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public void closeBuffer() {
printAllocationTraceIfNeeded();
if(Nd4j.getEnvironment().isFuncTracePrintDeallocate()) {
System.out.println("Java side deallocation current trace: \n " + currentTrace());
}
Nd4j.getNativeOps().dbClose(this);
} |
This method releases underlying buffer
| OpaqueDataBuffer::closeBuffer | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpaqueDataBuffer.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpaqueDataBuffer.java | Apache-2.0 |
public OpExecTrace(Pointer p) { super(p); } | /*
******************************************************************************
*
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
*****************************************************************************
package org.nd4j.nativeblas;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import java.nio.DoubleBuffer;
import java.nio.IntBuffer;
import java.nio.LongBuffer;
@Namespace("sd::ops") @NoOffset
public class OpExecTrace extends Pointer {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. | OpExecTrace::OpExecTrace | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpExecTrace.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpExecTrace.java | Apache-2.0 |
public OpExecTrace(long size) { super((Pointer)null); allocateArray(size); } | /*
******************************************************************************
*
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
*****************************************************************************
package org.nd4j.nativeblas;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import java.nio.DoubleBuffer;
import java.nio.IntBuffer;
import java.nio.LongBuffer;
@Namespace("sd::ops") @NoOffset
public class OpExecTrace extends Pointer {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}.
public OpExecTrace(Pointer p) { super(p); }
/** Native array allocator. Access with {@link Pointer#position(long)}. | OpExecTrace::OpExecTrace | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpExecTrace.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpExecTrace.java | Apache-2.0 |
public static OpaqueNDArrayArr createFrom(List<INDArray> array) {
OpaqueNDArray[] inputs = array.stream()
.map(OpaqueNDArray::fromINDArray).toArray(OpaqueNDArray[]::new);
OpaqueNDArrayArr inputsOpaque = (OpaqueNDArrayArr) new OpaqueNDArrayArr().capacity(inputs.length);
inputsOpaque.put(inputs);
return inputsOpaque;
} |
@see {@link #createFrom(INDArray...)}
@param array
@return
| OpaqueNDArrayArr::createFrom | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpaqueNDArrayArr.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/nativeblas/OpaqueNDArrayArr.java | Apache-2.0 |
public static String validate(TestCase testCase) {
return validate(testCase, false);
} |
Run test case
@param testCase Test case to run
@return NULL if test passes, or error message otherwise
| OpValidation::validate | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java | Apache-2.0 |
public static String validate(OpTestCase testCase) {
collectCoverageInformation(testCase);
//Check shape function:
List<LongShapeDescriptor> outShapes;
try {
outShapes = Nd4j.getExecutioner().calculateOutputShape(testCase.op());
} catch (Throwable t) {
throw new IllegalStateException("Error calculating output shapes during op validation", t);
}
if (outShapes.size() != testCase.testFns().size()) {
return "Expected number of output shapes and number of outputs differ. " + outShapes.size() + " output shapes," +
" but OpTestCase specifies " + testCase.testFns().size() + " outputs expected";
}
for (int i = 0; i < outShapes.size(); i++) {
val act = outShapes.get(i);
val exp = testCase.expShapes().get(i);
if(!Objects.equals(exp.dataType(), act.dataType())) {
return "Shape function check failed for output " + i + ": expected shape " + exp + ", actual shape " + act;
}
if(!Arrays.equals(act.getShape(), exp.getShape())){
return "Shape function check failed for output " + i + ": expected shape " + exp + ", actual shape " + act;
}
}
//Check the outputs:
try {
Nd4j.getExecutioner().execAndReturn(testCase.op());
} catch (Throwable t) {
throw new IllegalStateException("Error during op execution", t);
}
for (int i = 0; i < testCase.testFns().size(); i++) {
String error;
try {
error = testCase.testFns().get(i).apply(testCase.op().outputArguments().get(i));
} catch (Throwable t) {
throw new IllegalStateException("Exception thrown during op output validation for output " + i, t);
}
if (error != null) {
return "Output " + i + " failed: " + error;
}
}
return null; //OK
} |
Validate the outputs of a single op
@param testCase Op test case to run
@return NULL if test is OK, or an error message otherwise
| OpValidation::validate | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java | Apache-2.0 |
public OpTestCase expectedOutput(int outputNum, INDArray expected) {
return expectedOutput(outputNum,expected,1e-3);
} |
Validate the op output using INDArray.equals(INDArray)
@param outputNum Number of the output
@param expected Expected INDArray
| OpTestCase::expectedOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | Apache-2.0 |
public OpTestCase expectedOutput(int outputNum, INDArray expected,double eps) {
testFns.put(outputNum, new EqualityFn(expected,eps));
expShapes.put(outputNum, expected.shapeDescriptor());
return this;
} |
Validate the op output using INDArray.equals(INDArray)
@param outputNum Number of the output
@param expected Expected INDArray
| OpTestCase::expectedOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | Apache-2.0 |
public OpTestCase expectedOutputRelError(int outputNum, @NonNull INDArray expected, double maxRelError, double minAbsError) {
testFns.put(outputNum, new RelErrorFn(expected, maxRelError, minAbsError));
expShapes.put(outputNum, expected.shapeDescriptor());
return this;
} |
Validate the output for a single variable using element-wise relative error:
relError = abs(x-y)/(abs(x)+abs(y)), with x=y=0 case defined to be 0.0.
