code
stringlengths 11
173k
| docstring
stringlengths 2
593k
| func_name
stringlengths 2
189
| language
stringclasses 1
value | repo
stringclasses 833
values | path
stringlengths 11
294
| url
stringlengths 60
339
| license
stringclasses 4
values |
|---|---|---|---|---|---|---|---|
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 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 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 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 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 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 |
public List<Double> trainingEval(String param, IMetric metric) {
List<Double> data = new ArrayList<>();
for(EvaluationRecord er : trainingHistory)
data.add(er.getValue(param, metric));
return data;
} |
Get the results of a training evaluation on a given parameter for a given metric
Only works if there is only one evaluation with the given metric for param
| 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<Double> trainingEval(String param, int index, IMetric metric) {
List<Double> data = new ArrayList<>();
for(EvaluationRecord er : trainingHistory)
data.add(er.getValue(param, index, metric));
return data;
} |
Get the results of a training evaluation on a given parameter at a given index, for a given metric
Note that it returns all recorded evaluations.
Index determines the evaluation used not the epoch's results to return.
| 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<Double> trainingEval(IMetric metric) {
List<Double> data = new ArrayList<>();
for(EvaluationRecord er : trainingHistory)
data.add(er.getValue(metric));
return data;
} |
Get the results of a training evaluation for a given metric
Only works if there is only one evaluation with the given metric
| 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<IEvaluation> trainingEval(String param) {
List<IEvaluation> data = new ArrayList<>();
for(EvaluationRecord er : trainingHistory)
data.add(er.evaluation(param));
return data;
} |
Get the results of a training evaluation on a given parameter
Only works if there is only one evaluation for param.
| 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<IEvaluation> trainingEval(String param, int index) {
List<IEvaluation> data = new ArrayList<>();
for(EvaluationRecord er : trainingHistory)
data.add(er.evaluation(param, index));
return data;
} |
Get the results of a training evaluation on a given parameter at a given index
Note that it returns all recorded evaluations.
Index determines the evaluation used not the epoch's results to return.
| 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<Double> validationEval(String param, IMetric metric) {
List<Double> data = new ArrayList<>();
for(EvaluationRecord er : validationHistory)
data.add(er.getValue(param, metric));
return data;
} |
Get the results of a validation evaluation on a given parameter for a given metric
Only works if there is only one evaluation with the given metric for param
| 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 List<Double> validationEval(String param, int index, IMetric metric) {
List<Double> data = new ArrayList<>();
for(EvaluationRecord er : validationHistory)
data.add(er.getValue(param, index, metric));
return data;
} |
Get the results of a validation evaluation on a given parameter at a given index, for a given metric
Note that it returns all recorded evaluations.
Index determines the evaluation used not the epoch's results to return.
| 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 List<Double> validationEval(IMetric metric) {
List<Double> data = new ArrayList<>();
for(EvaluationRecord er : validationHistory)
data.add(er.getValue(metric));
return data;
} |
Get the results of a validation evaluation for a given metric
Only works if there is only one evaluation with the given metric
| 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 List<IEvaluation> validationEval(String param) {
List<IEvaluation> data = new ArrayList<>();
for(EvaluationRecord er : validationHistory)
data.add(er.evaluation(param));
return data;
} |
Get the results of a validation evaluation on a given parameter
Only works if there is only one evaluation for param.
| 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 List<IEvaluation> validationEval(String param, int index) {
List<IEvaluation> data = new ArrayList<>();
for(EvaluationRecord er : validationHistory)
data.add(er.evaluation(param, index));
return data;
} |
Get the results of a validation evaluation on a given parameter at a given index
Note that it returns all recorded evaluations.
Index determines the evaluation used not the epoch's results to return.
| 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 EvaluationRecord finalTrainingEvaluations() {
Preconditions.checkState(!trainingHistory.isEmpty(), "Cannot get final training evaluation - history is empty");
return trainingHistory.get(trainingHistory.size() - 1);
} |
Gets the training evaluations ran during the last epoch
| History::finalTrainingEvaluations | 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 EvaluationRecord finalValidationEvaluations() {
Preconditions.checkState(!validationHistory.isEmpty(), "Cannot get final validation evaluation - history is empty");
return validationHistory.get(validationHistory.size() - 1);
} |
Gets the validation evaluations ran during the last epoch
| History::finalValidationEvaluations | 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 EvaluationRecord trainingEvaluations(int epoch) {
if(epoch >= 0){
return trainingHistory.get(epoch);
} else {
return trainingHistory.get(trainingHistory.size() - epoch);
}
} |
Gets the evaluation record for a given epoch.
