Java Code Examples for org.deeplearning4j.nn.conf.MultiLayerConfiguration#toJson()
The following examples show how to use
org.deeplearning4j.nn.conf.MultiLayerConfiguration#toJson() .
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Example 1
| Source File: TestCustomActivation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testCustomActivationFn() {
//Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().updater(new Sgd(0.1)).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).activation(new CustomActivation()).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build();
String json = conf.toJson();
String yaml = conf.toYaml();
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
assertEquals(conf, confFromYaml);
}
Example 2
| Source File: CustomPreprocessorTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testCustomPreprocessor() {
//Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10)
.activation(Activation.SOFTMAX).nOut(10).build())
.inputPreProcessor(0, new MyCustomPreprocessor())
.build();
String json = conf.toJson();
String yaml = conf.toYaml();
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
assertEquals(conf, confFromYaml);
assertTrue(confFromJson.getInputPreProcess(0) instanceof MyCustomPreprocessor);
}
Example 3
| Source File: RandomTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testRngInitMLN() {
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(2,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build();
String json = conf.toJson();
MultiLayerNetwork net1 = new MultiLayerNetwork(conf);
net1.init();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf);
net2.init();
assertEquals(net1.params(), net2.params());
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(json);
Nd4j.getRandom().setSeed(987654321);
MultiLayerNetwork net3 = new MultiLayerNetwork(fromJson);
net3.init();
assertEquals(net1.params(), net3.params());
}
Example 4
| Source File: TestDropout.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSpatialDropoutJSON(){
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.list()
.layer(new DropoutLayer.Builder(new SpatialDropout(0.5)).build())
.build();
String asJson = conf.toJson();
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(asJson);
assertEquals(conf, fromJson);
}
Example 5
| Source File: TestVAE.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testJsonYaml() {
MultiLayerConfiguration config = new NeuralNetConfiguration.Builder().seed(12345).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.reconstructionDistribution(new GaussianReconstructionDistribution(Activation.IDENTITY))
.nIn(3).nOut(4).encoderLayerSizes(5).decoderLayerSizes(6).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.reconstructionDistribution(new GaussianReconstructionDistribution(Activation.TANH))
.nIn(7).nOut(8).encoderLayerSizes(9).decoderLayerSizes(10).build())
.layer(2, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.reconstructionDistribution(new BernoulliReconstructionDistribution()).nIn(11)
.nOut(12).encoderLayerSizes(13).decoderLayerSizes(14).build())
.layer(3, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.reconstructionDistribution(new ExponentialReconstructionDistribution(Activation.TANH))
.nIn(11).nOut(12).encoderLayerSizes(13).decoderLayerSizes(14).build())
.layer(4, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.lossFunction(new ActivationTanH(), LossFunctions.LossFunction.MSE).nIn(11)
.nOut(12).encoderLayerSizes(13).decoderLayerSizes(14).build())
.layer(5, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.reconstructionDistribution(new CompositeReconstructionDistribution.Builder()
.addDistribution(5, new GaussianReconstructionDistribution())
.addDistribution(5,
new GaussianReconstructionDistribution(Activation.TANH))
.addDistribution(5, new BernoulliReconstructionDistribution())
.build())
.nIn(15).nOut(16).encoderLayerSizes(17).decoderLayerSizes(18).build())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(18)
.nOut(19).activation(new ActivationTanH()).build())
.build();
String asJson = config.toJson();
String asYaml = config.toYaml();
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(asJson);
MultiLayerConfiguration fromYaml = MultiLayerConfiguration.fromYaml(asYaml);