Also has a minimum absolute error condition, which must be satisfied for the relative error failure to be considered
legitimate
@param outputNum output number
@param expected Expected INDArray
@param maxRelError Maximum allowable relative error
@param minAbsError Minimum absolute error for a failure to be considered legitimate
| OpTestCase::expectedOutputRelError | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | Apache-2.0 |
public OpTestCase expectedOutput(int outputNum, @NonNull LongShapeDescriptor expShape, @NonNull Function<INDArray, String> validationFn) {
testFns.put(outputNum, validationFn);
expShapes.put(outputNum, expShape);
return this;
} |
@param outputNum Output number to check
@param expShape Expected shape for the output
@param validationFn Function to use to validate the correctness of the specific Op. Should return null
if validation passes, or an error message if the op validation fails
| OpTestCase::expectedOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpTestCase.java | Apache-2.0 |
public static boolean checkActivationGradients(ActGradConfig config){
SameDiff sd = config.getSd();
List<String> actGrads = config.getActivationGradsToCheck();
double maxRelError = config.getMaxRelError();
double minAbsError = config.getMinAbsError();
Preconditions.checkState(sd != null, "SameDiff instance was not set in configuration");
Preconditions.checkState(actGrads != null && !actGrads.isEmpty(), "No activation gradients were specified to gradient check");
Preconditions.checkState(config.getEps() > 0.0, "Epsilon has not been set");
Preconditions.checkState(maxRelError > 0.0, "Max relative error must be set (is 0.0)");
for(String s : actGrads){
SDVariable v = sd.getVariables().get(s).getVariable();
Preconditions.checkState(v != null, "No variable with name \"%s\" was found", s);
Preconditions.checkState(v.getVariableType() == VariableType.ARRAY, "Only variables with type ARRAY may be " +
"gradient checked using this method. Variable \"%s\" has type %s", s, v.getVariableType());
Preconditions.checkState(v.dataType().isFPType(), "Cannot gradient check activation variable \"%s\": must be floating point type. Is type: %s", s, v.dataType());
if(v.dataType() != DataType.DOUBLE){
log.warn("Floating point variable {} is not double precision - this may result in spurious failures due to limited precision. Variable is type: {}", s, v.dataType());
}
}
boolean debugBefore = sd.isDebugMode();
if(config.isDebugMode()){
sd.enableDebugMode();
}
//Validation sanity checks:
if(!config.isSkipValidation()){
validateInternalState(sd, true);
}
//Loss function variables
List<String> lossFnVariables = sd.getLossVariables();
Preconditions.checkState(lossFnVariables != null && !lossFnVariables.isEmpty(), "Expected 1 or more loss function variables for gradient check, got %s", lossFnVariables);
//TODO also check that all inputs are non-zero (otherwise: consider out = sum(x * y) with all x and y being 0
// in this case, gradients of x and y are all 0 too
//Collect names of variables to get gradients for - i.e., the names of the GRADIENT variables for the specified activations
sd.createGradFunction();
Set<String> varsRequiringGrads = new HashSet<>();
for(String s : actGrads){
SDVariable grad = sd.getVariable(s).gradient();
Preconditions.checkState( grad != null,"Could not get gradient for activation \"%s\": gradient variable is null", s);
varsRequiringGrads.add(s);
}
//Calculate analytical gradients
Map<String,INDArray> grads = sd.calculateGradients(config.getPlaceholderValues(), new ArrayList<>(varsRequiringGrads));
Map<String,INDArray> gradientsForAct = new HashMap<>();
for(String s : actGrads){
INDArray arr = grads.get(s);
Preconditions.checkState(arr != null, "No activation gradient array for variable \"%s\"", s);
gradientsForAct.put(s, arr.dup());
}
//Now, check gradients
int totalNFailures = 0;
int totalCount = 0;
double maxError = 0.0;
ActivationGradientCheckListener listener = new ActivationGradientCheckListener();
sd.setListeners(listener);
Random r = new Random(12345);
int maxPerParam = config.getMaxPerParam();
for(String s : actGrads){
long n = gradientsForAct.get(s).length();
if(config.isPrint()){
log.info("Starting test for variable \"{}\" with {} values", s, n);
}
Iterator<long[]> iter;
if(maxPerParam > 0 && config.getSubset() != null && maxPerParam < n){
//Subset case
long[] shape = gradientsForAct.get(s).shape();
List<long[]> l = new ArrayList<>();
if(config.getSubset() == Subset.RANDOM){
Set<Integer> set = new HashSet<>();
while(set.size() < maxPerParam){
int next = r.nextInt((int)n);
set.add(next);
}
List<Integer> sorted = new ArrayList<>(set);
Collections.sort(sorted);
for(Integer i : sorted){
long[] pos = Shape.ind2subC(shape, i);
l.add(pos);
}
} else {
//Every N
long everyN = n / maxPerParam;
long curr = 0;
while(curr < n){
long[] pos = Shape.ind2subC(shape, curr);
