400 lines
19 KiB
C++
400 lines
19 KiB
C++
/* ******************************************************************************
|
|
*
|
|
*
|
|
* 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
|
|
******************************************************************************/
|
|
|
|
//
|
|
// @author Yurii Shyrma (iuriish@yahoo.com), created on 25.11.2017.
|
|
//
|
|
|
|
#include <system/op_boilerplate.h>
|
|
#if NOT_EXCLUDED(OP_softmax_cross_entropy_loss)
|
|
|
|
#include <ops/declarable/CustomOperations.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
CUSTOM_OP_IMPL(softmax_cross_entropy_loss, 3, 1, false, 1, 1) {
|
|
|
|
auto logits = INPUT_VARIABLE(0);
|
|
auto weights = INPUT_VARIABLE(1);
|
|
auto labels = INPUT_VARIABLE(2);
|
|
auto output = OUTPUT_VARIABLE(0);
|
|
|
|
int reductionMode = INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
|
|
double labelsSmoothing = T_ARG(0);
|
|
|
|
// input validation
|
|
REQUIRE_TRUE(labels->isSameShape(logits), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
|
|
// only 4 possible reduction modes exist
|
|
REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
|
|
// smoothing is possible for rank of logits/labels > 1
|
|
REQUIRE_TRUE(labels->rankOf() > 1 || (labels->rankOf() == 1 && labelsSmoothing == 0.), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: smoothing is not possible when rank of labels/ logits = 1 !");
|
|
|
|
if(!output->isScalar()) {
|
|
// weights array can be single scalar or has the same shape as output, and must be broadcastable to output shape
|
|
REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == output->rankOf(), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as output array, but got %i and %i correspondingly!", weights->rankOf(), output->rankOf());
|
|
// check whether broadcast operation is possible for weights array
|
|
REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *output), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: shapes of weights and output arrays should be broadcastable, but got weights = %s and output = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
|
|
}
|
|
|
|
// If label_smoothing is nonzero, smooth the labels towards 1/num_classes: new_onehot_labels = onehot_labels * (1 - label_smoothing) + label_smoothing / num_classes
|
|
// num_classes = labels->sizeAt(1)
|
|
NDArray* cLabels = new NDArray(labels->cast(weights->dataType()));
|
|
NDArray* newLabels = cLabels;
|
|
if(labelsSmoothing != 0.) {
|
|
newLabels = new NDArray(cLabels);
|
|
newLabels->assign((1.f - labelsSmoothing) * *cLabels + labelsSmoothing / cLabels->sizeAt(1));
|
|
}
|
|
|
|
// main formula: result = - sum_i(lables_i * log(softmax_i)) - sum over last dimension
|
|
// softmax_i = exp(logits_i) / sum_j(exp(logits_j))
|
|
// so result = sum_i( lables_i * (log(sum_j(exp(logits_j))) - logits_i) )
|
|
// for numerical stability we use shifted logits (one can approve this using simple math):
|
|
// softmax_i = exp(logits_i - maxLogit) / sum_j(exp(logits_j - maxLogit))
|
|
// maxLogit is max among logits_i
|
|
|
|
|
|
std::vector<int> dimensions = {-1};
|
|
NDArray shiftedLogits = *logits - logits->reduceAlongDimension(reduce::Max, dimensions, true);
|
|
NDArray logSumExp = shiftedLogits.transform(transform::Exp).reduceAlongDimension(reduce::Sum, dimensions, true).transform(transform::Log);
|
|
NDArray E = (*newLabels * (logSumExp - shiftedLogits)).reduceAlongDimension(reduce::Sum, dimensions);
|
|
|
|
// perform weights broadcasting/tile to E if it is necessary
|
|
auto weightsBroad = weights;
|
|
if(!weights->isScalar() && !weights->isSameShape(&E)) {
|
|
if(E.rankOf() == 1 && weights->isVector() && weights->rankOf() > 1)
|
|
weightsBroad = new NDArray(weights->reshape(weights->ordering(), {weights->lengthOf()}));
|
|
else
|
|
weightsBroad = new NDArray(weights->tileToShape(E.shapeInfo()));
|
|
}
|
|
|
|
// multiply E on weights
|
|
E *= *weightsBroad;
|
|
|
|
switch (reductionMode) {
|
|
case 0: // 0 - "none", un-reduced weighted losses with the same shape as labels.