assertEquals(config, fromJson);
assertEquals(config, fromYaml);
}
Example 6
| Source File: TestCustomLayers.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testJsonMultiLayerNetwork() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new CustomLayer(3.14159)).layer(2,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build();
String json = conf.toJson();
String yaml = conf.toYaml();
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
assertEquals(conf, confFromYaml);
}
Example 7
| Source File: GravesBidirectionalLSTMTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSerialization() {
final MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new AdaGrad(0.1))
.l2(0.001)
.seed(12345).list()
.layer(0, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder()
.activation(Activation.TANH).nIn(2).nOut(2)
.dist(new UniformDistribution(-0.05, 0.05)).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder()
.activation(Activation.TANH).nIn(2).nOut(2)
.dist(new UniformDistribution(-0.05, 0.05)).build())
.layer(2, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder()
.activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT)
.nIn(2).nOut(2).build())
.build();
final String json1 = conf1.toJson();
final MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson(json1);
final String json2 = conf1.toJson();
TestCase.assertEquals(json1, json2);
}
Example 8
| Source File: MiscRegressionTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testFrozenNewFormat(){
MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
.list()
.layer(0, new FrozenLayer(new DenseLayer.Builder().nIn(10).nOut(10).build()))
.build();
String json = configuration.toJson();
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(configuration, fromJson);
}
Example 9
| Source File: CNN1DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn1DWithCropping1D() {
Nd4j.getRandom().setSeed(1337);
int[] minibatchSizes = {1, 3};
int length = 7;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1, 2, 4};
int stride = 1;
int padding = 0;
int cropping = 1;
int croppedLength = length - 2 * cropping;
Activation[] activations = {Activation.SIGMOID};
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM};
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(new int[]{minibatchSize, convNIn, length});
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, croppedLength);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < croppedLength; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0);
}
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list()
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel)
.stride(stride).padding(padding).nIn(convNIn).nOut(convNOut1)
.build())
.layer(new Cropping1D.Builder(cropping).build())
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel)
.stride(stride).padding(padding).nIn(convNOut1).nOut(convNOut2)
.build())
.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.setInputType(InputType.recurrent(convNIn, length)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
+ afn + ", kernel = " + kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
}
Example 10
| Source File: CNN1DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn1DWithSubsampling1D() {
Nd4j.getRandom().setSeed(12345);
int[] minibatchSizes = {1, 3};
int length = 7;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1, 2, 4};
int stride = 1;
int padding = 0;
int pnorm = 2;
Activation[] activations = {Activation.SIGMOID, Activation.TANH};
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM};
for (Activation afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(new int[]{minibatchSize, convNIn, length});
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < length; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0);
}
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list()
.layer(0, new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel)
.stride(stride).padding(padding).nIn(convNIn).nOut(convNOut1)
.build())
.layer(1, new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel)
.stride(stride).padding(padding).nIn(convNOut1).nOut(convNOut2)
.build())
.layer(2, new Subsampling1DLayer.Builder(poolingType).kernelSize(kernel)
.stride(stride).padding(padding).pnorm(pnorm).build())
