// note precision is off on this test but the numbers are close
// investigation in a future release should determine how to resolve
@Test
public void testCalculateDeltaContained() {
Layer layer = getContainedConfig();
INDArray input = getContainedData();
INDArray col = getContainedCol();
INDArray epsilon = Nd4j.ones(1, 2, 4, 4);
INDArray expectedOutput =
Nd4j.create(
new double[] {
0.02036651, 0.02036651, 0.02036651, 0.02036651, 0.00039383,
0.00039383, 0.00039383, 0.00039383, 0.02036651, 0.02036651,
0.02036651, 0.02036651, 0.00039383, 0.00039383, 0.00039383,
0.00039383, 0.02036651, 0.02036651, 0.02036651, 0.02036651,
0.00039383, 0.00039383, 0.00039383, 0.00039383, 0.02036651,
0.02036651, 0.02036651, 0.02036651, 0.00039383, 0.00039383,
0.00039383, 0.00039383
},
new int[] {1, 2, 4, 4});
layer.setInput(input);
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer layer2 =
(org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) layer;
layer2.setCol(col);
INDArray delta = layer2.calculateDelta(epsilon);
assertArrayEquals(expectedOutput.shape(), delta.shape());
assertEquals(expectedOutput, delta);
}
@Test
public void testCalculateDelta() {
Layer layer = getContainedConfig();
INDArray col = getContainedCol();
INDArray expectedOutput =
Nd4j.create(
new double[] {
-12., -12., -12., -12., -56., -56., -56., -56., -12., -12., -12.,
-12., -56., -56., -56., -56., -12., -12., -12., -12., -56., -56.,
-56., -56., -12., -12., -12., -12., -56., -56., -56., -56.
},
new int[] {1, 2, 4, 4});
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer layer2 =
(org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) layer;
layer2.setCol(col);
INDArray delta = layer2.calculateDelta(epsilon);
assertEquals(expectedOutput.shape(), delta.shape());
assertEquals(expectedOutput, delta);
}