l.add(pos);
curr += everyN;
}
}
iter = l.iterator();
} else {
//Standard case: do all parameters
iter = new NdIndexIterator('c',gradientsForAct.get(s).shape());
}
INDArray varMask = (config.getGradCheckMask() == null ? null : config.getGradCheckMask().get(s));
listener.setVariableName(s);
int i=0;
while(iter.hasNext()){
long[] idx = iter.next();
String strIdx = null;
if(config.isPrint()){
strIdx = Arrays.toString(idx).replaceAll(" ","");
}
boolean maskValue = (varMask == null || (varMask.getDouble(idx) != 0));
if(!maskValue){
//Skip this specific entry (masked out)
continue;
}
//Set listener to apply eps, then do forward pass:
listener.setIdx(idx);
listener.setEps(config.getEps());
double scorePlus = 0.0;
Map<String,INDArray> m = sd.output(config.getPlaceholderValues(), lossFnVariables);
for(INDArray arr : m.values()){
scorePlus += arr.sumNumber().doubleValue();
}
listener.setEps(-config.getEps());
m = sd.output(config.getPlaceholderValues(), lossFnVariables);
double scoreMinus = 0.0;
for(INDArray arr : m.values()){
scoreMinus += arr.sumNumber().doubleValue();
}
double numericalGrad = (scorePlus - scoreMinus) / (2 * config.getEps());
double analyticGrad = gradientsForAct.get(s).getDouble(idx);
if (Double.isInfinite(numericalGrad) || Double.isNaN(numericalGrad)) {
throw new IllegalStateException("Numerical gradient was " + numericalGrad + " for variable \"" + s
+ "\", parameter " + i + " of " + n + " (position: " + strIdx + ")");
}
if (Double.isInfinite(analyticGrad) || Double.isNaN(analyticGrad)) {
throw new IllegalStateException("Analytic (SameDiff) gradient was " + analyticGrad + " for variable \"" + s
+ "\", parameter " + i + " of " + n + " (position: " + strIdx + ")");
}
double relError;
if(numericalGrad == 0.0 && analyticGrad == 0.0){
relError = 0.0;
} else {
relError = Math.abs(analyticGrad - numericalGrad) / (Math.abs(Math.abs(analyticGrad) + Math.abs(numericalGrad)));
}
if (relError > maxError)
maxError = relError;
if (relError > maxRelError || Double.isNaN(relError)) {
double absError = Math.abs(analyticGrad - numericalGrad);
if (absError < minAbsError) {
if(config.isPrint()) {
log.info("Param " + i + " (" + s + strIdx + ") passed: grad= " + analyticGrad
+ ", numericalGrad= " + numericalGrad + ", relError= " + relError
+ "; absolute error = " + absError + " < minAbsoluteError = " + minAbsError);
}
} else {
if (config.isPrint())
log.info("Param " + i + " (" + s + strIdx + ") FAILED: grad= " + analyticGrad
+ ", numericalGrad= " + numericalGrad + ", relError= " + relError
+ ", absError=" + absError
+ ", scorePlus=" + scorePlus + ", scoreMinus= " + scoreMinus);
if (config.isExitOnFirstFailure())
return false;
totalNFailures++;
}
} else if (config.isPrint()) {
log.info("Param " + i + " (" + s + strIdx + ") passed: grad= " + analyticGrad + ", numericalGrad= "
+ numericalGrad + ", relError= " + relError);
}
i++;
}
}
return totalNFailures == 0;
} |
Gradient check the ACTIVATIONS (i.e., ARRAY type SDVariables) as opposed to the parameters of a network (as
are tested in {@link #checkGradients(SameDiff, Map, double, double, double, boolean, boolean, boolean, boolean, Set, Map, int, Subset)}
@param config Configuration for gradient check
@return True if gradient checks pass
| Subset::checkActivationGradients | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/GradCheckUtil.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/GradCheckUtil.java | Apache-2.0 |
public TestCase(SameDiff sameDiff) {
this.sameDiff = sameDiff;
} |
@param sameDiff SameDiff instance to test. Note: All of the required inputs should already be set
| TestSerialization::TestCase | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase expectedOutput(@NonNull String name, @NonNull INDArray expected,double eps) {
return expected(name, new EqualityFn(expected,eps));
} |
Validate the output (forward pass) for a single variable using INDArray.equals(INDArray)
@param name Name of the variable to check
@param expected Expected INDArray
@param eps the expected epsilon, defaults to 1e-3
| TestSerialization::expectedOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase expectedOutput(@NonNull String name, @NonNull INDArray expected) {
return expectedOutput(name,expected,1e-3);
} |
Validate the output (forward pass) for a single variable using INDArray.equals(INDArray)
@param name Name of the variable to check
@param expected Expected INDArray
| TestSerialization::expectedOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase expectedOutputRelError(@NonNull String name, @NonNull INDArray expected, double maxRelError, double minAbsError) {
return expected(name, new RelErrorFn(expected, maxRelError, minAbsError));
} |
Validate the output (forward pass) for a single variable using element-wise relative error:
relError = abs(x-y)/(abs(x)+abs(y)), with x=y=0 case defined to be 0.0.
