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cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests2.cpp

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Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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
******************************************************************************/
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <helpers/helper_hash.h>
#include <NDArray.h>
#include <array/NDArrayList.h>
using namespace nd4j;
using namespace nd4j::graph;
class DeclarableOpsTests2 : public testing::Test {
public:
DeclarableOpsTests2() {
printf("\n");
}
};
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_1) {
NDArray input('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32);
NDArray indices('c', {1,6}, {0,1, 2,2, 1,2}, nd4j::DataType::INT32);
NDArray expected('c', {2,1,6,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20, 21,22,23,24, 21,22,23,24, 17,18,19,20, 21,22,23,24}, nd4j::DataType::FLOAT32);
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
TEST_F(DeclarableOpsTests2, gather_2) {
NDArray input('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24});
//auto indices ('c', {1,6}, {0,1, 2,2, 1,2});
NDArray expected('c', {2,6,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20, 21,22,23,24, 21,22,23,24, 17,18,19,20, 21,22,23,24});
nd4j::ops::gather op;
auto result = op.execute({&input}, {}, {1, 0,1, 2,2, 1,2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_3) {
NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24});
NDArray indices ('c', {1,1}, {2}, nd4j::DataType::INT32);
NDArray expected('c', {2,1,1,4}, {9,10,11,12,21,22,23,24});
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
TEST_F(DeclarableOpsTests2, gather_4) {
NDArray input('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24});
//auto indices ('c', {1,1}, {2});
NDArray expected('c', {2,4}, {9,10,11,12,21,22,23,24});
nd4j::ops::gather op;
auto result = op.execute({&input}, {}, {1, 2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_5) {
NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24});
NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT32);
NDArray expected('c', {2,2,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 21,22,23,24,17,18,19,20,21,22,23,24});
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_6) {
NDArray input ('c', {3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36});
NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT32);
NDArray expected('c', {2,3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36, 25,26,27,28,29,30,31,32,33,34,35,36, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36});
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_7) {
NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24});
NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT64);
NDArray expected('c', {2,3,2,3}, {1, 2, 3, 3, 2, 3, 5, 6, 7, 7, 6, 7, 9,10,11,11,10,11, 13,14,15,15,14,15, 17,18,19,19,18,19, 21,22,23,23,22,23});
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_8) {
NDArray input('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::FLOAT32);
NDArray indices('c', {1}, {2}, nd4j::DataType::INT32);
NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, nd4j::DataType::FLOAT32);
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
// output->printShapeInfo();
// output->printIndexedBuffer();
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_9) {
NDArray x('c', {2, 4, 3, 2}, nd4j::DataType::FLOAT32);
NDArray indices('c', {2}, {1, 0}, nd4j::DataType::INT32);
nd4j::ops::gather op;
auto result = op.execute({&x, &indices}, {}, {-2});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_10) {
NDArray x('c', {2, 2}, {1, 2, 3, 4});
NDArray e('c', {2, 2}, {3, 4, 1, 2});
nd4j::ops::gather op;
auto result = op.execute({&x}, {}, {0, 1, 0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_11) {
NDArray x('c', {2, 2}, {1, 2, 3, 4});
NDArray indices('c', {2}, {1, 0}, nd4j::DataType::INT64);
NDArray e('c', {2, 2}, {3, 4, 1, 2});
nd4j::ops::gather op;
auto result = op.execute({&x, &indices}, {}, {0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(e.isSameShape(z));
ASSERT_TRUE(e.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_12) {
NDArray input('c', {4}, {2.f, 3.f, 4.f, 5.f});
NDArray indices('c', {2}, {0, 2}, nd4j::DataType::INT32);
NDArray exp('c', {2}, {2.f, 4.f});
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, gather_13) {
NDArray input ('c', {2,3,4,5}, nd4j::DataType::DOUBLE);
NDArray indices ('c', {2,3,4}, {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3,0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}, nd4j::DataType::INT32);
