@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(); } } }