@param epoch The epoch to get results for. If negative, returns results for the epoch that many epochs from the end.
| History::trainingEvaluations | 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 Map<String, List<IEvaluation>> evaluations() {
return evaluations;
} |
Get all evaluations
| EvaluationRecord::evaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public List<IEvaluation> evaluations(String param) {
Preconditions.checkArgument(evaluations.containsKey(param),
"No evaluations for %s.", param);
return evaluations.get(param);
} |
Get evaluations for a given param/variable
@param param The target param/variable
| EvaluationRecord::evaluations | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public IEvaluation evaluation(String param, int index) {
return evaluations(param).get(index);
} |
Get the evaluation for param at the specified index
| EvaluationRecord::evaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public <T extends IEvaluation> T evaluation(String param) {
Preconditions.checkArgument(evaluations.containsKey(param),
"No evaluations for %s.", param);
Preconditions.checkArgument(evaluations.get(param).size() == 1,
"Multiple evaluations for %s. Use evaluations().", param);
return (T) evaluations.get(param).get(0);
} |
Get the evaluation for a given param/variable
<p>
Will throw an exception if there are more than one or no evaluations for the param
@param param The target param/variable
| EvaluationRecord::evaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public <T extends IEvaluation<T>> T evaluation(Class<T> evalClass) {
Preconditions.checkArgument(classEvaluations.containsKey(evalClass),
"Can't get evaluation for %s. Either no evaluations with that class are present, or more than one are.", evalClass);
return (T) classEvaluations.get(evalClass);
} |
Get the evaluation of a given type
<p>
Will throw an exception if there are more than one or no evaluations of that type
@param evalClass The type of evaluation to look for
| EvaluationRecord::evaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public <T extends IEvaluation<T>> T evaluation(String param, Class<T> evalClass) {
Collection<IEvaluation> evals = Collections2.filter(evaluations(param), Predicates.instanceOf(evalClass));
Preconditions.checkArgument(evals.size() == 1, "Multiple or no evaluations of type %s for param %s.", evalClass, param);
return (T) evals.iterator().next();
} |
Get the evaluation of a given type, for a given param/variable
<p>
Will throw an exception if there are more than one or no evaluations of that type for the given param
@param param The target param/variable
@param evalClass The type of evaluation to look for
| EvaluationRecord::evaluation | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public double getValue(IMetric metric) {
return evaluation(metric.getEvaluationClass()).getValue(metric);
} |
Get the metric's value for the evaluation of the metric's type
<p>
Will throw an exception if there are more than one or no evaluations of that type
@param metric The metric to calculate
| EvaluationRecord::getValue | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public double getValue(String param, IMetric metric) {
return evaluation(param, metric.getEvaluationClass()).getValue(metric);
} |
Get the metric's value for the evaluation of the metric's type, for a given param/variable
<p>
Will throw an exception if there are more than one or no evaluations of that type for the given param
@param param The target param/variable
@param metric The metric to calculate
| EvaluationRecord::getValue | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public double getValue(String param, int index, IMetric metric) {
return evaluation(param, index).getValue(metric);
} |
Get the metric's value for the evaluation for a given param/variable at the given index
<p>
Will throw an exception if the target evaluation doesn't support the given metric
@param param The target param/variable
@param index The index of the target evaluation on the param
@param metric The metric to calculate
| EvaluationRecord::getValue | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java | Apache-2.0 |
public Loss meanLoss(int epoch){
if(epoch >= 0){
return new Loss(lossNames, lossValues.getRow(epoch).toDoubleVector());
} else {
return new Loss(lossNames, lossValues.getRow(lossValues.rows() + epoch).toDoubleVector());
}
} |
Get the mean loss for a given epoch
If epoch is negative, counts backwards from the end.
E.g. losses(-1) gets the last epoch.
@param epoch The epoch to get. If negative, returns results for the epoch that many epochs from the end
| LossCurve::meanLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public Loss lastMeanLoss(){
return meanLoss(-1);
} |
Get the mean loss for the last epoch.
| LossCurve::lastMeanLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public float[] meanLoss(@NonNull String lossName){
int idx = lossNames.indexOf(lossName);
Preconditions.checkArgument(idx >= 0, "No loss value for %s. Existing losses: %s", lossName, lossNames);
float[] loss = new float[(int) lossValues.size(0)];
for(int i = 0 ; i < lossValues.size(0) ; i++){
loss[i] = lossValues.getFloat(i, idx);
}
return loss;
} |
Return all mean loss values for a given variable
| LossCurve::meanLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public float meanLoss(@NonNull String lossName, int epoch){
int idx = lossNames.indexOf(lossName);
Preconditions.checkArgument(idx >= 0, "No loss value for %s. Existing losses: %s", lossName, lossNames);
if(epoch >= 0) {
return lossValues.getFloat(epoch, idx);
} else {
return lossValues.getFloat(lossValues.rows() + epoch, idx);
}
} |
Return the mean loss value for a given variable on a given epoch.
