
     * Get convolution border mode from Keras layer configuration.
     *
     * @param layerConfig       dictionary containing Keras layer configuration
     * @return
     * @throws InvalidKerasConfigurationException
     */
public static ConvolutionMode getConvolutionModeFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
    Map<String, Object> innerConfig = getInnerLayerConfigFromConfig(layerConfig);
    if (!innerConfig.containsKey(LAYER_FIELD_BORDER_MODE))
        throw new InvalidKerasConfigurationException("Could not determine convolution border mode: no " + LAYER_FIELD_BORDER_MODE + " field found");
    String borderMode = (String) innerConfig.get(LAYER_FIELD_BORDER_MODE);
    ConvolutionMode convolutionMode = null;
    switch(borderMode) {
        /* Keras relies upon the Theano and TensorFlow border mode definitions
             * and operations:
             * - Theano: http://deeplearning.net/software/theano/library/tensor/nnet/conv.html#theano.tensor.nnet.conv.conv2d
             * - TensorFlow: https://www.tensorflow.org/api_docs/python/nn/convolution#conv2d
             */
        case LAYER_BORDER_MODE_SAME:
            /* TensorFlow-only "same" mode is equivalent to DL4J Same mode. */
            convolutionMode = ConvolutionMode.Same;
            break;
        case LAYER_BORDER_MODE_VALID:
        /* TensorFlow and Theano "valid" modes apply filter only
                 * to complete patches within the image borders with no
                 * padding. That is equivalent to DL4J Truncate mode
                 * with no padding.
                 */
        case LAYER_BORDER_MODE_FULL:
            /* Theano-only "full" mode zero pads the image so that
                 * outputs = (inputs + filters + 1) / stride. This should
                 * be equivalent to DL4J Truncate mode with padding
                 * equal to filters-1.
                 * TODO: verify this is correct.
                 */
            convolutionMode = ConvolutionMode.Truncate;
            break;
        default:
            throw new UnsupportedKerasConfigurationException("Unsupported convolution border mode: " + borderMode);
    }
    return convolutionMode;
}

Example 5 with ConvolutionMode

use of org.deeplearning4j.nn.conf.ConvolutionMode in project deeplearning4j by deeplearning4j.

the class TestConvolutionModes method testStrictTruncateConvolutionModeCompGraph.

@Test
public void testStrictTruncateConvolutionModeCompGraph() {
    //Idea: with convolution mode == Truncate, input size shouldn't matter (within the bounds of truncated edge),
    // and edge data shouldn't affect the output
    //Use: 9x9, kernel 3, stride 3, padding 0
    // Should get same output for 10x10 and 11x11...
    Nd4j.getRandom().setSeed(12345);
    int[] minibatches = { 1, 3 };
    int[] inDepths = { 1, 3 };
    int[] inSizes = { 9, 10, 11 };
    for (boolean isSubsampling : new boolean[] { false, true }) {
        for (int minibatch : minibatches) {
            for (int inDepth : inDepths) {
                INDArray origData = Nd4j.rand(new int[] { minibatch, inDepth, 9, 9 });
                for (int inSize : inSizes) {
                    for (ConvolutionMode cm : new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Truncate }) {
                        INDArray inputData = Nd4j.rand(new int[] { minibatch, inDepth, inSize, inSize });
                        inputData.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 9), NDArrayIndex.interval(0, 9)).assign(origData);
                        Layer layer;
                        if (isSubsampling) {
                            layer = new SubsamplingLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).build();
                        } else {
                            layer = new ConvolutionLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).nOut(3).build();
                        }
                        ComputationGraph net = null;
                        try {
                            ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).graphBuilder().addInputs("in").addLayer("0", layer, "in").addLayer("1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build(), "0").setOutputs("1").setInputTypes(InputType.convolutional(inSize, inSize, inDepth)).build();
                            net = new ComputationGraph(conf);
                            net.init();
                            if (inSize > 9 && cm == ConvolutionMode.Strict) {
                                fail("Expected exception");
                            }
                        } catch (DL4JException e) {
                            if (inSize == 9 || cm != ConvolutionMode.Strict) {
                                e.printStackTrace();
                                fail("Unexpected exception");
                            }
                            //Expected exception
                            continue;
                        } catch (Exception e) {
                            e.printStackTrace();
                            fail("Unexpected exception");
                        }
                        INDArray out = net.outputSingle(origData);
                        INDArray out2 = net.outputSingle(inputData);
                        assertEquals(out, out2);
                    }
                }
            }
        }
    }
}