.layer(3, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.setInputType(InputType.recurrent(convNIn, length)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
+ afn + ", kernel = " + kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
}
Example 11
| Source File: CNN1DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn1DWithLocallyConnected1D() {
Nd4j.getRandom().setSeed(1337);
int[] minibatchSizes = {2, 3};
int length = 7;
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int finalNOut = 4;
int[] kernels = {1};
int stride = 1;
int padding = 0;
Activation[] activations = {Activation.SIGMOID};
for (Activation afn : activations) {
for (int minibatchSize : minibatchSizes) {
for (int kernel : kernels) {
INDArray input = Nd4j.rand(new int[]{minibatchSize, convNIn, length});
INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
for (int i = 0; i < minibatchSize; i++) {
for (int j = 0; j < length; j++) {
labels.putScalar(new int[]{i, i % finalNOut, j}, 1.0);
}
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.dist(new NormalDistribution(0, 1)).convolutionMode(ConvolutionMode.Same).list()
.layer(new Convolution1DLayer.Builder().activation(afn).kernelSize(kernel)
.stride(stride).padding(padding).nIn(convNIn).nOut(convNOut1)
.build())
.layer(new LocallyConnected1D.Builder().activation(afn).kernelSize(kernel)
.stride(stride).padding(padding).nIn(convNOut1).nOut(convNOut2).hasBias(false)
.build())
.layer(new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.setInputType(InputType.recurrent(convNIn, length)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "Minibatch=" + minibatchSize + ", activationFn="
+ afn + ", kernel = " + kernel;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
Example 12
| Source File: TestCustomUpdater.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCustomUpdater() {
//Create a simple custom updater, equivalent to SGD updater
double lr = 0.03;
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder().seed(12345)
.activation(Activation.TANH).updater(new CustomIUpdater(lr)) //Specify custom IUpdater
.list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new OutputLayer.Builder().nIn(10).nOut(10)
.lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345)
.activation(Activation.TANH).updater(new Sgd(lr)).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder()
.nIn(10).nOut(10).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
//First: Check updater config
assertTrue(((BaseLayer) conf1.getConf(0).getLayer()).getIUpdater() instanceof CustomIUpdater);
assertTrue(((BaseLayer) conf1.getConf(1).getLayer()).getIUpdater() instanceof CustomIUpdater);
assertTrue(((BaseLayer) conf2.getConf(0).getLayer()).getIUpdater() instanceof Sgd);
assertTrue(((BaseLayer) conf2.getConf(1).getLayer()).getIUpdater() instanceof Sgd);
CustomIUpdater u0_0 = (CustomIUpdater) ((BaseLayer) conf1.getConf(0).getLayer()).getIUpdater();
CustomIUpdater u0_1 = (CustomIUpdater) ((BaseLayer) conf1.getConf(1).getLayer()).getIUpdater();
assertEquals(lr, u0_0.getLearningRate(), 1e-6);
assertEquals(lr, u0_1.getLearningRate(), 1e-6);
Sgd u1_0 = (Sgd) ((BaseLayer) conf2.getConf(0).getLayer()).getIUpdater();
Sgd u1_1 = (Sgd) ((BaseLayer) conf2.getConf(1).getLayer()).getIUpdater();
assertEquals(lr, u1_0.getLearningRate(), 1e-6);
assertEquals(lr, u1_1.getLearningRate(), 1e-6);
//Second: check JSON
String asJson = conf1.toJson();
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(asJson);
assertEquals(conf1, fromJson);
Nd4j.getRandom().setSeed(12345);
MultiLayerNetwork net1 = new MultiLayerNetwork(conf1);
net1.init();
Nd4j.getRandom().setSeed(12345);
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
//Third: check gradients are equal
INDArray in = Nd4j.rand(5, 10);
INDArray labels = Nd4j.rand(5, 10);
net1.setInput(in);
net2.setInput(in);
net1.setLabels(labels);
net2.setLabels(labels);
net1.computeGradientAndScore();
net2.computeGradientAndScore();;
assertEquals(net1.getFlattenedGradients(), net2.getFlattenedGradients());
}
Example 13
| Source File: CNN3DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testDeconv3d() {
Nd4j.getRandom().setSeed(12345);
// Note: we checked this with a variety of parameters, but it takes a lot of time.