See {@link #meanLoss(int)}
| LossCurve::meanLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public float lastMeanLoss(@NonNull String lossName){
int idx = lossNames.indexOf(lossName);
Preconditions.checkArgument(idx >= 0, "No loss value for %s. Existing losses: %s", lossName, lossNames);
return lossValues.getFloat(lossValues.rows() - 1, idx);
} |
Return the mean loss value for a given variable on the last epoch.
| LossCurve::lastMeanLoss | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public Loss lastMeanDelta(){
return lastMeanLoss().sub(meanLoss(-2));
} |
Return the loss delta between the last epoch and the one before it.
Equivalent to meanLoss(-1) - meanLoss(-2).
A positive delta means the loss is increasing, and a negative delta means it is decreasing.
| LossCurve::lastMeanDelta | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public double lastMeanDelta(String lossName){
return lastMeanDelta().getLoss(lossName);
} |
Return the loss delta between the last epoch and the one before it, for a given variable.
Equivalent to meanLoss(-1) - meanLoss(-2).
A positive delta means the loss is increasing, and a negative delta means it is decreasing.
| LossCurve::lastMeanDelta | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public LossCurve addLossAndCopy(Loss loss){
return addLossAndCopy(loss.getLosses(), loss.lossNames());
} |
Return a new LossCurve with the given losses added on as the most recent epoch
| LossCurve::addLossAndCopy | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java | Apache-2.0 |
public ScoreListener() {
this(10, true);
} |
Create a ScoreListener reporting every 10 iterations, and at the end of each epoch
| ScoreListener::ScoreListener | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/impl/ScoreListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/impl/ScoreListener.java | Apache-2.0 |
public ScoreListener(int frequency) {
this(frequency, true);
} |
Create a ScoreListener reporting every N iterations, and at the end of each epoch
| ScoreListener::ScoreListener | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/impl/ScoreListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/impl/ScoreListener.java | Apache-2.0 |
public ScoreListener(int frequency, boolean reportEpochs) {
this(frequency, reportEpochs, true);
} |
Create a ScoreListener reporting every N iterations, and optionally at the end of each epoch
| ScoreListener::ScoreListener | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/impl/ScoreListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/impl/ScoreListener.java | Apache-2.0 |
public List<Checkpoint> availableCheckpoints(){
if(!checkpointRecordFile.exists()){
return Collections.emptyList();
}
return availableCheckpoints(rootDir);
} |
List all available checkpoints. A checkpoint is 'available' if the file can be loaded. Any checkpoint files that
have been automatically deleted (given the configuration) will not be returned here.
@return List of checkpoint files that can be loaded
| KeepMode::availableCheckpoints | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public static List<Checkpoint> availableCheckpoints(File directory){
File checkpointRecordFile = new File(directory, "checkpointInfo.txt");
Preconditions.checkState(checkpointRecordFile.exists(), "Could not find checkpoint record file at expected path %s", checkpointRecordFile.getAbsolutePath());
List<String> lines;
try(InputStream is = new BufferedInputStream(new FileInputStream(checkpointRecordFile))){
lines = IOUtils.readLines(is);
} catch (IOException e){
throw new RuntimeException("Error loading checkpoint data from file: " + checkpointRecordFile.getAbsolutePath(), e);
}
List<Checkpoint> out = new ArrayList<>(lines.size()-1); //Assume first line is header
for( int i=1; i<lines.size(); i++ ){
Checkpoint c = Checkpoint.fromFileString(lines.get(i));
if(new File(directory, c.getFilename()).exists()){
out.add(c);
}
}
return out;
} |
List all available checkpoints. A checkpoint is 'available' if the file can be loaded. Any checkpoint files that
have been automatically deleted (given the configuration) will not be returned here.