Also has a minimum absolute error condition, which must be satisfied for the relative error failure to be considered
legitimate
@param name Name of the variable to check
@param expected Expected INDArray
@param maxRelError Maximum allowable relative error
@param minAbsError Minimum absolute error for a failure to be considered legitimate
| TestSerialization::expectedOutputRelError | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase expected(@NonNull SDVariable var, @NonNull INDArray output) {
return expected(var.name(), output);
} |
Validate the output (forward pass) for a single variable using INDArray.equals(INDArray)
@param var Variable to check
@param output Expected INDArray
| TestSerialization::expected | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase expected(@NonNull String name, @NonNull INDArray output) {
return expectedOutput(name, output);
} |
Validate the output (forward pass) for a single variable using INDArray.equals(INDArray)
@param name Name of the variable to check
@param output Expected INDArray
| TestSerialization::expected | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase expected(String name, Function<INDArray, String> validationFn) {
if (fwdTestFns == null)
fwdTestFns = new LinkedHashMap<>();
fwdTestFns.put(name, validationFn);
return this;
} |
@param name The name of the variable to check
@param validationFn Function to use to validate the correctness of the specific Op. Should return null
if validation passes, or an error message if the op validation fails
| TestSerialization::expected | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public TestCase gradCheckSkipVariables(String... toSkip) {
if (gradCheckSkipVariables == null)
gradCheckSkipVariables = new LinkedHashSet<>();
Collections.addAll(gradCheckSkipVariables, toSkip);
return this;
} |
Specify the input variables that should NOT be gradient checked.
For example, if an input is an integer index (not real valued) it should be skipped as such an input cannot
be gradient checked
@param toSkip Name of the input variables to skip gradient check for
| TestSerialization::gradCheckSkipVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/TestCase.java | Apache-2.0 |
public static Map<String, INDArray> stackOutputs(List<Map<String, INDArray>> outputs){
Map<String, List<INDArray>> outs = new HashMap<>();
for(Map<String, INDArray> batch : outputs){
for(String k : batch.keySet()){
if(!outs.containsKey(k))
outs.put(k, new ArrayList<INDArray>());
outs.get(k).add(batch.get(k));
}
}
Map<String, INDArray> ret = new HashMap<>();
for(String k : outs.keySet()){
try {
ret.put(k, Nd4j.concat(0, outs.get(k).toArray(new INDArray[0])));
} catch(Exception e){
throw new ND4JException("Error concatenating batch outputs", e);
}
}
return ret;
} |
Stack batch outputs, like an output from {@link org.nd4j.autodiff.samediff.SameDiff#output(MultiDataSetIterator, String...)}
| TrainingUtils::stackOutputs | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/TrainingUtils.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/TrainingUtils.java | Apache-2.0 |
public static List<INDArray> getSingleOutput(List<Map<String, INDArray>> outputs, String output){
List<INDArray> batches = new ArrayList<>();
for(Map<String, INDArray> batch : outputs)
batches.add(batch.get(output));
return batches;
} |
Get a list of batch outputs for a single variable from a list of batch outputs for all variables
| TrainingUtils::getSingleOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/TrainingUtils.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/TrainingUtils.java | Apache-2.0 |
public static boolean executedOn(String className,int lineNumber,DifferentialFunction funcToTest) {
if(funcToTest.getCreationLocation() != null) {
return funcToTest.getCreationLocation().getLineNumber() == lineNumber &&
funcToTest.getCreationLocation().getClassName().equals(className);
}
return false;
} |
Tests whether the given function was executed on the given line number and class name.
Note: In order for this function to work correctly, ensure that
{@link Environment#isVerbose()}} or {@link Environment#isDebug()} is true
set via {@link Environment#setDebug(boolean)} or {@link Environment#setVerbose(boolean)}
or {@link org.nd4j.linalg.api.ops.executioner.OpExecutioner#enableDebugMode(boolean)}
or {@link org.nd4j.linalg.api.ops.executioner.OpExecutioner#enableVerboseMode(boolean)}
@param className class name to test
@param lineNumber line number to test
@param funcToTest function to test
@return
| SameDiffUtils::executedOn | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | Apache-2.0 |
public static Map<String, INDArray> stackOutputs(List<ExecutionResult> outputs){
Map<String, List<INDArray>> outs = new HashMap<>();
for(ExecutionResult batch : outputs) {
if(batch.getOutputs() != null) {
for(String k : batch.getOutputs().keySet()) {
if(!outs.containsKey(k))
outs.put(k, new ArrayList<>());
outs.get(k).add(batch.getOutputs().get(k).get());
}
} else if(batch.getValueOutputs() != null) {
for(String k : batch.getValueOutputs().keySet()) {
if(!outs.containsKey(k))
outs.put(k, new ArrayList<>());
outs.get(k).add(batch.getValueOutputs().get(k).getTensorValue());
}
}
}
Map<String, INDArray> ret = new HashMap<>();
for(String k : outs.keySet()) {
try {
ret.put(k, Nd4j.concat(0, outs.get(k).toArray(new INDArray[0])));
} catch(Exception e){
throw new ND4JException("Error concatenating batch outputs", e);
}
}
return ret;
} |
Stack batch outputs, like an output from {@link SameDiff#output(MultiDataSetIterator, String...)}
| SameDiffUtils::stackOutputs | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | Apache-2.0 |
public static List<INDArray> getSingleOutput(List<Map<String, INDArray>> outputs, String output){
List<INDArray> batches = new ArrayList<>();
for(Map<String, INDArray> batch : outputs)
batches.add(batch.get(output));
return batches;
} |
Get a list of batch outputs for a single variable from a list of batch outputs for all variables
| SameDiffUtils::getSingleOutput | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | Apache-2.0 |
public static SDVariable reductionBroadcastableWithOrigShape(int origRank, int[] reduceDims, SDVariable toExpand) {
if (Shape.isWholeArray(origRank, reduceDims)) {
//Output is [1,1] which is already broadcastable
return toExpand;
} else if (origRank == 2 && reduceDims.length == 1) {
//In this case: [a,b] -> [1,b] or [a,b] -> [a,1]
//both are already broadcastable
return toExpand;
} else {
//Example: [a,b,c].sum(1) -> [a,c]... want [a,1,c]
for (int d : reduceDims) {
toExpand = toExpand.getSameDiff().expandDims(toExpand, d);
}
return toExpand;
}
} |
Add 1s as required to the array make an array possible to be broadcast with the original (pre-reduce) array.