NDArray expected('c', {2,3, 2,3,4, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100,101,102,103,104, 105,106,107,108,109, 110,111,112,113,114, 115,116,117,118,119, 100,101,102,103,104, 105,106,107,108,109, 110,111,112,113,114, 115,116,117,118,119, 100,101,102,103,104, 105,106,107,108,109, 110,111,112,113,114, 115,116,117,118,119,
100,101,102,103,104, 105,106,107,108,109, 110,111,112,113,114, 115,116,117,118,119, 100,101,102,103,104, 105,106,107,108,109, 110,111,112,113,114, 115,116,117,118,119, 100,101,102,103,104, 105,106,107,108,109, 110,111,112,113,114, 115,116,117,118,119});
input.linspace(0);
nd4j::ops::gather op;
auto result = op.execute({&input, &indices}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto* output = result->at(0);
ASSERT_TRUE(expected.isSameShapeStrict(output));
ASSERT_TRUE(expected.equalsTo(output));
delete result;
}
[WIP] More of CUDA (#95) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
2019-08-02 19:01:03 +02:00
TEST_F(DeclarableOpsTests2, BroadcastGradientArgs_1) {
NDArray input ('c', {3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36}, nd4j::DataType::INT32);
NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT32);
nd4j::ops::broadcastgradientargs op;
auto result = op.execute({&input, &indices}, {}, {});
ASSERT_EQ(ND4J_STATUS_KERNEL_FAILURE, result->status());
delete result;
}
TEST_F(DeclarableOpsTests2, NLP_Cbow_Test_1) {
auto exp0 = NDArrayFactory::create<double>('c', {1, 10});
auto exp1 = NDArrayFactory::create<double>('c', {1, 10});
auto exp2 = NDArrayFactory::create<double>('c', {1, 10});
exp0.assign(0.0095);
exp1.assign(0.019875);
exp2.assign(0.02);
auto target = NDArrayFactory::create<int>(0);
auto ngStarter = NDArrayFactory::empty<int>();
auto context = NDArrayFactory::create<int>('c', {3}, {0, 1, 2});
auto locked = NDArrayFactory::create<int>('c', {3});
auto indices = NDArrayFactory::create<int>('c', {2}, {4, 5});
auto codes = NDArrayFactory::create<int8_t>('c', {2}, {1, 1});
auto syn0 = NDArrayFactory::create<double>('c', {100, 10});
auto syn1 = NDArrayFactory::create<double>('c', {100, 10});
auto syn1Neg = NDArrayFactory::empty<double>();
auto expTable = NDArrayFactory::create<double>('c', {10000});
auto negTable = NDArrayFactory::empty<double>();
auto numWords = NDArrayFactory::create<int>('c', {1}, {1});
syn0.assign(0.01);
syn1.assign(0.02);
expTable.assign(0.5);
auto alpha = NDArrayFactory::create<double>(0.025);
auto randomValue = NDArrayFactory::create<Nd4jLong>(2L);
auto inferenceVector = NDArrayFactory::empty<double>();
nd4j::ops::cbow op;
auto result = op.execute({&target, &ngStarter, &context, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &numWords, &locked, &inferenceVector}, {}, {}, {true}, true);
ASSERT_EQ(Status::OK(), result->status());
auto row_s0_0 = syn0({0,1, 0,0}, true);
auto row_s0_1 = syn0({1,2, 0,0}, true);
auto row_s0_2 = syn0({2,3, 0,0}, true);
auto row_s1_4 = syn1({4,5, 0,0}, true);
auto row_s1_5 = syn1({5,6, 0,0}, true);
auto row_s1_6 = syn1({6,7, 0,0}, true);
ASSERT_EQ(exp0, row_s0_0);
ASSERT_EQ(exp0, row_s0_1);
ASSERT_EQ(exp0, row_s0_2);
ASSERT_EQ(exp1, row_s1_4);
ASSERT_EQ(exp1, row_s1_5);
ASSERT_EQ(exp2, row_s1_6);
delete result;
}
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
TEST_F(DeclarableOpsTests2, Test_Concat_3D_1) {
auto x0 = NDArrayFactory::create<double>('c', {1, 100, 150});
auto x1 = NDArrayFactory::create<double>('c', {1, 100, 150});
auto x2 = NDArrayFactory::create<double>('c', {1, 100, 150});
auto x3 = NDArrayFactory::create<double>('c', {1, 100, 150});
x0.assign(1.0);
x1.assign(2.0);
x2.assign(3.0);
x3.assign(4.0);
nd4j::ops::concat op;
auto result = op.execute({&x0, &x1, &x2, &x3}, {}, {0}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
Nd4jLong numOfTads= ShapeUtils::getNumOfSubArrs(z->getShapeInfo(), {0});
ASSERT_TRUE(4 == numOfTads);
for (int e = 0; e < numOfTads; e++) {
NDArray tad = (*z)(e, {0});
auto mean = tad.meanNumber().e<double>(0);
ASSERT_NEAR((double) e+1, mean, 1e-5);
}
delete result;
}
TEST_F(DeclarableOpsTests2, YetAnotherMatmulTest_1) {
auto A = NDArrayFactory::create<float>('c', {3, 3});
auto B = NDArrayFactory::create<float>('c', {3, 1});
auto exp = NDArrayFactory::create<float>('c', {3, 1}, {14.00, 32.00, 50.00});
A.linspace(1);
B.linspace(1);
nd4j::ops::matmul op;
auto result = op.execute({&A, &B}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests2, Test_Squeeze_1) {
auto x = NDArrayFactory::create<float>('c', {2, 1, 3, 1, 1, 1, 4});
x.linspace(1);
auto exp = x.reshape('c', {2, 3, 4});
nd4j::ops::squeeze op;
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