Note that the checkpointInfo.txt file must exist, as this stores checkpoint information
@return List of checkpoint files that can be loaded from the specified directory
| KeepMode::availableCheckpoints | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public Checkpoint lastCheckpoint(){
if(!checkpointRecordFile.exists()){
return null;
}
return lastCheckpoint(rootDir);
} |
Return the most recent checkpoint, if one exists - otherwise returns null
@return Checkpoint
| KeepMode::lastCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public static Checkpoint lastCheckpoint(File rootDir){
List<Checkpoint> all = availableCheckpoints(rootDir);
if(all.isEmpty()){
return null;
}
return all.get(all.size()-1);
} |
Return the most recent checkpoint, if one exists - otherwise returns null
@param rootDir Root direcotry for the checkpoint files
@return Checkpoint
| KeepMode::lastCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public File getFileForCheckpoint(Checkpoint checkpoint){
return getFileForCheckpoint(checkpoint.getCheckpointNum());
} |
Get the model file for the given checkpoint. Checkpoint model file must exist
@param checkpoint Checkpoint to get the model file for
@return Model file for the checkpoint
| KeepMode::getFileForCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public File getFileForCheckpoint(int checkpointNum) {
return getFileForCheckpoint(rootDir, checkpointNum);
} |
Get the model file for the given checkpoint number. Checkpoint model file must exist
@param checkpointNum Checkpoint number to get the model file for
@return Model file for the checkpoint
| KeepMode::getFileForCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public SameDiff loadCheckpoint(int checkpointNum, boolean loadUpdaterState){
return loadCheckpoint(rootDir, checkpointNum, loadUpdaterState);
} |
Load a given checkpoint number
@param loadUpdaterState If true: load the updater state. See {@link SameDiff#load(File, boolean)} for more details
| KeepMode::loadCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public static SameDiff loadCheckpoint(File rootDir, int checkpointNum, boolean loadUpdaterState) {
File f = getFileForCheckpoint(rootDir, checkpointNum);
return SameDiff.load(f, loadUpdaterState);
} |
Load a SameDiff instance for the given checkpoint that resides in the specified root directory
@param rootDir Directory that the checkpoint resides in
@param checkpointNum Checkpoint model number to load
@param loadUpdaterState If true: load the updater state. See {@link SameDiff#load(File, boolean)} for more details
@return The loaded model
| KeepMode::loadCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public static SameDiff loadLastCheckpoint(File rootDir, boolean loadUpdaterState){
Checkpoint last = lastCheckpoint(rootDir);
return loadCheckpoint(rootDir, last.getCheckpointNum(), loadUpdaterState);
} |
Load the last (most recent) checkpoint from the specified root directory
@param rootDir Root directory to load checpoint from
@return ComputationGraph for last checkpoint
| KeepMode::loadLastCheckpoint | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public Builder saveUpdaterState(boolean saveUpdaterState){
this.saveUpdaterState = saveUpdaterState;
return this;
} |
Whether the updater state (history/state for Adam, Nesterov momentum, etc) should be saved with each checkpoint.<br>
Updater state is saved by default.
If you expect to continue training on any of the checkpoints, this should be set to true. However, it will increase
the file size.
@param saveUpdaterState If true: updater state will be saved with checkpoints. False: not saved.
| Builder::saveUpdaterState | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/checkpoint/CheckpointListener.java | Apache-2.0 |
public DifferentialFunction(SameDiff sameDiff,NodeDef nodeDef, Map<String, AttrValue> attributesForNode, GraphDef graph) {
this.sameDiff = sameDiff;
setInstanceId();
initFromTensorFlow(nodeDef, sameDiff,attributesForNode ,graph);
recordCreation();
} |
Initialize the function from the given
{@link NodeDef}
@param nodeDef
| DifferentialFunction::DifferentialFunction | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunction.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunction.java | Apache-2.0 |
public DifferentialFunction(SameDiff sameDiff, Onnx.NodeProto node, Map<String, Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph) {
this.sameDiff = sameDiff;
setInstanceId();
initFromOnnx(node, sameDiff, attributesForNode, graph);
recordCreation();
} |
Initialize the function from the given
{@link Onnx.NodeProto}
@param node
| DifferentialFunction::DifferentialFunction | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunction.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunction.java | Apache-2.0 |
public static SDVariable[] initializeLoopBody(String[] namesToUse,SameDiff loopBody,int maxIterations) {
Preconditions.checkState( namesToUse != null && namesToUse.length == 2,"Number of input names must be 2.");
SDVariable[] ret = new SDVariable[] {
loopBody.constant(namesToUse[1], maxIterations),
loopBody.var(namesToUse[0], Nd4j.zeros(1)),
};
return ret;
} |
Initializes the loop variables with default parameters. The variables are as follows:
current iteration
max number of iterations
extra condition to use
The passed in variable names will be assumed to be names for each of these variables
mentioned above respectively. Please ensure that these are the intended names
of the variables.
@param namesToUse the names of the variables to use. Must be length 2.
@param loopBody the loop body to initialize
@param maxIterations the max iterations to iterate over
| ControlFlow::initializeLoopBody | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SDVariable[] initializeLoopBody(String[] namesToUse,SameDiff loopBody,int maxIterations,boolean extraCond) {
Preconditions.checkState( namesToUse != null && namesToUse.length == 3,"Number of input names must be 3.");
SDVariable[] ret = new SDVariable[] {
loopBody.var(namesToUse[0], Nd4j.zeros(1)),
loopBody.constant(namesToUse[1], maxIterations),
loopBody.constant(namesToUse[2], extraCond)
};
return ret;
} |
Initializes the loop variables with default parameters. The variables are as follows:
current iteration
max number of iterations
extra condition to use
The passed in variable names will be assumed to be names for each of these variables
mentioned above respectively. Please ensure that these are the intended names
of the variables.