<p>
Example: if doing [a,b,c].sum(1), result is [a,c]. To 'undo' this in a way that can be auto-broadcast,
we want to expand as required - i.e., [a,c] -> [a,1,c] which can be auto-broadcast with the original [a,b,c].
This is typically only used with reduction operations backprop.
@param origRank Rank of the original array, before the reduction was executed
@param reduceDims Dimensions that the original array was reduced from
@param toExpand Array to add 1s to the shape to (such that it can be
@return Reshaped array.
| SameDiffUtils::reductionBroadcastableWithOrigShape | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/util/SameDiffUtils.java | Apache-2.0 |
public Operands addArgument(@NonNull String id, @NonNull INDArray array) {
map.put(NodeDescriptor.builder().name(id).build(), array);
return this;
} |
This method allows to pass array to the node identified by its name
@param id
@param array
@return
| Operands::addArgument | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public Operands addArgument(int id, @NonNull INDArray array) {
map.put(NodeDescriptor.builder().id(id).build(), array);
return this;
} |
This method allows to pass array to the node identified by numeric id
@param id
@param array
@return
| Operands::addArgument | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public Operands addArgument( int id, int index, @NonNull INDArray array) {
map.put(NodeDescriptor.builder().id(id).index(index).build(), array);
return this;
} |
This method allows to pass array to multi-output node in the graph
@param id
@param index
@param array
@return
| Operands::addArgument | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public Operands addArgument(String name, int id, int index, @NonNull INDArray array) {
map.put(NodeDescriptor.builder().name(name).id(id).index(index).build(), array);
return this;
} |
This method allows to pass array to multi-output node in the graph
@param id
@param index
@param array
@return
| Operands::addArgument | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public INDArray getById(@NonNull String name) {
return map.get(NodeDescriptor.builder().name(name).build());
} |
This method returns array identified its name
@param name
@return
| Operands::getById | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public INDArray getById(int id) {
return map.get(NodeDescriptor.builder().id(id).build());
} |
This method returns array identified its numeric id
@param name
@return
| Operands::getById | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public INDArray getById(int id, int index) {
return map.get(NodeDescriptor.builder().id(id).index(index).build());
} |
This method returns array identified its numeric id and index
@param name
@return
| Operands::getById | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public INDArray[] asArray() {
val val = map.values();
val res = new INDArray[val.size()];
int cnt = 0;
for (val v: val)
res[cnt++] = v;
return res;
} |
This method return operands as array, in order of addition
@return
| Operands::asArray | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public Collection<Pair<NodeDescriptor, INDArray>> asCollection() {
val c = new HashSet<Pair<NodeDescriptor, INDArray>>();
for (val k: map.keySet())
c.add(Pair.makePair(k, map.get(k)));
return c;
} |
This method returns contents of this entity as collection of key->value pairs
@return
| Operands::asCollection | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public int size() {
return map.size();
} |
This method returns number of values in this entity
@return
| Operands::size | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/input/Operands.java | Apache-2.0 |
public int getFlatConfiguration(FlatBufferBuilder builder) {
byte prof = profilingMode == OpExecutioner.ProfilingMode.INF_PANIC ? ProfilingMode.INF_PANIC :
profilingMode == OpExecutioner.ProfilingMode.NAN_PANIC ? ProfilingMode.NAN_PANIC :
profilingMode == OpExecutioner.ProfilingMode.ANY_PANIC ? ProfilingMode.ANY_PANIC : ProfilingMode.NONE;
byte exec = executionMode == ExecutionMode.SEQUENTIAL ? org.nd4j.graph.ExecutionMode.SEQUENTIAL :
executionMode == ExecutionMode.AUTO ? org.nd4j.graph.ExecutionMode.AUTO :
executionMode == ExecutionMode.STRICT ? org.nd4j.graph.ExecutionMode.STRICT : -1;
byte outp = outputMode == OutputMode.IMPLICIT ? org.nd4j.graph.OutputMode.IMPLICIT :
outputMode == OutputMode.EXPLICIT ? org.nd4j.graph.OutputMode.EXPLICIT :
outputMode == OutputMode.EXPLICIT_AND_IMPLICIT ? org.nd4j.graph.OutputMode.EXPLICIT_AND_IMPLICIT :
outputMode == OutputMode.VARIABLE_SPACE ? org.nd4j.graph.OutputMode.VARIABLE_SPACE : -1;
if (exec == -1)
throw new UnsupportedOperationException("Unknown values were passed into configuration as ExecutionMode: [" + executionMode + "]");
if (outp == -1)
throw new UnsupportedOperationException("Unknown values were passed into configuration as OutputMode: [" + outputMode + "]");
return FlatConfiguration.createFlatConfiguration(builder, -1, prof, exec, outp, gatherTimings, footprintForward, footprintBackward, Direction.FORWARD_ONLY);
} |
This method
@param builder
@return
| ExecutorConfiguration::getFlatConfiguration | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/conf/ExecutorConfiguration.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/execution/conf/ExecutorConfiguration.java | Apache-2.0 |
public ListenerVariables otherRequiredVariables(SameDiff sd){
return ListenerVariables.empty();
} |
Return any requested variables that are not part of the evaluations
| BaseEvaluationListener::otherRequiredVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | Apache-2.0 |
public void epochStartEvaluations(SameDiff sd, At at){
//No op
} |
See {@link Listener#epochStart(SameDiff, At)}
| BaseEvaluationListener::epochStartEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | Apache-2.0 |
public ListenerResponse epochEndEvaluations(SameDiff sd, At at, LossCurve lossCurve, long epochTimeMillis, EvaluationRecord evaluations) {
//No op
return ListenerResponse.CONTINUE;
} |
See {@link Listener#epochEnd(SameDiff, At, LossCurve, long)}, also provided the requested evaluations
