Java Code Examples for org.deeplearning4j.nn.multilayer.MultiLayerNetwork#clearLayerMaskArrays()
The following examples show how to use
org.deeplearning4j.nn.multilayer.MultiLayerNetwork#clearLayerMaskArrays() .
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMaskingRnn() {
int timeSeriesLength = 5;
int nIn = 5;
int layerSize = 4;
int nOut = 2;
int[] minibatchSizes = new int[] {1, 3};
for (int miniBatchSize : minibatchSizes) {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(new NoOp())
.dist(new NormalDistribution(0, 1.0)).seed(12345L).list()
.layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.TANH)
.build())
.layer(1, new org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder()
.poolingType(PoolingType.AVG).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
Random r = new Random(12345L);
INDArray input = Nd4j.rand(new int[] {miniBatchSize, nIn, timeSeriesLength}).subi(0.5);
INDArray mask;
if (miniBatchSize == 1) {
mask = Nd4j.create(new double[] {1, 1, 1, 1, 0}).reshape(1,5);
} else {
mask = Nd4j.create(new double[][] {{1, 1, 1, 1, 1}, {1, 1, 1, 1, 0}, {1, 1, 1, 0, 0}});
}
INDArray labels = Nd4j.zeros(miniBatchSize, nOut);
for (int i = 0; i < miniBatchSize; i++) {
int idx = r.nextInt(nOut);
labels.putScalar(i, idx, 1.0);
}
net.setLayerMaskArrays(mask, null);
INDArray outputMasked = net.output(input);
net.clearLayerMaskArrays();
for (int i = 0; i < miniBatchSize; i++) {
INDArray maskRow = mask.getRow(i);
int tsLength = maskRow.sumNumber().intValue();
INDArray inputSubset = input.get(NDArrayIndex.interval(i, i, true), NDArrayIndex.all(),
NDArrayIndex.interval(0, tsLength));
INDArray outSubset = net.output(inputSubset);
INDArray outputMaskedSubset = outputMasked.getRow(i,true);
assertEquals(outSubset, outputMaskedSubset);
}
}
}
Example 2
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});
//Shape for mask: [minibatch, 1, 1, width]
INDArray maskArray = Nd4j.create(new double[] {1, 1, 1, 1, 1, 0}, new int[]{1,1,1,width});
//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 long[] {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}, new long[]{1,2});
net.setLabels(labels);
net.computeGradientAndScore();
}
}
Example 3
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@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())
.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}, new int[]{1,1,height,1});
//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 long[] {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}, new long[]{1,2});
net.setLabels(labels);
net.computeGradientAndScore();
}
}
Example 4
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@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())
.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}})
.reshape('c', minibatch, 1, 1, width);
//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++) {
int numSteps = width - i;
INDArray subset = inToBeMasked.get(NDArrayIndex.interval(i, i, true), NDArrayIndex.all(),
NDArrayIndex.all(), NDArrayIndex.interval(0, numSteps));
assertArrayEquals(new long[] {1, depthIn, height, width - i}, subset.shape());
INDArray outSubset = net.output(subset);
INDArray outMaskedSubset = outMasked.getRow(i, true);
assertEquals("minibatch: " + i, outSubset, outMaskedSubset);
}
}
}
Example 5
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});
//Shape for mask: [minibatch, 1, height, 1] -> broadcast
INDArray maskArray = Nd4j.create(new double[][] {{1, 1, 1, 1, 1}, {1, 1, 1, 1, 0}, {1, 1, 1, 0, 0}})
.reshape('c', minibatch, 1, height, 1);
//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++) {
int numSteps = height - i;
INDArray subset = inToBeMasked.get(NDArrayIndex.interval(i, i, true), NDArrayIndex.all(),
NDArrayIndex.interval(0, numSteps), NDArrayIndex.all());
assertArrayEquals(new long[] {1, depthIn, height - i, width}, subset.shape());
INDArray outSubset = net.output(subset);
INDArray outMaskedSubset = outMasked.getRow(i, true);
assertEquals("minibatch: " + i, outSubset, outMaskedSubset);
}
}
}
Example 6
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMaskingCnnDim23() {
//Test masking, where mask is along dimension 2 AND 3
//For example, input images of 2 different sizes
int minibatch = 2;
int depthIn = 2;
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, 2)
.stride(1, 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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray inToBeMasked = Nd4j.rand(new int[] {minibatch, depthIn, height, width});
//Second example in minibatch: size [3,2]
inToBeMasked.get(point(1), NDArrayIndex.all(), NDArrayIndex.interval(3,height), NDArrayIndex.all()).assign(0);
inToBeMasked.get(point(1), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(2,width)).assign(0);
//Shape for mask: [minibatch, 1, height, 1] -> broadcast
INDArray maskArray = Nd4j.create(minibatch, 1, height, width);
maskArray.get(point(0), all(), all(), all()).assign(1);
maskArray.get(point(1), all(), interval(0,3), interval(0,2)).assign(1);
net.setLayerMaskArrays(maskArray, null);
INDArray outMasked = net.output(inToBeMasked);
net.clearLayerMaskArrays();
net.setLayerMaskArrays(maskArray, null);
for (int i = 0; i < minibatch; i++) {
INDArray subset;
if(i == 0){
subset = inToBeMasked.get(interval(i, i, true), all(), all(), all());
} else {
subset = inToBeMasked.get(interval(i, i, true), all(), interval(0,3), interval(0,2));
}
net.clear();
net.clearLayerMaskArrays();
INDArray outSubset = net.output(subset);
INDArray outMaskedSubset = outMasked.getRow(i,true);
assertEquals("minibatch: " + i + ", " + pt, outSubset, outMaskedSubset);
}
}
}