@param namesToUse the names of the variables to use. Must be length 3.
@param loopBody the loop body to initialize
@param maxIterations the max iterations to iterate over
@param extraCond the extra condition to use
| ControlFlow::initializeLoopBody | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SDVariable[] args(SDVariable maxIterations,SDVariable condIn,SDVariable startIterations,SDVariable[] extraArgs) {
return LoopArgs.builder().extraArgs(extraArgs)
.condIn(condIn)
.maxIters(maxIterations)
.startIter(startIterations).build().toArgs();
} |
Create the arguments used in {@link #condBody()}
and {@link #loopWithConditions(String[], String, SameDiff, SameDiff, String, SDVariable[], String[], String[])}
@param maxIterations the max number of iterations
@param condIn the input conditions
@param startIterations the start iterations
@param extraArgs the extra arguments for the user
@return the ordered arguments
| ControlFlow::args | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SDVariable ifCond(SameDiff sameDiff,String outputName, String ifName, @NonNull SameDiffNoArgSingleLambda cond,
@NonNull SameDiffNoArgSingleLambda trueBody, @NonNull SameDiffNoArgSingleLambda falseBody){
ifName = sameDiff.newBlockName(ifName == null ? "if" : ifName);
NameScope ifScope = sameDiff.withNameScope(ifName);
NameScope condScope = sameDiff.withNameScope("cond");
final SDVariable pred = cond.define(sameDiff);
condScope.close();
if (pred.dataType() != DataType.BOOL) {
//cleanup partially added block
for(SDVariable v : sameDiff.getVariablesInScope(ifScope))
sameDiff.getVariables().remove(v.name());
for(SameDiffOp op : sameDiff.getOpsInScope(ifScope)) {
for(String in : op.getInputsToOp()){
sameDiff.removeArgFromOp(in, op.getOp());
}
sameDiff.getOps().remove(op.getName());
}
throw new IllegalStateException("Can not use " + pred.name()
+ " as the condition of an If statement, the condition must be a boolean.");
}
final Map<String, SDVariable[]> switches = new HashMap<>();
final Set<String> declared = Sets.newHashSet(sameDiff.variableMap().keySet());
sameDiff.addArgumentInterceptor(argument -> {
if(argument == null)
return null;
// if its declared in the if, we don't care about it
if(declared == null || !declared.contains(argument.name()))
return argument;
// if we've already added a switch, move on
if(switches.containsKey(argument.name()))
return switches.get(argument.name())[1];
SDVariable[] s = sameDiff.switchOp(argument, pred);
switches.put(argument.name(), s);
return s[1];
});
NameScope trueScope = sameDiff.withNameScope("trueBody");
SDVariable trueOut = trueBody.define(sameDiff);
sameDiff.removeArgumentInterceptor();
if(declared.contains(trueOut.name())) {
SDVariable[] s = sameDiff.switchOp(trueOut, pred);
switches.put(trueOut.name(), s);
trueOut = s[1];
}
trueScope.close();
final Set<String> declared2 = Sets.newHashSet(sameDiff.variableMap().keySet());
sameDiff.addArgumentInterceptor(argument -> {
// if its declared in the if, we don't care about it
if(!declared2.contains(argument.name()))
return argument;
// if we've already added a switch, move on
if(switches.containsKey(argument.name()))
return switches.get(argument.name())[0];
SDVariable[] s = sameDiff.switchOp(argument, pred);
switches.put(argument.name(), s);
return s[0];
});
NameScope falseScope = sameDiff.withNameScope("falseBody");
SDVariable falseOut = falseBody.define(sameDiff);
sameDiff.removeArgumentInterceptor();
if(declared2.contains(falseOut.name())) {
SDVariable[] s = sameDiff.switchOp(falseOut, pred);
switches.put(falseOut.name(), s);
falseOut = s[0];
}
falseScope.close();
SDVariable output = sameDiff.merge(trueOut, falseOut);
ifScope.close();
return sameDiff.updateVariableNameAndReference(output, outputName);
} |
Constructs a If statement using the tensorflow style control flow operations (Switch and Merge)
If the result of cond is true, returns the result of trueBody, otherwise returns the result of falseBody
Note that cond and body lambdas are only called once to construct the graph. The constructed graph is used to evaluate.
