Java Code Examples for org.nd4j.linalg.factory.Nd4j#zerosLike()
The following examples show how to use
org.nd4j.linalg.factory.Nd4j#zerosLike() .
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Example 1
| Source File: CudaExecutionerTest.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSoftmax1D_1() throws Exception {
INDArray input1T = Nd4j.create(new double[]{ -0.75, 0.58, 0.42, 1.03, -0.61, 0.19, -0.37, -0.40, -1.42, -0.04});
INDArray input1 = Nd4j.create(new double[]{ -0.75, 0.58, 0.42, 1.03, -0.61, 0.19, -0.37, -0.40, -1.42, -0.04});
INDArray input2 = Nd4j.zerosLike(input1);
Nd4j.copy(input1, input2);
INDArray output1 = Nd4j.create(1, 10);
INDArray output1T = Nd4j.create(1, 10);
System.out.println("FA --------------------");
Nd4j.getExecutioner().exec(new OldSoftMax(input1, output1));
Nd4j.getExecutioner().exec(new OldSoftMax(input1T, output1T));
System.out.println("FB --------------------");
System.out.println("Softmax = " + output1);
INDArray output2 = Nd4j.create(1,10);
Nd4j.getExecutioner().exec(new SoftMaxDerivative(input2, output2));
System.out.println("Softmax Derivative = " + output2);
INDArray assertion1 = Nd4j.create(new double[]{0.04, 0.16, 0.14, 0.26, 0.05, 0.11, 0.06, 0.06, 0.02, 0.09});
assertArrayEquals(assertion1.data().asFloat(), output1.data().asFloat(), 0.01f);
assertArrayEquals(assertion1.data().asFloat(), output1T.data().asFloat(), 0.01f);
}
Example 2
| Source File: CudaTransformsTests.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSoftmax1D_1() throws Exception {
INDArray input1T = Nd4j.create(new double[]{ -0.75, 0.58, 0.42, 1.03, -0.61, 0.19, -0.37, -0.40, -1.42, -0.04}).transpose();
INDArray input1 = Nd4j.create(new double[]{ -0.75, 0.58, 0.42, 1.03, -0.61, 0.19, -0.37, -0.40, -1.42, -0.04});
INDArray input2 = Nd4j.zerosLike(input1);
Nd4j.copy(input1, input2);
INDArray output1 = Nd4j.create(1, 10);
INDArray output1T = Nd4j.create(1, 10);
System.out.println("FA --------------------");
Nd4j.getExecutioner().exec(new OldSoftMax(input1, output1));
Nd4j.getExecutioner().exec(new OldSoftMax(input1T, output1T));
System.out.println("FB --------------------");
System.out.println("Softmax = " + output1);
INDArray output2 = Nd4j.create(1,10);
Nd4j.getExecutioner().exec(new SoftMaxDerivative(input2, output2));
System.out.println("Softmax Derivative = " + output2);
INDArray assertion1 = Nd4j.create(new double[]{0.04, 0.16, 0.14, 0.26, 0.05, 0.11, 0.06, 0.06, 0.02, 0.09});
assertArrayEquals(assertion1.data().asFloat(), output1.data().asFloat(), 0.01f);
assertArrayEquals(assertion1.data().asFloat(), output1T.data().asFloat(), 0.01f);
}
Example 3
| Source File: NativeOpExecutionerTest.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSoftmax1D_1() throws Exception {
INDArray input1T = Nd4j.create(new double[]{ -0.75, 0.58, 0.42, 1.03, -0.61, 0.19, -0.37, -0.40, -1.42, -0.04});
INDArray input1 = Nd4j.create(new double[]{ -0.75, 0.58, 0.42, 1.03, -0.61, 0.19, -0.37, -0.40, -1.42, -0.04});
INDArray input2 = Nd4j.zerosLike(input1);
Nd4j.copy(input1, input2);
INDArray output1 = Nd4j.create(1, 10);
INDArray output1T = Nd4j.create(1, 10);
System.out.println("FA --------------------");
Nd4j.getExecutioner().exec(new OldSoftMax(input1, output1));
Nd4j.getExecutioner().exec(new OldSoftMax(input1T, output1T));
System.out.println("FB --------------------");
System.out.println("Softmax = " + output1);
INDArray output2 = Nd4j.create(1,10);
Nd4j.getExecutioner().exec(new SoftMaxDerivative(input2, output2));