int[] depths = {8, 8, 9};
int[] heights = {8, 9, 9};
int[] widths = {8, 8, 9};
int[][] kernels = {{2, 2, 2}, {3, 3, 3}, {2, 3, 2}};
int[][] strides = {{1, 1, 1}, {1, 1, 1}, {2, 2, 2}};
Activation[] activations = {Activation.SIGMOID, Activation.TANH, Activation.IDENTITY};
ConvolutionMode[] modes = {ConvolutionMode.Truncate, ConvolutionMode.Same, ConvolutionMode.Same};
int[] mbs = {1, 3, 2};
Convolution3D.DataFormat[] dataFormats = new Convolution3D.DataFormat[]{Convolution3D.DataFormat.NCDHW, Convolution3D.DataFormat.NDHWC, Convolution3D.DataFormat.NCDHW};
int convNIn = 2;
int finalNOut = 2;
int[] deconvOut = {2, 3, 4};
for (int i = 0; i < activations.length; i++) {
Activation afn = activations[i];
int miniBatchSize = mbs[i];
int depth = depths[i];
int height = heights[i];
int width = widths[i];
ConvolutionMode mode = modes[i];
int[] kernel = kernels[i];
int[] stride = strides[i];
Convolution3D.DataFormat df = dataFormats[i];
int dOut = deconvOut[i];
INDArray input;
if (df == Convolution3D.DataFormat.NDHWC) {
input = Nd4j.rand(new int[]{miniBatchSize, depth, height, width, convNIn});
} else {
input = Nd4j.rand(new int[]{miniBatchSize, convNIn, depth, height, width});
}
INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
for (int j = 0; j < miniBatchSize; j++) {
labels.putScalar(new int[]{j, j % finalNOut}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.weightInit(new NormalDistribution(0, 0.1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel)
.stride(stride).nIn(convNIn).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Deconvolution3D.Builder().activation(afn).kernelSize(kernel)
.stride(stride).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.setInputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "DataFormat = " + df + ", minibatch size = " + miniBatchSize + ", activationFn=" + afn
+ ", kernel = " + Arrays.toString(kernel) + ", stride = "
+ Arrays.toString(stride) + ", mode = " + mode.toString()
+ ", input depth " + depth + ", input height " + height
+ ", input width " + width;
if (PRINT_RESULTS) {
log.info(msg);
}
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(64));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
Example 14
| Source File: FrozenLayerWithBackpropTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testFrozenWithBackpropLayerInstantiation() {
//We need to be able to instantitate frozen layers from JSON etc, and have them be the same as if
// they were initialized via the builder
MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder().seed(12345).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build())
.layer(1, new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build())
.layer(2, new OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10)
.nOut(10).build())
.build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).list().layer(0,
new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()))
.layer(1, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(
new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()))
.layer(2, new OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10)
.nOut(10).build())
.build();
MultiLayerNetwork net1 = new MultiLayerNetwork(conf1);
net1.init();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
assertEquals(net1.params(), net2.params());
String json = conf2.toJson();
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf2, fromJson);
MultiLayerNetwork net3 = new MultiLayerNetwork(fromJson);
net3.init();
INDArray input = Nd4j.rand(10, 10);
INDArray out2 = net2.output(input);
INDArray out3 = net3.output(input);
assertEquals(out2, out3);
}
Example 15
| Source File: CNN3DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn3DCropping() {
Nd4j.getRandom().setSeed(42);
int depth = 6;
int height = 6;
int width = 6;
int[] minibatchSizes = {3};
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int denseNOut = 5;
int finalNOut = 8;
int[] kernel = {1, 1, 1};
int[] cropping = {0, 0, 1, 1, 2, 2};
Activation[] activations = {Activation.SIGMOID};
ConvolutionMode[] modes = {ConvolutionMode.Same};
for (Activation afn : activations) {
for (int miniBatchSize : minibatchSizes) {
for (ConvolutionMode mode : modes) {
int outDepth = mode == ConvolutionMode.Same ?
depth : (depth - kernel[0]) + 1;
int outHeight = mode == ConvolutionMode.Same ?
height : (height - kernel[1]) + 1;
int outWidth = mode == ConvolutionMode.Same ?