See <a href="http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf">Tensorflow Control Flow Implementation</a>
@param outputName Name to give the output variable. If null, doesn't rename
@param ifName The name of the if block. If null, uses "if"
@param cond A lambda evaluating to the if condition
@param trueBody A lambda to be executed if cond is true (the if block)
@param falseBody A lambda to be executed if cond is false (the else block)
@return The value of trueBody if cond is true, or falseBody if it isn't
| ControlFlow::ifCond | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SDVariable[] loopWithConditions(LoopParams loopParams) {
return loopWithConditions(loopParams.outputVarNames,
loopParams.loopName,loopParams.parent,
loopParams.functionBody,
loopParams.functionName,
loopParams.loopVars,
loopParams.functionBodyInputs,
loopParams.functionBodyOutputs);
} |
A simplified function using {@link LoopParams}
invoking the same function as {@link #loopWithConditions(String[], String, SameDiff, SameDiff, String, SDVariable[], String[], String[])}
@param loopParams the loop parameters to use
@return
| LoopParams::loopWithConditions | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SDVariable[] loopWithConditions(
String[] outputVarNames,
String loopName,
SameDiff parent,
SameDiff functionBody,
String functionName,
SDVariable[] loopVars,
String[] functionBodyInputs,
String[] functionBodyOutputs) {
Preconditions.checkState(functionBodyInputs != null && functionBodyOutputs != null && functionBodyInputs.length == functionBodyOutputs.length,"Sub graph input and output names must be defined and equal in length.");
Preconditions.checkState(loopVars.length == functionBodyInputs.length,"Loop variables and function body inputs must be equal in length.");
for(SDVariable variable : loopVars) {
if(variable.getSameDiff() != parent) {
throw new IllegalArgumentException("Variable named " + variable.name() + " does not have correct samediff instance. Must have parent outer samediff instance.");
}
}
SameDiffSingleLambda cond = condBody();
SameDiffLambda loopBody = loopBody(parent,functionBody,functionName,functionBodyInputs,functionBodyOutputs);
return parent.whileLoop(outputVarNames,loopName,loopVars,cond,loopBody);
} |
Loop with conditions allows a user to provide a lambda to invoke
any number of times.
@param outputVarNames the output variable names to use
@param loopName the name of the loop to use when creating the variables/ops
@param parent the parent samediff instance to put the loop
@param functionBody the function body to use
@param functionName the name of the function to use within the samediff instance
@param loopVars the loop variables to use during execution
@param functionBodyInputs the inputs to invoke the function with
@param functionBodyOutputs the outputs to be retrieved from the function itself
@return the output exit variables at the end of the loop
| LoopParams::loopWithConditions | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static LoopLambdaArgs argsFromInputs(SDVariable[] inputs) {
SDVariable[] extraArgs = inputs.length > 3 ? new SDVariable[inputs.length - 3] : new SDVariable[0];
//add extra arguments offset by 3 representing custom inputs
if(extraArgs.length > 0) {
for(int i = 0; i < extraArgs.length; i++) {
extraArgs[i] = inputs[i + 3];
}
}
return LoopLambdaArgs.builder()
.iterCount(inputs[1])
.iterStart(inputs[0])
.condIn(inputs[2])
.extraArgs(extraArgs)
.build();
} |
Create {@link LoopLambdaArgs} from the given arguments.
This is used to properly order arguments for use with {@link #loopBody(SameDiff, SameDiff, String, String[], String[])}
and {@link #condBody()}
@param inputs the inputs to order, these generally should be from within a lambda. The first 3 arguments are:
current iter count, maximum number of iterations, extra arguments if any
@return
| LoopParams::argsFromInputs | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public Invoke.InvokeParams invokeParams(String functionName,String[] subGraphInputNames,String[] subGraphOutputNames) {
List<SDVariable> inputs = new ArrayList<>();
//starting iteration
inputs.add(iterStart);
//ending iteration
inputs.add(iterCount);
//user custom condition
inputs.add(condIn);
inputs.addAll(Arrays.asList(extraArgs));
return Invoke.InvokeParams.builder()
.functionName(functionName)
.inputs(inputs.toArray(new SDVariable[inputs.size()]))
.subGraphInputVarNames(subGraphInputNames)
.subGraphOutputVarNames(subGraphOutputNames)
.inputVarNames(inputs.stream().map(input ->
input.name()).collect(Collectors.toList())
.toArray(new String[inputs.size()]))
.build();
} |
Construct {@link org.nd4j.linalg.api.ops.custom.Invoke.InvokeParams}
for usage with {@link SameDiff#invoke(Invoke.InvokeParams)}
the variables here reflect what is used in the loop.
A user can use {@link LoopLambdaArgs} to create an appropriately configured
{@link org.nd4j.linalg.api.ops.custom.Invoke.InvokeParams} to be used
with the body.
