Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#tensorsAlongDimension()
The following examples show how to use
org.nd4j.linalg.api.ndarray.INDArray#tensorsAlongDimension() .
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Example 1
| Source File: ShapeTestsC.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSixteenZeroOne() {
INDArray baseArr = Nd4j.linspace(1, 16, 16, DataType.DOUBLE).reshape(2, 2, 2, 2);
assertEquals(4, baseArr.tensorsAlongDimension(0, 1));
INDArray columnVectorFirst = Nd4j.create(new double[][] {{1, 5}, {9, 13}});
INDArray columnVectorSecond = Nd4j.create(new double[][] {{2, 6}, {10, 14}});
INDArray columnVectorThird = Nd4j.create(new double[][] {{3, 7}, {11, 15}});
INDArray columnVectorFourth = Nd4j.create(new double[][] {{4, 8}, {12, 16}});
INDArray[] assertions =
new INDArray[] {columnVectorFirst, columnVectorSecond, columnVectorThird, columnVectorFourth};
for (int i = 0; i < baseArr.tensorsAlongDimension(0, 1); i++) {
INDArray test = baseArr.tensorAlongDimension(i, 0, 1);
assertEquals("Wrong at index " + i, assertions[i], test);
}
}
Example 2
| Source File: ShapeTestsC.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSixteenSecondDim() {
INDArray baseArr = Nd4j.linspace(1, 16, 16, DataType.DOUBLE).reshape(2, 2, 2, 2);
INDArray[] assertions = new INDArray[] {Nd4j.create(new double[] {1, 3}), Nd4j.create(new double[] {2, 4}),
Nd4j.create(new double[] {5, 7}), Nd4j.create(new double[] {6, 8}),
Nd4j.create(new double[] {9, 11}), Nd4j.create(new double[] {10, 12}),
Nd4j.create(new double[] {13, 15}), Nd4j.create(new double[] {14, 16}),
};
for (int i = 0; i < baseArr.tensorsAlongDimension(2); i++) {
INDArray arr = baseArr.tensorAlongDimension(i, 2);
assertEquals("Failed at index " + i, assertions[i], arr);
}
}
Example 3
| Source File: ShapeTestsC.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSixteenFirstDim() {
INDArray baseArr = Nd4j.linspace(1, 16, 16, DataType.DOUBLE).reshape(2, 2, 2, 2);
INDArray[] assertions = new INDArray[] {Nd4j.create(new double[] {1, 5}), Nd4j.create(new double[] {2, 6}),
Nd4j.create(new double[] {3, 7}), Nd4j.create(new double[] {4, 8}),
Nd4j.create(new double[] {9, 13}), Nd4j.create(new double[] {10, 14}),
Nd4j.create(new double[] {11, 15}), Nd4j.create(new double[] {12, 16}),
};
for (int i = 0; i < baseArr.tensorsAlongDimension(1); i++) {
INDArray arr = baseArr.tensorAlongDimension(i, 1);
assertEquals("Failed at index " + i, assertions[i], arr);
}
}
Example 4
| Source File: ShapeTests.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSixteenZeroOne() {
INDArray baseArr = Nd4j.linspace(1, 16, 16, DataType.DOUBLE).reshape(2, 2, 2, 2);
assertEquals(4, baseArr.tensorsAlongDimension(0, 1));
INDArray columnVectorFirst = Nd4j.create(new double[][] {{1, 3}, {2, 4}});
INDArray columnVectorSecond = Nd4j.create(new double[][] {{9, 11}, {10, 12}});
INDArray columnVectorThird = Nd4j.create(new double[][] {{5, 7}, {6, 8}});
INDArray columnVectorFourth = Nd4j.create(new double[][] {{13, 15}, {14, 16}});
INDArray[] assertions =
new INDArray[] {columnVectorFirst, columnVectorSecond, columnVectorThird, columnVectorFourth};
for (int i = 0; i < baseArr.tensorsAlongDimension(0, 1); i++) {
INDArray test = baseArr.tensorAlongDimension(i, 0, 1);
assertEquals("Wrong at index " + i, assertions[i], test);
}
}
Example 5
| Source File: ShapeTests.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSixteenSecondDim() {
INDArray baseArr = Nd4j.linspace(1, 16, 16, DataType.DOUBLE).reshape(2, 2, 2, 2);
INDArray[] assertions = new INDArray[] {Nd4j.create(new double[] {1, 5}), Nd4j.create(new double[] {9, 13}),
