@Test
  public void testBackpropResults() {
    Layer layer = getContainedConfig();
    INDArray col = getContainedCol();

    INDArray expectedWeightGradient =
        Nd4j.create(
            new double[] {-1440., -1440., -1984., -1984., -1440., -1440., -1984., -1984.},
            new int[] {2, 1, 2, 2});
    INDArray expectedBiasGradient =
        Nd4j.create(
            new double[] {-544., -544.},
            new int[] {
              2,
            });
    INDArray expectedEpsilon =
        Nd4j.create(
            new double[] {
              -12., -12., -12., -12., -12., -12., -12., -12., -12., -12., -12.,
              -12., -12., -12., -12., -12., -56., -56., -56., -56., -56., -56.,
              -56., -56., -56., -56., -56., -56., -56., -56., -56., -56., -12.,
              -12., -12., -12., -12., -12., -12., -12., -12., -12., -12., -12.,
              -12., -12., -12., -12., -56., -56., -56., -56., -56., -56., -56.,
              -56., -56., -56., -56., -56., -56., -56., -56., -56.
            },
            new int[] {1, 1, 8, 8});

    org.deeplearning4j.nn.layers.convolution.ConvolutionLayer layer2 =
        (org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) layer;
    layer2.setCol(col);
    Pair<Gradient, INDArray> pair = layer2.backpropGradient(epsilon);

    assertEquals(expectedEpsilon.shape(), pair.getSecond().shape());
    assertEquals(expectedWeightGradient.shape(), pair.getFirst().getGradientFor("W").shape());
    assertEquals(expectedBiasGradient.shape(), pair.getFirst().getGradientFor("b").shape());
    assertEquals(expectedEpsilon, pair.getSecond());
    assertEquals(expectedWeightGradient, pair.getFirst().getGradientFor("W"));
    assertEquals(expectedBiasGradient, pair.getFirst().getGradientFor("b"));
  }
  @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);
  }
  @Test
  public void testPreOutputMethod() {
    Layer layer = getContainedConfig();
    INDArray col = getContainedCol();

    INDArray expectedOutput =
        Nd4j.create(
            new double[] {
              4., 4., 4., 4., 4., 4., 4., 4., 8., 8., 8., 8., 8.,
              8., 8., 8., 4., 4., 4., 4., 4., 4., 4., 4., 8., 8.,
              8., 8., 8., 8., 8., 8.
            },
            new int[] {1, 2, 4, 4});

    org.deeplearning4j.nn.layers.convolution.ConvolutionLayer layer2 =
        (org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) layer;
    layer2.setCol(col);
    INDArray activation = layer2.preOutput(true);

    assertEquals(expectedOutput.shape(), activation.shape());
    assertEquals(expectedOutput, activation);
  }