| BaseEvaluationListener::epochEndEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | Apache-2.0 |
public ListenerResponse validationDoneEvaluations(SameDiff sd, At at, long validationTimeMillis, EvaluationRecord evaluations) {
//No op
return ListenerResponse.CONTINUE;
} |
See {@link Listener#validationDone(SameDiff, At, long)}, also provided the requested evaluations
| BaseEvaluationListener::validationDoneEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | Apache-2.0 |
public void activationAvailableEvaluations(SameDiff sd, At at, MultiDataSet batch, SameDiffOp op, String varName,
INDArray activation){
//No op
} |
See {@link Listener#activationAvailable(SameDiff, At, MultiDataSet, SameDiffOp, String, INDArray)}
| BaseEvaluationListener::activationAvailableEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/BaseEvaluationListener.java | Apache-2.0 |
public Map<String, List<IEvaluation>> trainEvaluations() {
return trainEvaluations;
} |
Get the requested training evaluations
| ListenerEvaluations::trainEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Map<String, Integer> trainEvaluationLabels() {
return trainEvaluationLabels;
} |
Get the label indices for the requested training evaluations
| ListenerEvaluations::trainEvaluationLabels | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Map<String, List<IEvaluation>> validationEvaluations() {
return validationEvaluations;
} |
Get the requested validation evaluations
| ListenerEvaluations::validationEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Map<String, Integer> validationEvaluationLabels() {
return validationEvaluationLabels;
} |
Get the label indices for the requested validation evaluations
| ListenerEvaluations::validationEvaluationLabels | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public ListenerVariables requiredVariables() {
return new ListenerVariables(trainEvaluations.keySet(), validationEvaluations.keySet(),
new HashSet<String>(), new HashSet<String>());
} |
Get the required variables for these evaluations
| ListenerEvaluations::requiredVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public boolean isEmpty() {
return trainEvaluations.isEmpty() && validationEvaluations.isEmpty();
} |
@return true if there are no requested evaluations
| ListenerEvaluations::isEmpty | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Builder trainEvaluation(@NonNull String variableName, int labelIndex, @NonNull IEvaluation... evaluations) {
addEvaluations(false, this.trainEvaluations, this.trainEvaluationLabels, variableName,
labelIndex, evaluations);
return this;
} |
Add requested training evaluations for a parm/variable
@param variableName The variable to evaluate
@param labelIndex The index of the label to evaluate against
@param evaluations The evaluations to run
| Builder::trainEvaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Builder trainEvaluation(@NonNull SDVariable variable, int labelIndex, @NonNull IEvaluation... evaluations) {
return trainEvaluation(variable.name(), labelIndex, evaluations);
} |
Add requested training evaluations for a parm/variable
@param variable The variable to evaluate
@param labelIndex The index of the label to evaluate against
@param evaluations The evaluations to run
| Builder::trainEvaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Builder validationEvaluation(@NonNull String variableName, int labelIndex, @NonNull IEvaluation... evaluations) {
addEvaluations(true, this.validationEvaluations, this.validationEvaluationLabels, variableName,
labelIndex, evaluations);
return this;
} |
Add requested validation evaluations for a parm/variable
@param variableName The variable to evaluate
@param labelIndex The index of the label to evaluate against
@param evaluations The evaluations to run
| Builder::validationEvaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Builder validationEvaluation(@NonNull SDVariable variable, int labelIndex, @NonNull IEvaluation... evaluations) {
return validationEvaluation(variable.name(), labelIndex, evaluations);
} |
Add requested validation evaluations for a parm/variable
@param variable The variable to evaluate
@param labelIndex The index of the label to evaluate against
@param evaluations The evaluations to run
| Builder::validationEvaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Builder addEvaluations(boolean validation, @NonNull String variableName, int labelIndex, @NonNull IEvaluation... evaluations) {
if (validation) {
return validationEvaluation(variableName, labelIndex, evaluations);
} else {
return trainEvaluation(variableName, labelIndex, evaluations);
}
} |
Add requested evaluations for a parm/variable, for either training or validation
@param validation Whether to add these evaluations as validation or training
@param variableName The variable to evaluate
@param labelIndex The index of the label to evaluate against
@param evaluations The evaluations to run
| Builder::addEvaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerEvaluations.java | Apache-2.0 |
public Set<String> trainingVariables() {
return trainingVariables;
} |
Get required training variables
| ListenerVariables::trainingVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Set<String> validationVariables() {
return validationVariables;
} |
Get required validation variables
| ListenerVariables::validationVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Set<String> evaluationVariables() {
return evaluationVariables;
} |
Get required evaluation variables
| ListenerVariables::evaluationVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Set<String> inferenceVariables() {
return inferenceVariables;
} |
Get required inference variables
| ListenerVariables::inferenceVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Set<String> requiredVariables(Operation op) {
switch (op) {
case TRAINING:
return trainingVariables;
case TRAINING_VALIDATION:
return validationVariables;
case INFERENCE:
return inferenceVariables;
case EVALUATION:
return evaluationVariables;
}
throw new IllegalArgumentException("Unknown operation " + op);
} |
Get required variables for specified op