Nd4j.create(new double[] {3, 7}), Nd4j.create(new double[] {11, 15}),
Nd4j.create(new double[] {2, 6}), Nd4j.create(new double[] {10, 14}),
Nd4j.create(new double[] {4, 8}), Nd4j.create(new double[] {12, 16}),
};
for (int i = 0; i < baseArr.tensorsAlongDimension(2); i++) {
INDArray arr = baseArr.tensorAlongDimension(i, 2);
assertEquals("Failed at index " + i, assertions[i], arr);
}
}
Example 6
| Source File: ShapeTests.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSixteenFirstDim() {
INDArray baseArr = Nd4j.linspace(1, 16, 16, DataType.DOUBLE).reshape(2, 2, 2, 2);
INDArray[] assertions = new INDArray[] {Nd4j.create(new double[] {1, 3}), Nd4j.create(new double[] {9, 11}),
Nd4j.create(new double[] {5, 7}), Nd4j.create(new double[] {13, 15}),
Nd4j.create(new double[] {2, 4}), Nd4j.create(new double[] {10, 12}),
Nd4j.create(new double[] {6, 8}), Nd4j.create(new double[] {14, 16}),
};
for (int i = 0; i < baseArr.tensorsAlongDimension(1); i++) {
INDArray arr = baseArr.tensorAlongDimension(i, 1);
assertEquals("Failed at index " + i, assertions[i], arr);
}
}
Example 7
| Source File: OpExecutionerTestsC.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testTad() {
INDArray arr = Nd4j.linspace(1, 12, 12, DataType.DOUBLE).reshape(2, 3, 2);
for (int i = 0; i < arr.tensorsAlongDimension(0); i++) {
// System.out.println(arr.tensorAlongDimension(i, 0));
arr.tensorAlongDimension(i, 0);
}
}
Example 8
| Source File: LoneTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testFlattenedView() {
int rows = 8;
int cols = 8;
int dim2 = 4;
int length = rows * cols;
int length3d = rows * cols * dim2;
INDArray first = Nd4j.linspace(1, length, length).reshape('c', rows, cols);
INDArray second = Nd4j.create(DataType.DOUBLE, new long[]{rows, cols}, 'f').assign(first);
INDArray third = Nd4j.linspace(1, length3d, length3d).reshape('c', rows, cols, dim2);
first.addi(0.1);
second.addi(0.2);
third.addi(0.3);
first = first.get(NDArrayIndex.interval(4, 8), NDArrayIndex.interval(0, 2, 8));
for (int i = 0; i < first.tensorsAlongDimension(0); i++) {
// System.out.println(first.tensorAlongDimension(i, 0));
first.tensorAlongDimension(i, 0);
}
for (int i = 0; i < first.tensorsAlongDimension(1); i++) {
// System.out.println(first.tensorAlongDimension(i, 1));
first.tensorAlongDimension(i, 1);
}
second = second.get(NDArrayIndex.interval(3, 7), NDArrayIndex.all());
third = third.permute(0, 2, 1);
INDArray cAssertion = Nd4j.create(new double[]{33.10, 35.10, 37.10, 39.10, 41.10, 43.10, 45.10, 47.10, 49.10,
51.10, 53.10, 55.10, 57.10, 59.10, 61.10, 63.10});
INDArray fAssertion = Nd4j.create(new double[]{33.10, 41.10, 49.10, 57.10, 35.10, 43.10, 51.10, 59.10, 37.10,
45.10, 53.10, 61.10, 39.10, 47.10, 55.10, 63.10});
assertEquals(cAssertion, Nd4j.toFlattened('c', first));
assertEquals(fAssertion, Nd4j.toFlattened('f', first));
}
Example 9
| Source File: DataSetUtil.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
public static INDArray tailor4d2d(@NonNull INDArray data) {
long instances = data.size(0);
long channels = data.size(1);
long height = data.size(2);
long width = data.size(3);
INDArray in2d = Nd4j.create(channels, height * width * instances);
long tads = data.tensorsAlongDimension(3, 2, 0);
for (int i = 0; i < tads; i++) {
INDArray thisTAD = data.tensorAlongDimension(i, 3, 2, 0);
in2d.putRow(i, Nd4j.toFlattened(thisTAD));
}
return in2d.transposei();
}
Example 10
| Source File: KerasConvolution.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
/**
* Return processed parameter values obtained from Keras convolutional layers.