Merge master to upstream (#7945) * Shugeo strided slice zeros (#14) * Modified strided_slice op to properly work with empty-like shapes. * Fixed test for reduce_mean with empty-like input. * [WIP] Last merge (#15) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks * [WIP] Fixing outstanding issues for NLP (#9) * Avoid using not-inited objects * Test fixed. * Redundant method avoided for models like FastText * KMeans++ implementation * KMeans++ implementation * Disable parallel execution * KMeans++ * Tests * Dev branch merge (#16) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Fix some issues on master (#17) * Fix DataVec test issue * Fix issue with dl4j SameDiff output layer * Dtype fix for lambda layers * #7912 BertIterator dtype fix (use float32 not global default) * [WIP] Next set of CUDA stuff (#7) New CUDA implementations and improvements * bad file * Dev branch master merge (#23) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * SameDiff ops, TF import and fixes (#24) * CheckNumerics tests + fixes + misc fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fake quant Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * FakeQuantWithMinMaxArgs Signed-off-by: AlexDBlack <blacka101@gmail.com> * CheckNumerics fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Javadoc Signed-off-by: AlexDBlack <blacka101@gmail.com> * Exception tweak Signed-off-by: AlexDBlack <blacka101@gmail.com> * fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix for out of scope stack allocated var use Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignores Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignore for known failing test (already logged issue) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Merge upstream to fork (#25) * Add thousand-separator commas to TotalParams (#7915) * Add thousand-separator commas to TotalParams The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them. * Add thousand-separator commas to MultiLayerNetwork Corresponding change to MultiLayerNetwork Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com> * Update contributing and issue/PR templates (#7934) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix link to AdaDelta paper (#7942) Fix link to AdaDelta paper hosted on matthewzeiler.com Signed-off-by: Jxtps * Fixes, and ignores for known/logged failing issues (#7943) Signed-off-by: AlexDBlack <blacka101@gmail.com> * SameDiff + DL4J/SameDiff: Multiple fixes (#28) * #7919 HDF5 attribute buffer length fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7909 Arbiter constructor exception ux improvements Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7925 RNN output layer length checks Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Add listener for validating inputs are not incorrectly modified Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Integrate NonInplaceValidationListener into tests * #7844 DL4J SameDiff fixes for variable minibatch size * DL4J SameDiff fixes - ensure gradient for input placeholder is available Signed-off-by: AlexDBlack <blacka101@gmail.com> * Tweaks to ExternalErrorsFunction - use placeholders, make more robust * Another fix * More fixes * More SameDiff/DL4J fixes * Scope out scalar array creation in BaseScalarOp * Remove debug code Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] Final dev branch merge (#29) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * [WIP] Multiple dataset iterators (#27) * Splitting dataset into arbitrary number * Fixes * Multiple split of iterator * Test * Test * Some fixes * signature change * one more tweak Signed-off-by: raver119 <raver119@gmail.com> * one more test for sequential use of DataSetIteratorSplitter Signed-off-by: raver119 <raver119@gmail.com> * Fixes * Fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * couple of assertions tweaked Signed-off-by: raver119 <raver119@gmail.com> * MDS splitter test :/ Signed-off-by: raver119 <raver119@gmail.com> * Minor refactoring * Multi dataset * Some fixes * More tests * Small number of test fixes/improvements (failures on CI) (#31) Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] More CUDA stuff (#26) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * LRN BP CUDA Signed-off-by: raver119 <raver119@gmail.com> * less memory Signed-off-by: raver119 <raver119@gmail.com> * Fixed bug with crop_and_resize op helper. * get rid of unnecessary index-calculation dunction Signed-off-by: Yurii <yurii@skymind.io> * Fixed sort with nth_element cuda-based helper. * Refactored nth_element. * Refactored nth_element op and tests. * Modified usage of dim array with sortTad routine. * Refactored main routine of helper for non_max_image_suppression op. * non_max_image_suppression op helper with cuda kernel implementation. Initial revision. * fix vol2col cuda kernel * meh Signed-off-by: raver119 <raver119@gmail.com> * topK