| ListenerVariables::requiredVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public ListenerVariables merge(ListenerVariables other) {
return new ListenerVariables(
Sets.newHashSet(Sets.union(trainingVariables, other.trainingVariables)),
Sets.newHashSet(Sets.union(validationVariables, other.validationVariables)),
Sets.newHashSet(Sets.union(evaluationVariables, other.evaluationVariables)),
Sets.newHashSet(Sets.union(inferenceVariables, other.inferenceVariables)));
} |
Return a new ListenerVariables that contains the variables of this ListenerVariables and of other
| ListenerVariables::merge | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder requireVariables(@NonNull Operation op, @NonNull String... variables) {
switch (op) {
case TRAINING:
trainingVariables.addAll(Arrays.asList(variables));
break;
case TRAINING_VALIDATION:
validationVariables.addAll(Arrays.asList(variables));
break;
case INFERENCE:
inferenceVariables.addAll(Arrays.asList(variables));
break;
case EVALUATION:
evaluationVariables.addAll(Arrays.asList(variables));
break;
}
return this;
} |
Add required variables for the specified op
@param op The op to require the variable for
| Builder::requireVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder requireVariables(@NonNull Operation op, @NonNull SDVariable... variables) {
String[] names = new String[variables.length];
for (int i = 0; i < variables.length; i++)
names[i] = variables[i].name();
return requireVariables(op, names);
} |
Add required variables for the specified op
@param op The op to require the variable for
| Builder::requireVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder trainingVariables(@NonNull String... variables) {
return requireVariables(Operation.TRAINING, variables);
} |
Add required variables for training
| Builder::trainingVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder trainingVariables(@NonNull SDVariable... variables) {
return requireVariables(Operation.TRAINING, variables);
} |
Add required variables for training
| Builder::trainingVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder validationVariables(@NonNull String... variables) {
return requireVariables(Operation.TRAINING_VALIDATION, variables);
} |
Add required variables for validation
| Builder::validationVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder validationVariables(@NonNull SDVariable... variables) {
return requireVariables(Operation.TRAINING_VALIDATION, variables);
} |
Add required variables for validation
| Builder::validationVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder inferenceVariables(@NonNull String... variables) {
return requireVariables(Operation.INFERENCE, variables);
} |
Add required variables for inference
| Builder::inferenceVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder inferenceVariables(@NonNull SDVariable... variables) {
return requireVariables(Operation.INFERENCE, variables);
} |
Add required variables for inference
| Builder::inferenceVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder evaluationVariables(@NonNull String... variables) {
return requireVariables(Operation.EVALUATION, variables);
} |
Add required variables for evaluation
| Builder::evaluationVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Builder evaluationVariables(@NonNull SDVariable... variables) {
return requireVariables(Operation.EVALUATION, variables);
} |
Add required variables for evaluation
| Builder::evaluationVariables | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/ListenerVariables.java | Apache-2.0 |
public Loss(@NonNull List<String> lossNames, @NonNull double[] losses) {
Preconditions.checkState(lossNames.size() == losses.length, "Expected equal number of loss names and loss values");
this.lossNames = lossNames;
this.losses = losses;
} |
@param lossNames Names of the losses
@param losses Values for each loss. Must be same length as lossNames
| Loss::Loss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | Apache-2.0 |
public int numLosses() {
return lossNames.size();
} |
@return Number of loss values (i.e., length of lossNames and losses)
| Loss::numLosses | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | Apache-2.0 |
public List<String> lossNames() {
return lossNames;
} |
@return Names of all of the loss components
| Loss::lossNames | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | Apache-2.0 |
public double[] lossValues() {
return losses;
} |
@return Values corresponding to each of the losses (same order as lossNames())
| Loss::lossValues | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | Apache-2.0 |
public double getLoss(@NonNull String lossName) {
int idx = lossNames.indexOf(lossName);
Preconditions.checkState(idx >= 0, "No loss with name \"%s\" exists. All loss names: %s", lossName, lossNames);
return losses[idx];
} |
Get the specified loss by name
@param lossName Name of the loss (must exist)
@return Specified loss value
| Loss::getLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | Apache-2.0 |
public double totalLoss() {
double sum = 0.0;
for (double d : losses) {
sum += d;
}
return sum;
} |
@return The total loss (sum of all loss components)
| Loss::totalLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/Loss.java | Apache-2.0 |
public static At defaultAt(){
return new At(0, 0, 0, 0, null, Operation.INFERENCE);
} |
@return A new instance with everything set to 0, and operation set to INFERENCE
| At::defaultAt | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public static At defaultAt(@NonNull Operation op){
return new At(0, 0, 0, 0, null, op);
} |
@param op Operation
@return A new instance with everything set to 0, except for the specified operation
| At::defaultAt | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public int epoch(){
return epoch;
} |
@return The current training epoch
| At::epoch | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public int iteration(){
return iteration;
} |
@return The current training iteration
| At::iteration | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public int trainingThreadNum(){
return trainingThreadNum;
} |
@return The number of the SameDiff thread