width : (width - kernel[2]) + 1;
outDepth -= cropping[0] + cropping[1];
outHeight -= cropping[2] + cropping[3];
outWidth -= cropping[4] + cropping[5];
INDArray input = Nd4j.rand(new int[]{miniBatchSize, convNIn, depth, height, width});
INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
for (int i = 0; i < miniBatchSize; i++) {
labels.putScalar(new int[]{i, i % finalNOut}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel)
.nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build())
.layer(1, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNOut1).nOut(convNOut2).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build())
.layer(2, new Cropping3D.Builder(cropping).build())
.layer(3, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(3,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
convNOut2, true))
.setInputType(InputType.convolutional3D(depth, height, width, convNIn)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "Minibatch size = " + miniBatchSize + ", activationFn=" + afn
+ ", kernel = " + Arrays.toString(kernel) + ", mode = " + mode.toString()
+ ", input depth " + depth + ", input height " + height
+ ", input width " + width;
if (PRINT_RESULTS) {
log.info(msg);
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
Example 16
| Source File: CNN3DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn3DUpsampling() {
Nd4j.getRandom().setSeed(42);
int depth = 2;
int height = 2;
int width = 2;
int[] minibatchSizes = {3};
int convNIn = 2;
int convNOut = 4;
int denseNOut = 5;
int finalNOut = 42;
int[] upsamplingSize = {2, 2, 2};
Activation[] activations = {Activation.SIGMOID};
ConvolutionMode[] modes = {ConvolutionMode.Truncate};
for (Activation afn : activations) {
for (int miniBatchSize : minibatchSizes) {
for (ConvolutionMode mode : modes) {
for(Convolution3D.DataFormat df : Convolution3D.DataFormat.values()) {
int outDepth = depth * upsamplingSize[0];
int outHeight = height * upsamplingSize[1];
int outWidth = width * upsamplingSize[2];
INDArray input = df == Convolution3D.DataFormat.NCDHW ? Nd4j.rand(miniBatchSize, convNIn, depth, height, width) : Nd4j.rand(miniBatchSize, depth, height, width, convNIn);
INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
for (int i = 0; i < miniBatchSize; i++) {
labels.putScalar(new int[]{i, i % finalNOut}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.seed(12345)
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNIn).nOut(convNOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Upsampling3D.Builder(upsamplingSize[0]).dataFormat(df).build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
convNOut, true))
.setInputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "Minibatch size = " + miniBatchSize + ", activationFn=" + afn
+ ", kernel = " + Arrays.toString(upsamplingSize) + ", mode = " + mode.toString()
+ ", input depth " + depth + ", input height " + height
+ ", input width " + width;
if (PRINT_RESULTS) {
log.info(msg);
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
}
Example 17
| Source File: TestCustomLayers.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCustomOutputLayerMLN() {
//Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().seed(12345).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new CustomOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nIn(10).nOut(10).build())
.build();
String json = conf.toJson();
String yaml = conf.toYaml();
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
assertEquals(conf, confFromYaml);
//Third: check initialization
Nd4j.getRandom().setSeed(12345);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertTrue(net.getLayer(1) instanceof CustomOutputLayerImpl);
//Fourth: compare to an equivalent standard output layer (should be identical)
MultiLayerConfiguration conf2 =
new NeuralNetConfiguration.Builder().seed(12345).weightInit(WeightInit.XAVIER)
.list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build();
Nd4j.getRandom().setSeed(12345);
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
assertEquals(net2.params(), net.params());
INDArray testFeatures = Nd4j.rand(1, 10);
INDArray testLabels = Nd4j.zeros(1, 10);
testLabels.putScalar(0, 3, 1.0);
DataSet ds = new DataSet(testFeatures, testLabels);
assertEquals(net2.output(testFeatures), net.output(testFeatures));
assertEquals(net2.score(ds), net.score(ds), 1e-6);
}
Example 18
| Source File: CNN3DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn3DPooling() {
Nd4j.getRandom().setSeed(42);
int depth = 4;
int height = 4;
int width = 4;
int[] minibatchSizes = {3};
int convNIn = 2;
int convNOut = 4;
int denseNOut = 5;
int finalNOut = 42;
int[] kernel = {2, 2, 2};
Activation[] activations = {Activation.SIGMOID};
Subsampling3DLayer.PoolingType[] poolModes = {Subsampling3DLayer.PoolingType.AVG};
ConvolutionMode[] modes = {ConvolutionMode.Truncate};
for (Activation afn : activations) {
for (int miniBatchSize : minibatchSizes) {
for (Subsampling3DLayer.PoolingType pool : poolModes) {
for (ConvolutionMode mode : modes) {
for (Convolution3D.DataFormat df : Convolution3D.DataFormat.values()) {