@param functionName the name of the function to invoke
@param subGraphInputNames the subgraph input names to invoke the function with
@param subGraphOutputNames the subgraph output names to expect returned from the function
@return the appropriate invoke parameters for use with {@link #condBody()} and {@link #loopBody(SameDiff, SameDiff, String, String[], String[])}
| LoopLambdaArgs::invokeParams | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SameDiffLambda loopBody(SameDiff parent,
SameDiff functionBody,
String functionName,
String[] subGraphInputNames,
String[] subGraphOutputNames) {
Preconditions.checkState(subGraphInputNames != null && subGraphOutputNames != null && subGraphInputNames.length == subGraphOutputNames.length,"Sub graph input and output names must be defined and equal in length.");
if(parent.getFunction(functionName) == null)
parent.putSubFunction(functionName,functionBody);
return (sameDiff, inputs) -> {
LoopLambdaArgs loopLambdaArgs = ControlFlow.argsFromInputs(inputs);
Invoke.InvokeParams invokeParams = loopLambdaArgs.invokeParams(functionName, subGraphInputNames, subGraphOutputNames);
SDVariable[] invoke = sameDiff.invoke(invokeParams);
List<SDVariable> retList = new ArrayList<>();
//current iterations + 1 (each time the body is invoked update the current iteration)
retList.add(inputs[0].add(1.0));
retList.add(inputs[1]);
retList.add(invoke[2]);
//assign extra parameters to the invoke output
//loop over non condition out variables starting from the end
for(int i = 3; i < invoke.length; i++) {
retList.add(invoke[i]);
}
return retList.toArray(new SDVariable[retList.size()]);
};
} |
Create a {@link SameDiffLambda} to be used in combination with
{@link #condBody()} and {@link SameDiff#invoke(Invoke.InvokeParams)}
this lambda will use samediff invoke as the function bdoy
and setup the appropriate parameters to create a looping construct
as described in {@link #loopBody(SameDiff, SameDiff, String, String[], String[])}
@param parent
@param functionBody
@param functionName
@param subGraphInputNames the subgraph input names for use to invoke the graph with
@param subGraphOutputNames the subgraph output names to expect to be returned from the subgraph invoke
@return
| LoopLambdaArgs::loopBody | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SDVariable[] whileLoop(SameDiff sameDiff, String[] outputNames, final String loopName, @NonNull SDVariable[] loopVars,
@NonNull SameDiffSingleLambda cond, @NonNull SameDiffLambda body) {
final String frameName = sameDiff.newBlockName(loopName == null ? "while" : loopName);
NameScope loopScope = sameDiff.withNameScope(frameName);
SDVariable counter = sameDiff.scalar(sameDiff.generateNewVarName("counter", 0), 0);
SDVariable[] entered = new SDVariable[loopVars.length];
for (int i = 0; i < loopVars.length; i++) {
entered[i] = new Enter(sameDiff, frameName, loopVars[i]).outputVariable();
}
SDVariable[] merged = new SDVariable[loopVars.length];
Merge[] mergeOps = new Merge[loopVars.length];
for (int i = 0; i < loopVars.length; i++) {
// the second arg will later be replaced with the output of NextIteration
// but that isn't available yet (and can't be, as it depends on this)
mergeOps[i] = new Merge(sameDiff, entered[i], entered[i]);
mergeOps[i].setFrameName(frameName);
merged[i] = mergeOps[i].outputVariable();
}
Merge counterMerge = new Merge(sameDiff, counter, counter);
counter = counterMerge.outputVariable();
counterMerge.setFrameName(frameName);
NameScope condScope = sameDiff.withNameScope("cond");
SDVariable condResult = cond.define(sameDiff, merged);
condScope.close();
if (condResult.dataType() != DataType.BOOL)
throw new IllegalStateException("Can not use " + condResult.name() + " as the condition of an While loop, the condition must be a boolean.");
final Set<String> alreadyEntered = Sets.newHashSet();
SDVariable[] trueSwitches = new SDVariable[loopVars.length];
SDVariable[] exits = new SDVariable[loopVars.length];
for (int i = 0; i < loopVars.length; i++) {
SDVariable[] s = sameDiff.switchOp(merged[i], condResult);
trueSwitches[i] = s[1];
alreadyEntered.add(s[1].name());
Exit exit = new Exit(sameDiff, s[0]);
exit.setFrameName(frameName);
exits[i] = exit.outputVariable();
}
final Set<String> declared = Sets.newHashSet(sameDiff.variableMap().keySet());
final Map<String, SDVariable> done = new HashMap<>();
final SameDiff sd = sameDiff;
sameDiff.addArgumentInterceptor(argument -> {
if (argument == null)
return null;
if (!declared.contains(argument.name()))
return argument;
if (alreadyEntered.contains(argument.name()))
return argument;
if (done.containsKey(argument.name()))
return done.get(argument.name());
SDVariable e = new Enter(sd, frameName, argument, true).outputVariable();
done.put(argument.name(), e);
return e;
});
NameScope bodyScope = sameDiff.withNameScope("body");
SDVariable[] outs = body.define(sameDiff, trueSwitches);
if (outs.length != mergeOps.length)
throw new IllegalArgumentException("Number of loop variables must be equal to number of outputs.");
bodyScope.close();
sameDiff.removeArgumentInterceptor();
counter.add(1);
for (int i = 0; i < outs.length; i++) {
NextIteration nextIteration = new NextIteration(sameDiff, outs[i]);
nextIteration.setFrameName(frameName);
SDVariable n = nextIteration.outputVariable();
mergeOps[i].replaceArg(1, n);
}
counterMerge.replaceArg(1, counter);
loopScope.close();
return sameDiff.updateVariableNamesAndReferences(exits, outputNames);
} |
Constructs a While loop using the tensorflow style control flow operations (Switch, Merge, Enter, Exit, and NextIteration)
<p>
Repeatedly executes body on the loop variables and updates them with the results, until cond evaluates to false
<p>
Note that cond and body lambdas are only called once to construct the graph. The constructed graph is used for further iterations.