| At::trainingThreadNum | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public long javaThreadNum(){
return javaThreadNum;
} |
@return The Java/JVM thread number for training
| At::javaThreadNum | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public Operation operation(){
return operation;
} |
@return The current operation
| At::operation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public At copy(){
return new At(epoch, iteration, trainingThreadNum, javaThreadNum, frameIter, operation);
} |
@return A copy of the current At instance
| At::copy | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public At copy(Operation operation){
return new At(epoch, iteration, trainingThreadNum, javaThreadNum, frameIter, operation);
} |
@param operation Operation to set in the new instance
@return A copy of the current instance, but with the specified operation
| At::copy | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java | Apache-2.0 |
public static ObjectMapper jsonMapper() {
ObjectMapper json = new ObjectMapper();
json.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
json.configure(SerializationFeature.FAIL_ON_EMPTY_BEANS, false);
json.configure(MapperFeature.SORT_PROPERTIES_ALPHABETICALLY, false);
json.disable(SerializationFeature.INDENT_OUTPUT); //One line
return json;
} |
Get a new JSON mapper for use in serializing/deserializing JSON format
| ProfilingListener::jsonMapper | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public static Builder builder(File outputFile) {
return new Builder(outputFile);
} |
Create a new builder
@param outputFile Output file. Will be overwritten if file already exists
| ProfilingListener::builder | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public Builder recordAll() {
this.all = true;
this.nIter = -1;
this.nMs = -1;
return this;
} |
If called, all data will be profiled with no limits (other than a warmup, if set)
| Builder::recordAll | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public Builder warmup(int iterations) {
this.warmup = iterations;
return this;
} |
Specify the number of warmup iterations - i.e., these will be excluded from profiling results
| Builder::warmup | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public Builder maxProfileIterations(int iterations) {
this.nIter = iterations;
this.all = false;
return this;
} |
Set a limit on the maximum number of iterations to profile (after warmup, if any).
Any ops executed after the specified number of iterations will not be profiled/recorded
| Builder::maxProfileIterations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public Builder maxProfilerMilliseconds(long ms) {
this.nMs = ms;
this.all = false;
return this;
} |
Set a limit on the maximum duration for profiling, in milliseconds.
Any ops executed after the specified amount of time since the first (non-warmup) operation start will not be
profiled/recorded
| Builder::maxProfilerMilliseconds | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public Builder operations(Operation... operations) {
this.operations = operations;
return this;
} |
Specify the operations (training, inference, etc) to profile.
If not set, all operations are profiled
| Builder::operations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public ProfilingListener build() {
return new ProfilingListener(outputFile, all, warmup, nIter, nMs, operations);
} |
Create the profiling listener
| Builder::build | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java | Apache-2.0 |
public OpBenchmarkListener(Operation operation, @NonNull Mode mode, long minRuntime) {
this.operation = operation;
this.mode = mode;
this.minRuntime = minRuntime;
} |
@param operation Operation to collect stats for
@param mode Mode - see {@link OpBenchmarkListener}
@param minRuntime Minimum runtime - only applies to Mode.SINGLE_ITER_PRINT. If op runtime below this: don't print
| Mode::OpBenchmarkListener | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/debugging/OpBenchmarkListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/debugging/OpBenchmarkListener.java | Apache-2.0 |
public ExecDebuggingListener(PrintMode printMode, int maxIterations, boolean logIter){
this.printMode = printMode;
this.maxIterations = maxIterations;
this.logIter = logIter;
} |
@param printMode Print mode, see {@link PrintMode}
@param maxIterations Maximum number of iterations to print. <= 0 for "all iterations"
@param logIter If true: prefix iteration/epoch, such as "(iter=1,epoch=0,op=3)" to the output
| PrintMode::ExecDebuggingListener | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/debugging/ExecDebuggingListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/debugging/ExecDebuggingListener.java | Apache-2.0 |
public List<EvaluationRecord> trainingEval(){
return trainingHistory;
} |
Get the training evaluations
| History::trainingEval | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
public List<EvaluationRecord> validationEval(){
return validationHistory;
} |
Get the validation evaluations
| History::validationEval | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
public LossCurve lossCurve(){
return lossCurve;
} |
Get the loss curve
| History::lossCurve | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
public long trainingTimeMillis(){
return trainingTimeMillis;
} |
Get the total training time, in milliseconds
| History::trainingTimeMillis | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
public List<Long> validationTimesMillis(){
return validationTimesMillis;
} |
Get the total validation time, in milliseconds
| History::validationTimesMillis | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
public int trainingEpochs(){
return trainingHistory.size();
} |
Get the number of epochs trained for
| History::trainingEpochs | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
public int validationEpochs(){
return validationHistory.size();
} |
Get the number of epochs validation was ran on
| History::validationEpochs | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java | Apache-2.0 |
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