private static MultiLayerNetwork deepConvNetwork(
int seed, int iterations, int numRows, int numColumns, int outputNum) {
MultiLayerConfiguration.Builder conf =
new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.activation("sigmoid")
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0.0, 0.01))
// .learningRate(7*10e-5)
.learningRate(1e-3)
.learningRateScoreBasedDecayRate(1e-1)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.list(7)
.layer(
0,
new ConvolutionLayer.Builder(new int[] {5, 5}, new int[] {1, 1})
.name("cnn1")
.nIn(numRows * numColumns)
.nOut(6)
.build())
.layer(
1,
new SubsamplingLayer.Builder(
SubsamplingLayer.PoolingType.MAX, new int[] {2, 2}, new int[] {2, 2})
.name("maxpool1")
.build())
.layer(
2,
new ConvolutionLayer.Builder(new int[] {5, 5}, new int[] {1, 1})
.name("cnn2")
.nOut(16)
.biasInit(1)
.build())
.layer(
3,
new SubsamplingLayer.Builder(
SubsamplingLayer.PoolingType.MAX, new int[] {2, 2}, new int[] {2, 2})
.name("maxpool2")
.build())
.layer(4, new DenseLayer.Builder().name("ffn1").nOut(120).build())
.layer(5, new DenseLayer.Builder().name("ffn2").nOut(84).build())
.layer(
6,
new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.name("output")
.nOut(outputNum)
.activation("softmax") // radial basis function
// required
.build())
.backprop(true)
.pretrain(false)
.cnnInputSize(numRows, numColumns, 1);
MultiLayerNetwork model = new MultiLayerNetwork(conf.build());
return model;
}
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();
}
}
}
public static void testBackward() {
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);
model.feedForward();
org.deeplearning4j.nn.api.Layer convolutionLayer = model.getLayer(0);
INDArray output = convolutionLayer.activate();
INDArray epsilon =
Nd4j.rand(seed, output.size(0), output.size(1), output.size(2), output.size(3));
Method initGradientView = model.getClass().getDeclaredMethod("initGradientsView");
initGradientView.setAccessible(true);
initGradientView.invoke(model);
double start = System.nanoTime();
for (int i = 0; i < backwardIterations; i++) {
convolutionLayer.backpropGradient(epsilon);
}
double end = System.nanoTime();
double timeMillis = (end - start) / 1e6 / backwardIterations;
writer.write(
"Convolution("
+ testCase.nInputPlane
+ " "
+ testCase.nOutputPlane
+ " "
+ testCase.kW
+ " "
+ testCase.kH
+ " "
+ testCase.dW
+ " "
+ testCase.dH
+ " "
+ testCase.padW
+ " "
+ testCase.padH
+ " "
+ testCase.inputWidth
+ " "
+ testCase.inputHeight
+ ") "
+ " backward, "
+ timeMillis
+ "\n");
} catch (Exception ex) {
ex.printStackTrace();
}
}
}
