@Test
public void testPreOutputMethodContained() {
Layer layer = getContainedConfig();
INDArray col = getContainedCol();
INDArray expectedOutput =
Nd4j.create(
new double[] {
4., 4., 4., 4., 8., 8., 8., 8., 4., 4., 4., 4., 8., 8., 8., 8., 4., 4., 4., 4., 8.,
8., 8., 8., 4., 4., 4., 4., 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);
assertArrayEquals(expectedOutput.shape(), activation.shape());
assertEquals(expectedOutput, activation);
}
public static void testAccuracy() {
double[][][][] data = {
{
{
{1.0, 2.0, 3.0},
{4.0, 5.0, 6.0},
{7.0, 8.0, 9.0}
}
}
};
double[] flat = ArrayUtil.flattenDoubleArray(data);
int[] shape = {1, 1, 3, 3};
INDArray input = Nd4j.create(flat, shape, 'c');
TestCase testCase = new TestCase(1, 1, 2, 2, 1, 1, 0, 0, 3, 3);
ConvolutionLayer convolutionLayerBuilder =
new ConvolutionLayer.Builder(testCase.kW, testCase.kH)
.nIn(testCase.nInputPlane)
.stride(testCase.dW, testCase.dH)
.padding(testCase.padW, testCase.padH)
.nOut(testCase.nOutputPlane)
.build();
MultiLayerConfiguration.Builder builder =
new NeuralNetConfiguration.Builder().list().layer(0, convolutionLayerBuilder);
MultiLayerConfiguration conf = builder.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setInput(input);
model.getLayer(0).setInput(input);
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer convolutionLayer =
(org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) model.getLayer(0);
System.out.println(convolutionLayer.params());
System.out.println(convolutionLayer.preOutput(false));
}
public static void testForward() {
for (TestCase testCase : allTestCases) {
try (BufferedWriter writer =
new BufferedWriter(new FileWriter(new File("dl4jPerformance.csv"), true))) {
ConvolutionLayer convolutionLayerBuilder =
new ConvolutionLayer.Builder(testCase.kW, testCase.kH)
.nIn(testCase.nInputPlane)
.stride(testCase.dW, testCase.dH)
.padding(testCase.padW, testCase.padH)
.nOut(testCase.nOutputPlane)
.build();
MultiLayerConfiguration.Builder builder =
new NeuralNetConfiguration.Builder().list().layer(0, convolutionLayerBuilder);
MultiLayerConfiguration conf = builder.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
INDArray input =
Nd4j.rand(
seed, batchSize, testCase.nInputPlane, testCase.inputWidth, testCase.inputHeight);
model.setInput(input);
model.getLayer(0).setInput(input);
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer convolutionLayer =
(org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) model.getLayer(0);
double start = System.nanoTime();
for (int i = 0; i < forwardIterations; i++) {
convolutionLayer.preOutput(false);
}
double end = System.nanoTime();
double timeMillis = (end - start) / 1e6 / forwardIterations;
writer.write(
"Convolution("
+ testCase.nInputPlane
+ " "
+ testCase.nOutputPlane
+ " "
+ testCase.kW
+ " "
+ testCase.kH
+ " "
+ testCase.dW
+ " "
+ testCase.dH
+ " "
+ testCase.padW
+ " "
+ testCase.padH
+ " "
+ testCase.inputWidth
+ " "
+ testCase.inputHeight
+ ") "
+ " forward, "
+ timeMillis
+ "\n");
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
