Java Code Examples for org.deeplearning4j.nn.conf.ConvolutionMode#toString()
The following examples show how to use
org.deeplearning4j.nn.conf.ConvolutionMode#toString() .
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Example 1
| 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);
}
}
}
}
Example 2
| 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 3
| 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 4
| 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 5
| 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);
}
}