int outDepth = depth / kernel[0];
int outHeight = height / kernel[1];
int outWidth = width / kernel[2];
INDArray input = Nd4j.rand(
df == Convolution3D.DataFormat.NCDHW ? new int[]{miniBatchSize, convNIn, depth, height, width}
: new int[]{miniBatchSize, depth, height, width, convNIn});
INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
for (int i = 0; i < miniBatchSize; i++) {
labels.putScalar(new int[]{i, i % finalNOut}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.weightInit(WeightInit.XAVIER)
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNIn).nOut(convNOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Subsampling3DLayer.Builder(kernel)
.poolingType(pool).convolutionMode(mode).dataFormat(df).build())
.layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(2,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,convNOut, df))
.setInputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "Minibatch size = " + miniBatchSize + ", activationFn=" + afn
+ ", kernel = " + Arrays.toString(kernel) + ", mode = " + mode.toString()
+ ", input depth " + depth + ", input height " + height
+ ", input width " + width + ", dataFormat=" + df;
if (PRINT_RESULTS) {
log.info(msg);
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
}
}
Example 19
| Source File: FrozenLayerTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testFrozenLayerInstantiation() {
//We need to be able to instantitate frozen layers from JSON etc, and have them be the same as if
// they were initialized via the builder
MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder().seed(12345).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build())
.layer(1, new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build())
.layer(2, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10)
.nOut(10).build())
.build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).list().layer(0,
new org.deeplearning4j.nn.conf.layers.misc.FrozenLayer(new DenseLayer.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()))
.layer(1, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayer(
new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()))
.layer(2, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10)
.nOut(10).build())
.build();
MultiLayerNetwork net1 = new MultiLayerNetwork(conf1);
net1.init();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
assertEquals(net1.params(), net2.params());
String json = conf2.toJson();
MultiLayerConfiguration fromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf2, fromJson);
MultiLayerNetwork net3 = new MultiLayerNetwork(fromJson);
net3.init();
INDArray input = Nd4j.rand(10, 10);
INDArray out2 = net2.output(input);
INDArray out3 = net3.output(input);
assertEquals(out2, out3);
}
Example 20
| Source File: CNN3DGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnn3DZeroPadding() {
Nd4j.getRandom().setSeed(42);
int depth = 4;
int height = 4;
int width = 4;
int[] minibatchSizes = {3};
int convNIn = 2;
int convNOut1 = 3;
int convNOut2 = 4;
int denseNOut = 5;
int finalNOut = 42;
int[] kernel = {2, 2, 2};
int[] zeroPadding = {1, 1, 2, 2, 3, 3};
Activation[] activations = {Activation.SIGMOID};
ConvolutionMode[] modes = {ConvolutionMode.Truncate, ConvolutionMode.Same};
for (Activation afn : activations) {
for (int miniBatchSize : minibatchSizes) {
for (ConvolutionMode mode : modes) {
int outDepth = mode == ConvolutionMode.Same ?
depth : (depth - kernel[0]) + 1;
int outHeight = mode == ConvolutionMode.Same ?
height : (height - kernel[1]) + 1;
int outWidth = mode == ConvolutionMode.Same ?
width : (width - kernel[2]) + 1;
outDepth += zeroPadding[0] + zeroPadding[1];
outHeight += zeroPadding[2] + zeroPadding[3];
outWidth += zeroPadding[4] + zeroPadding[5];
INDArray input = Nd4j.rand(new int[]{miniBatchSize, convNIn, depth, height, width});
INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
for (int i = 0; i < miniBatchSize; i++) {
labels.putScalar(new int[]{i, i % finalNOut}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel)
.nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build())
.layer(1, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1)
.nIn(convNOut1).nOut(convNOut2).hasBias(false)
.convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
.build())
.layer(2, new ZeroPadding3DLayer.Builder(zeroPadding).build())
.layer(3, new DenseLayer.Builder().nOut(denseNOut).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.inputPreProcessor(3,
new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
convNOut2, true))
.setInputType(InputType.convolutional3D(depth, height, width, convNIn)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "Minibatch size = " + miniBatchSize + ", activationFn=" + afn
+ ", kernel = " + Arrays.toString(kernel) + ", mode = " + mode.toString()
+ ", input depth " + depth + ", input height " + height
+ ", input width " + width;
if (PRINT_RESULTS) {
log.info(msg);
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(512));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