System.out.println("Softmax Derivative = " + output2);
INDArray assertion1 = Nd4j.create(new double[]{0.04, 0.16, 0.14, 0.26, 0.05, 0.11, 0.06, 0.06, 0.02, 0.09});
assertArrayEquals(assertion1.data().asFloat(), output1.data().asFloat(), 0.01f);
assertArrayEquals(assertion1.data().asFloat(), output1T.data().asFloat(), 0.01f);
}
Example 4
| Source File: CudaTransformsTests.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSoftmax2D_T() {
INDArray input1 = Nd4j.create(1000).transpose();
INDArray input2 = Nd4j.zerosLike(input1);
Nd4j.copy(input1, input2);
INDArray output1 = Nd4j.create(1, 1000);
Nd4j.getExecutioner().exec(new OldSoftMax(input1, output1));
System.out.println("Softmax = " + output1);
INDArray output2 = Nd4j.create(1,1000);
Nd4j.getExecutioner().exec(new SoftMaxDerivative(input2, output2));
System.out.println("Softmax Derivative = " + output2);
assertEquals(1.0f, output1.sumNumber().floatValue(), 0.01f);
assertEquals(1.0f, output2.sumNumber().floatValue(), 0.01f);
}
Example 5
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testLayerNormOP() {
final INDArray random = Nd4j.rand(DataType.DOUBLE, 10, 4);
final INDArray standardized = random.ulike();
Nd4j.getExecutioner().exec(new Standardize(random, standardized, 1));
final INDArray gain = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray bias = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray res = standardized.mulRowVector(gain).addRowVector(bias);
final INDArray output = Nd4j.zerosLike(res);
Nd4j.getExecutioner().exec(new LayerNorm(standardized, gain, bias, output, true, 1));
assertEquals(res, output);
}
Example 6
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testLayerNormOPNoBias() {
final INDArray random = Nd4j.rand(DataType.DOUBLE, 10, 4);
final INDArray standardized = random.ulike();
Nd4j.getExecutioner().exec(new Standardize(random, standardized, 1));
final INDArray gain = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray res = standardized.mulRowVector(gain);
final INDArray output = Nd4j.zerosLike(res);
Nd4j.getExecutioner().exec(new LayerNorm(standardized, gain, output, true, 1));
assertEquals(res, output);
}
Example 7
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testStandardizeOP() {
final INDArray random = Nd4j.rand(new int[]{10, 4});
final int[] axis = new int[]{1};
final INDArray means = random.mean(axis);
final INDArray std = random.std(false, axis);
final INDArray res = random.subColumnVector(means).divColumnVector(std);
final INDArray output = Nd4j.zerosLike(res);
Nd4j.getExecutioner().exec(new Standardize(random, output, 1));
assertEquals(res, output);
}
Example 8
| Source File: MtcnnUtil.java From mtcnn-java with Apache License 2.0 | 4 votes |
|
/**
* Non Maximum Suppression - greedily selects the boxes with high confidence. Keep the boxes that have overlap area
* below the threshold and discards the others.
*
* original code:
* - https://github.com/kpzhang93/MTCNN_face_detection_alignment/blob/master/code/codes/MTCNNv2/nms.m
* - https://github.com/davidsandberg/facenet/blob/master/src/align/detect_face.py#L687
*
* @param boxes nd array with bounding boxes: [[x1, y1, x2, y2 score]]
* @param threshold NMS threshold - retain overlap <= thresh
* @param nmsType NMS method to apply. Available values ('Min', 'Union')
* @return Returns the NMS result
*/
public static INDArray nonMaxSuppression(INDArray boxes, double threshold, NonMaxSuppressionType nmsType) {
if (boxes.isEmpty()) {
return Nd4j.empty();
}
// TODO Try to prevent following duplications!