|
|
output->assign(&E);
|
|
break;
|
|
|
|
case 1: { // 1 - "weighted_sum", output is scalar and equal to sum of all elements of E array
|
|
E.reduceNumber(reduce::Sum, *output);
|
|
break;
|
|
}
|
|
case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
|
|
double sum;
|
|
if (weights->isScalar())
|
|
sum = weights->e<double>(0) * E.lengthOf();
|
|
else
|
|
sum = weightsBroad->reduceNumber(reduce::Sum).e<double>(0);
|
|
|
|
if (sum == 0.)
|
|
*output = 0.;
|
|
else
|
|
output->assign(E.reduceNumber(reduce::Sum) / sum);
|
|
break;
|
|
}
|
|
case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
|
|
Nd4jLong numOfNonZeroWeights = 0;
|
|
if(weights->isScalar()) {
|
|
if(weights->e<double>(0) != 0.)
|
|
numOfNonZeroWeights = E.lengthOf();
|
|
}
|
|
else {
|
|
numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
|
|
}
|
|
|
|
if (numOfNonZeroWeights == 0)
|
|
*output = 0.;
|
|
else
|
|
output->assign(E.reduceNumber(reduce::Sum) / double(numOfNonZeroWeights));
|
|
|
|
break;
|
|
}
|
|
}
|
|
|
|
if(weightsBroad != weights)
|
|
delete weightsBroad;
|
|
|
|
if(newLabels != cLabels)
|
|
delete newLabels;
|
|
|
|
delete cLabels;
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
DECLARE_TYPES(softmax_cross_entropy_loss) {
|
|
|
|
getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS})
|
|
->setAllowedInputTypes(1, {ALL_FLOATS})
|
|
->setAllowedInputTypes(2, {ALL_FLOATS, ALL_INTS})
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
DECLARE_SHAPE_FN(softmax_cross_entropy_loss) {
|
|
|
|
auto logitsShapeInfo = inputShape->at(0);
|
|
auto weightsShapeInfo = inputShape->at(1);
|
|
auto labelsShapeInfo = inputShape->at(2);
|
|
|
|
// labels and logits must have the same shapes
|
|
REQUIRE_TRUE(shape::shapeEquals(logitsShapeInfo, labelsShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly!", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
|
|
|
|
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
|
|
Nd4jLong const* outShapeInfo = nullptr;
|
|
|
|
if(INT_ARG(0) != 0) // in this case output is scalar
|
|
outShapeInfo = ConstantShapeHelper::getInstance().scalarShapeInfo(outType);
|
|
else { // in this case output has the shape as labels and logits minus last dimension
|
|
std::vector<int> dimensions = {-1};
|
|
outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(logitsShapeInfo), dimensions, logitsShapeInfo, false, true, block.getWorkspace());
|
|
|
|
// weights array can be single scalar or has the same rank as output, and must be broadcastable to output
|
|
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(outShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as output array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(outShapeInfo));
|
|
// check whether broadcast operation is possible for weights array
|
|
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, outShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: shapes of weights and output arrays should be broadcastable, but got weights = %s and output = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(outShapeInfo).c_str());
|
|
}
|
|
|
|
return SHAPELIST(outShapeInfo);
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
CUSTOM_OP_IMPL(softmax_cross_entropy_loss_grad, 3, 3, false, 1, 1) {
|
|
|
|
auto logits = INPUT_VARIABLE(0);
|
|
auto weights = INPUT_VARIABLE(1);
|
|
auto labels = INPUT_VARIABLE(2);
|
|
|
|
auto dLdp = OUTPUT_VARIABLE(0); // dL/dlogits
|
|
auto dLdw = OUTPUT_VARIABLE(1); // dL/dweights
|
|
auto dLdl = OUTPUT_VARIABLE(2); // dL/dlabels
|
|
|
|
auto labelsSmoothing = T_ARG(0);
|
|
|
|
int reductionMode = INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
|
|
// take into account Alex's proposition to treat "none" the same as "weighted_sum" mode when calculating gradients
|
|
if(reductionMode == 0)
|
|
reductionMode = 1;
|
|
|
|
std::vector<int> dimensions = {-1};
|
|
|
|
// input validation
|
|
REQUIRE_TRUE(labels->isSameShape(logits), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
|
|
// only 4 possible reduction modes exist
|
|
REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
|
|
auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(logits->ordering(), dimensions, logits->shapeInfo(), false, false, block.getWorkspace());
|
|
// weights array can be single scalar or has the same shape as loss, and must be broadcastable to loss shape