<p>
See <a href="http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf">Tensorflow Control Flow Implementation</a>
@param outputNames Names to give the output variables. If null, doesn't rename
@param loopName The name of the loop block and frame (must be unique). If null, uses "if"
@param loopVars Loop variables' inputs
@param cond A lambda evaluating to the loop condition
@param body A lambda doing the loop operation and returning the new loop variable values
@return The values of the loop variables once condition is false
| LoopLambdaArgs::whileLoop | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public static SameDiffSingleLambda condBody() {
// combine for loop and while loop together
return (sameDiff, inputs) -> {
SDVariable currIteration = inputs[0];
SDVariable maxIterations = inputs[1];
SDVariable extraCond = inputs[2];
SDVariable and = sameDiff.bitwise().and(
currIteration.lt(maxIterations.castTo(currIteration.dataType()))
.castTo(DataType.INT64),
extraCond.castTo(DataType.INT64));
SDVariable ret = and.castTo( DataType.BOOL);
return ret;
};
} |
Returns a lambda that takes in a custom condition and a built-in for
loop counter concept in the following manner:
int currIteration = 0;
boolean cond = ...;
int maxIterations = ...;
for(int i = currIteration; i < maxIterations && cond; i++) {
//body....
}
The inputs to the lambda are the following order:
currIteration (the starting iteration)
maxIterations (the number of times to loop)
cond: the custom condition the user passes in
@return the lambda described above for usage in the {@link #whileLoop(SameDiff, String[], String, SDVariable[], SameDiffSingleLambda, SameDiffLambda)}
routine
| LoopLambdaArgs::condBody | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ControlFlow.java | Apache-2.0 |
public String name(){
return varName;
} |
Get the name of the SDVariable
@return Name of the variable
| SDVariable::name | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | Apache-2.0 |
public boolean isPlaceHolder() {
return variableType == VariableType.PLACEHOLDER;
} |
Returns true if this variable is a placeholder
@return
| SDVariable::isPlaceHolder | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | Apache-2.0 |
public INDArray getArr() {
return getArr(false);
} |
A getter for the allocated ndarray with this {@link SDVariable}.
This getter will lazy initialize an array if one is not found based on the associated shape and
{@link WeightInitScheme} - if this is possible. If this is not possible (due to shapes being unknown, etc)
null is returned
@return the {@link INDArray} associated with this variable.
| SDVariable::getArr | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | Apache-2.0 |
public INDArray getArr(boolean enforceExistence) {
if(sameDiff.arrayAlreadyExistsForVarName(getVarName()))
return sameDiff.getArrForVarName(getVarName());
if(variableType == VariableType.ARRAY && enforceExistence) {
throw new UnsupportedOperationException("Cannot get array for ARRAY type SDVariable - use SDVariable.exec or SameDiff.output instead");
} else if(variableType == VariableType.ARRAY) {
if(sameDiff.isEagerMode()) {
return sameDiff.getEagerArrForVarName(name());
}
return null;
}
INDArray ret = sameDiff.getArrForVarName(getVarName());
if(enforceExistence && ret == null) {
throw new IllegalStateException("No array exists for variable \"" + name() + "\"");
}
return ret;
} |
A getter for the allocated ndarray with this {@link SDVariable}.
This getter will lazy initialize an array if one is not found based on the associated shape and
{@link WeightInitScheme} - if this is possible.<br>
If this is not possible (due to shapes being unknown, etc) either:<br>
(a) null is returned - if enforceExistence == false, or<br>
(b) an IllegalStateException is thrown, if enforceExistence == true
@return the {@link INDArray} associated with this variable.
| SDVariable::getArr | java | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SDVariable.java | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.