*
* @param kerasParamValue INDArray containing raw Keras weights to be processed
* @return Processed weights, according to which backend was used.
* @throws InvalidKerasConfigurationException Invalid Keras configuration exception.
*/
public INDArray getConvParameterValues(INDArray kerasParamValue) throws InvalidKerasConfigurationException {
INDArray paramValue;
switch (this.getDimOrder()) {
case TENSORFLOW:
if (kerasParamValue.rank() == 5)
// CNN 3D case
paramValue = kerasParamValue.permute(4, 3, 0, 1, 2);
else
/* TensorFlow convolutional weights: # rows, # cols, # inputs, # outputs */
paramValue = kerasParamValue.permute(3, 2, 0, 1);
break;
case THEANO:
/* Theano convolutional weights match DL4J: # outputs, # inputs, # rows, # cols
* Theano's default behavior is to rotate filters by 180 degree before application.
*/
paramValue = kerasParamValue.dup();
for (int i = 0; i < paramValue.tensorsAlongDimension(2, 3); i++) {
//dup required since we only want data from the view not the whole array
INDArray copyFilter = paramValue.tensorAlongDimension(i, 2, 3).dup();
double[] flattenedFilter = copyFilter.ravel().data().asDouble();
ArrayUtils.reverse(flattenedFilter);
INDArray newFilter = Nd4j.create(flattenedFilter, copyFilter.shape());
//manipulating weights in place to save memory
INDArray inPlaceFilter = paramValue.tensorAlongDimension(i, 2, 3);
inPlaceFilter.muli(0).addi(newFilter.castTo(inPlaceFilter.dataType()));
}
break;
default:
throw new InvalidKerasConfigurationException("Unknown keras backend " + this.getDimOrder());
}
return paramValue;
}
Example 11
| Source File: ConcatTestsC.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testConcat3d() {
INDArray first = Nd4j.linspace(1, 24, 24, Nd4j.dataType()).reshape('c', 2, 3, 4);
INDArray second = Nd4j.linspace(24, 36, 12, Nd4j.dataType()).reshape('c', 1, 3, 4);
INDArray third = Nd4j.linspace(36, 48, 12, Nd4j.dataType()).reshape('c', 1, 3, 4);
//ConcatV2, dim 0
INDArray exp = Nd4j.create(2 + 1 + 1, 3, 4);
exp.put(new INDArrayIndex[] {NDArrayIndex.interval(0, 2), NDArrayIndex.all(), NDArrayIndex.all()}, first);
exp.put(new INDArrayIndex[] {NDArrayIndex.point(2), NDArrayIndex.all(), NDArrayIndex.all()}, second);
exp.put(new INDArrayIndex[] {NDArrayIndex.point(3), NDArrayIndex.all(), NDArrayIndex.all()}, third);
INDArray concat0 = Nd4j.concat(0, first, second, third);
assertEquals(exp, concat0);
//ConcatV2, dim 1
second = Nd4j.linspace(24, 32, 8, Nd4j.dataType()).reshape('c', 2, 1, 4);
for (int i = 0; i < second.tensorsAlongDimension(1); i++) {
INDArray secondTad = second.tensorAlongDimension(i, 1);
// System.out.println(second.tensorAlongDimension(i, 1));
}
third = Nd4j.linspace(32, 48, 16).reshape('c', 2, 2, 4);
exp = Nd4j.create(2, 3 + 1 + 2, 4);
exp.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.interval(0, 3), NDArrayIndex.all()}, first);
exp.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.point(3), NDArrayIndex.all()}, second);
exp.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.interval(4, 6), NDArrayIndex.all()}, third);
INDArray concat1 = Nd4j.concat(1, first, second, third);
assertEquals(exp, concat1);
//ConcatV2, dim 2
second = Nd4j.linspace(24, 36, 12).reshape('c', 2, 3, 2);
third = Nd4j.linspace(36, 42, 6).reshape('c', 2, 3, 1);
exp = Nd4j.create(2, 3, 4 + 2 + 1);
exp.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4)}, first);
exp.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(4, 6)}, second);
exp.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(6)}, third);
INDArray concat2 = Nd4j.concat(2, first, second, third);
assertEquals(exp, concat2);
}