|
|
REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == shape::rank(lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but got %i and %i correspondingly!", weights->rankOf(), shape::rank(lossShapeInfo));
|
|
// check whether broadcast operation is possible for weights array
|
|
REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(weights->shapeInfo(), lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got weights = %s and loss = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
|
|
// smoothing is possible for rank of logits/labels > 1
|
|
REQUIRE_TRUE(labels->rankOf() > 1 || (labels->rankOf() == 1 && labelsSmoothing == 0.), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: smoothing is not possible when rank of labels/ logits = 1 !");
|
|
|
|
// If label_smoothing is nonzero, smooth the labels towards 1/num_classes: new_onehot_labels = onehot_labels * (1 - label_smoothing) + label_smoothing / num_classes
|
|
// num_classes = labels->sizeAt(1)
|
|
NDArray* cLabels = new NDArray(labels->cast(weights->dataType()));
|
|
NDArray* newLabels = cLabels;
|
|
if(labelsSmoothing != 0.) {
|
|
newLabels = new NDArray(labels->shapeInfo(), dLdl->dataType(), false, block.launchContext());
|
|
newLabels->assign((1.f - labelsSmoothing) * *cLabels + labelsSmoothing / cLabels->sizeAt(1));
|
|
}
|
|
|
|
NDArray softmax = (*logits - logits->reduceAlongDimension(reduce::Max, dimensions, true)).transform(transform::Exp);
|
|
softmax /= softmax.reduceAlongDimension(reduce::Sum, dimensions, true);
|
|
|
|
// dEdp = softmax * sum_i(lables_i) - labels
|
|
dLdp->assign(softmax * newLabels->reduceAlongDimension(reduce::Sum, dimensions, true) - *newLabels);
|
|
|
|
// dEdl = -log(softmax)
|
|
dLdl->assign(-softmax.transform(transform::Log)* (1.f - labelsSmoothing));
|
|
|
|
NDArray shiftedLogits = *logits - logits->reduceAlongDimension(reduce::Max, dimensions, true);
|
|
NDArray logSumExp = shiftedLogits.transform(transform::Exp).reduceAlongDimension(reduce::Sum, dimensions, true).transform(transform::Log);
|
|
NDArray E = (*newLabels * (logSumExp - shiftedLogits)).reduceAlongDimension(reduce::Sum, dimensions);
|
|
|
|
// perform weights broadcasting/tile to E if it is necessary
|
|
auto weightsBroad = weights;
|
|
if(!weights->isScalar() && !weights->isSameShape(&E))
|
|
weightsBroad = new NDArray(weights->tileToShape(E.shapeInfo()));
|
|
|
|
dimensions = ShapeUtils::evalDimsToExclude(dLdp->rankOf(), dimensions);
|
|
|
|
switch (reductionMode) {
|
|
case 1: { // 1 - "none" and "weighted_sum", output is scalar and equal to sum of all elements of E array
|
|
|
|
if(weights->isScalar() || weights->lengthOf() == 1) {
|
|
dLdw->assign(E.reduceNumber(reduce::Sum));
|
|
*dLdp *= *weights;
|
|
*dLdl *= *weights;
|
|
}
|
|
else {
|
|
dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, *weightsBroad, *dLdp);
|
|
dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, *weightsBroad, *dLdl);
|
|
|
|
if(weights != weightsBroad) {
|
|
std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
|
|
E.reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true);
|
|
}
|
|
else
|
|
dLdw->assign(E);
|
|
}
|
|
|
|
break;
|
|
}
|
|
case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
|
|
NDArray sum;
|
|
if (weights->isScalar())
|
|
sum = (*weights) * E.lengthOf();
|
|
else
|
|
sum = weightsBroad->reduceNumber(reduce::Sum);
|
|
|
|
if (sum.e<double>(0) == 0.) {
|
|
*dLdp = 0.;
|
|
*dLdl = 0.;
|
|
*dLdw = 0.;
|
|
}
|
|
else {
|
|
|
|
if(weights->isScalar() || weights->lengthOf() == 1) {
|
|
NDArray temp = *weights / sum;
|
|
*dLdp *= temp;
|
|
*dLdl *= temp;
|
|
*dLdw = 0.;
|
|
}
|
|
else {
|
|
|
|
NDArray temp = *weightsBroad / sum;
|
|
dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdp);
|
|
dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdl);
|
|
|
|
if(weights != weightsBroad) {
|
|
std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
|
|
((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum)).reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true);
|
|
}
|
|
else
|
|
dLdw->assign((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum));
|
|
}
|
|
}
|
|
break;
|
|
}
|
|
case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
|
|
Nd4jLong numOfNonZeroWeights = 0;
|
|
if(weights->isScalar()) {
|
|
if(weights->e<double>(0) != 0.)