INDArray x1 = boxes.get(all(), point(0)).dup();
INDArray y1 = boxes.get(all(), point(1)).dup();
INDArray x2 = boxes.get(all(), point(2)).dup();
INDArray y2 = boxes.get(all(), point(3)).dup();
INDArray s = boxes.get(all(), point(4)).dup();
//area = (x2 - x1 + 1) * (y2 - y1 + 1)
INDArray area = (x2.sub(x1).add(1)).mul(y2.sub(y1).add(1));
// sorted_s = np.argsort(s)
INDArray sortedS = Nd4j.sortWithIndices(s, 0, SORT_ASCENDING)[0];
INDArray pick = Nd4j.zerosLike(s);
int counter = 0;
while (sortedS.size(0) > 0) {
if (sortedS.size(0) == 1) {
pick.put(counter++, sortedS.dup());
break;
}
long lastIndex = sortedS.size(0) - 1;
INDArray i = sortedS.get(point(lastIndex), all()); // last element
INDArray idx = sortedS.get(interval(0, lastIndex), all()).transpose(); // all until last excluding
pick.put(counter++, i.dup());
INDArray xx1 = Transforms.max(x1.get(idx), x1.get(i).getInt(0));
INDArray yy1 = Transforms.max(y1.get(idx), y1.get(i).getInt(0));
INDArray xx2 = Transforms.min(x2.get(idx), x2.get(i).getInt(0));
INDArray yy2 = Transforms.min(y2.get(idx), y2.get(i).getInt(0));
// w = np.maximum(0.0, xx2 - xx1 + 1)
// h = np.maximum(0.0, yy2 - yy1 + 1)
// inter = w * h
INDArray w = Transforms.max(xx2.sub(xx1).add(1), 0.0f);
INDArray h = Transforms.max(yy2.sub(yy1).add(1), 0.0f);
INDArray inter = w.mul(h);
// if method is 'Min':
// o = inter / np.minimum(area[i], area[idx])
// else:
// o = inter / (area[i] + area[idx] - inter)
int areaI = area.get(i).getInt(0);
INDArray o = (nmsType == NonMaxSuppressionType.Min) ?
inter.div(Transforms.min(area.get(idx), areaI)) :
inter.div(area.get(idx).add(areaI).sub(inter));
INDArray oIdx = MtcnnUtil.getIndexWhereVector(o, value -> value <= threshold);
//INDArray oIdx = getIndexWhereVector2(o, Conditions.lessThanOrEqual(threshold));
if (oIdx.isEmpty()) {
break;
}
sortedS = Nd4j.expandDims(sortedS.get(oIdx), 0).transpose();
}
//pick = pick[0:counter]
return (counter == 0) ? Nd4j.empty() : pick.get(interval(0, counter));
}
Example 9
| Source File: Variance.java From nd4j with Apache License 2.0 | 4 votes |
|
@Override
public INDArray noOp() {
return Nd4j.zerosLike(x());
}
Example 10
| Source File: Variance.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public INDArray noOp() {
return Nd4j.zerosLike(x());
}
Example 11
| Source File: KerasBatchNormalization.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
/**
* Set weights for layer.
*
* @param weights Map from parameter name to INDArray.
*/
@Override
public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException {
this.weights = new HashMap<>();
if (center) {
if (weights.containsKey(PARAM_NAME_BETA))
this.weights.put(BatchNormalizationParamInitializer.BETA, weights.get(PARAM_NAME_BETA));
else
throw new InvalidKerasConfigurationException("Parameter " + PARAM_NAME_BETA + " does not exist in weights");
} else {
INDArray dummyBeta = Nd4j.zerosLike(weights.get(PARAM_NAME_BETA));
this.weights.put(BatchNormalizationParamInitializer.BETA, dummyBeta);
}
if (scale) {
if (weights.containsKey(PARAM_NAME_GAMMA))
this.weights.put(BatchNormalizationParamInitializer.GAMMA, weights.get(PARAM_NAME_GAMMA));
else
throw new InvalidKerasConfigurationException(
"Parameter " + PARAM_NAME_GAMMA + " does not exist in weights");
} else {
INDArray dummyGamma = weights.containsKey(PARAM_NAME_GAMMA)
? Nd4j.onesLike(weights.get(PARAM_NAME_GAMMA))
: Nd4j.onesLike(weights.get(PARAM_NAME_BETA));
this.weights.put(BatchNormalizationParamInitializer.GAMMA, dummyGamma);
}
if (weights.containsKey(conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_MEAN()))
this.weights.put(BatchNormalizationParamInitializer.GLOBAL_MEAN, weights.get(conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_MEAN()));
else
throw new InvalidKerasConfigurationException(
"Parameter " + conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_MEAN() + " does not exist in weights");
if (weights.containsKey(conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_VARIANCE()))
this.weights.put(BatchNormalizationParamInitializer.GLOBAL_VAR, weights.get(conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_VARIANCE()));
else
throw new InvalidKerasConfigurationException(
"Parameter " + conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_VARIANCE() + " does not exist in weights");
if (weights.size() > 4) {
Set<String> paramNames = weights.keySet();
paramNames.remove(PARAM_NAME_BETA);
paramNames.remove(PARAM_NAME_GAMMA);
paramNames.remove(conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_MEAN());
paramNames.remove(conf.getLAYER_FIELD_BATCHNORMALIZATION_MOVING_VARIANCE());
String unknownParamNames = paramNames.toString();
log.warn("Attempting to set weights for unknown parameters: "
+ unknownParamNames.substring(1, unknownParamNames.length() - 1));
}
}