|
|
numOfNonZeroWeights = E.lengthOf();
|
|
}
|
|
else
|
|
numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
|
|
|
|
if (numOfNonZeroWeights == 0) {
|
|
*dLdp = 0.;
|
|
*dLdl = 0.;
|
|
*dLdw = 0.;
|
|
}
|
|
else {
|
|
|
|
if(weights->isScalar() || weights->lengthOf() == 1) {
|
|
NDArray temp = *weights / numOfNonZeroWeights;
|
|
*dLdp *= temp;
|
|
*dLdl *= temp;
|
|
dLdw->assign(E.reduceNumber(reduce::Sum) / numOfNonZeroWeights);
|
|
}
|
|
else {
|
|
NDArray temp = *weightsBroad / numOfNonZeroWeights;
|
|
dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdp);
|
|
dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdl);
|
|
|
|
if(weights != weightsBroad) {
|
|
std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
|
|
E.reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true);
|
|
*dLdw /= numOfNonZeroWeights;
|
|
}
|
|
else
|
|
dLdw->assign(E / numOfNonZeroWeights);
|
|
}
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
|
|
if(weightsBroad != weights)
|
|
delete weightsBroad;
|
|
|
|
if(newLabels != cLabels)
|
|
delete newLabels;
|
|
|
|
delete cLabels;
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
DECLARE_TYPES(softmax_cross_entropy_loss_grad) {
|
|
|
|
getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS})
|
|
->setAllowedInputTypes(1, {ALL_FLOATS})
|
|
->setAllowedInputTypes(2, {ALL_FLOATS, ALL_INTS})
|
|
->setAllowedInputTypes(3, {ALL_FLOATS})
|
|
->setAllowedInputTypes(4, {ALL_FLOATS})
|
|
->setAllowedInputTypes(5, {ALL_FLOATS})
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
DECLARE_SHAPE_FN(softmax_cross_entropy_loss_grad) {
|
|
|
|
auto logitsShapeInfo = inputShape->at(0);
|
|
auto weightsShapeInfo = inputShape->at(1);
|
|
auto labelsShapeInfo = inputShape->at(2);
|
|
|
|
std::vector<int> dimensions = {-1};
|
|
|
|
// labels and logits must have the same shapes
|
|
REQUIRE_TRUE(shape::shapeEquals(logitsShapeInfo, labelsShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly!", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
|
|
auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(logitsShapeInfo), dimensions, logitsShapeInfo, false, false, block.getWorkspace());
|
|
// weights array can be single scalar or has the same rank as loss, and must be broadcastable to loss
|
|
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(lossShapeInfo));
|
|
// check whether broadcast operation is possible for weights array
|
|
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got weights = %s and loss = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
|
|
|
|
auto outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
|
|
|
|
auto dLdpShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(outType, shape::order(logitsShapeInfo), shape::shapeOf(logitsShapeInfo), shape::rank(logitsShapeInfo)));
|
|
auto dLdwShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(outType, shape::order(weightsShapeInfo), shape::shapeOf(weightsShapeInfo), shape::rank(weightsShapeInfo)));
|
|
auto dLdlShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(outType, shape::order(labelsShapeInfo), shape::shapeOf(labelsShapeInfo), shape::rank(labelsShapeInfo)));
|
|
|
|
return SHAPELIST(dLdpShapeInfo, dLdwShapeInfo, dLdlShapeInfo);
|
|
}
|
|
|
|
|
|
}
|
|
}
|
|
|
|
#endif |