concept Signed-off-by: raver119 <raver119@gmail.com> * unsorted topK with scanWitdh of 1 Signed-off-by: raver119 <raver119@gmail.com> * correct vol2col tests * sorted/unsorted topK Signed-off-by: raver119 <raver119@gmail.com> * implementation and fixing col2im/col2vol * Corrected usage flags with input/output with reverse op. * dup is const now Signed-off-by: raver119 <raver119@gmail.com> * percentile op Signed-off-by: raver119 <raver119@gmail.com> * group tests for mapool2d Signed-off-by: Yurii <yurii@skymind.io> * special test for george Signed-off-by: raver119 <raver119@gmail.com> * less threads for sortTad Signed-off-by: raver119 <raver119@gmail.com> * provide conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * remove auther in sort tad kernel code Signed-off-by: Yurii <yurii@skymind.io> * provide depthwise_conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * - max_pooling_with_argmax - null check for special use Signed-off-by: raver119 <raver119@gmail.com> * dts cuda Signed-off-by: raver119 <raver119@gmail.com> * provide sconv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * std cuda Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op to conform TF implementation. * Improved suppression helper. * provide pooling3d for cuda Signed-off-by: Yurii <yurii@skymind.io> * minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * more of minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * (bi)dynamic_rnn Signed-off-by: raver119 <raver119@gmail.com> * templates init order Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op. * Added cuda kernel for non_max_suppression. * CPU sort by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value tests Signed-off-by: raver119 <raver119@gmail.com> * Eliminate compiler error with cuda implementation. * - repaired gradCheck in cuda - provide conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * missed signature Signed-off-by: raver119 <raver119@gmail.com> * provide depthwise_conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * Implementation of lup helper with cuda kernel. Initial commit. * further work on backprops for convolutions Signed-off-by: Yurii <yurii@skymind.io> * CUDA linear sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * CUDA tad sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * start providing of backprop for pooling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * Added atomicAdd for bool datatype. * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition scalar CUDA Signed-off-by: raver119 <raver119@gmail.com> * important comment Signed-off-by: raver119 <raver119@gmail.com> * fix pooling2d/3d backprop helpers Signed-off-by: Yurii <yurii@skymind.io> * Added non-linear test with dynamic_partition. * Improved test for dynamic_partition. * dynamic_partition TAD concept Signed-off-by: raver119 <raver119@gmail.com> * - dynamic_partition TAD CUDA impl - dynamic_partition TAD CPU fix Signed-off-by: raver119 <raver119@gmail.com> * - rewrite cpu code for usampling2d/3d - write cuda code for usampling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * dynamic_stitch CUDA vector case Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case impl Signed-off-by: raver119 <raver119@gmail.com> * Added tests for dynamic_stitch 3D-4D cases. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * Fixed type check for dynamic stitch. * min/max bp Signed-off-by: raver119 <raver119@gmail.com> * rewrite code for upsampling2d/3d cpu Signed-off-by: Yurii <yurii@skymind.io> * reduce min/max/norm_max bp Signed-off-by: raver119 <raver119@gmail.com> * lup implementation. Additional enhancements. * provide code for upsamling2d/3d backprop Signed-off-by: Yurii <yurii@skymind.io> * weightedCrossEntropyWithLogits Signed-off-by: raver119 <raver119@gmail.com> * Fixed template math atomicMul for 64bit ints. * Refactored dynamic_partition_bp op. * inverseBroadcast fix Signed-off-by: raver119 <raver119@gmail.com> * DynamicPartitionBP test datatype fixed. * - nd4j_atomicMul Windows fix - cpu/NDArrayLambda.hpp excluded from CUDA Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
delete result;
}
TEST_F(DeclarableOpsTests2, Test_Squeeze_2) {
auto x = NDArrayFactory::create<float>('c', {2, 3, 4});
x.linspace(1);
auto exp = x.dup();
nd4j::ops::squeeze op;
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp->isSameShape(z));
ASSERT_TRUE(exp->equalsTo(z));
delete result;
delete exp;
}
TEST_F(DeclarableOpsTests2, Test_FloorMod_1) {
auto x = NDArrayFactory::create<float>('c', {1, 3}, {2.0, 6.0, -3.0});
auto y = NDArrayFactory::create<float>('c', {1, 3}, {-3.0, 2.0, -2.0});
auto exp = NDArrayFactory::create<float>('c', {1, 3}, {-1., 0., -1.,});
nd4j::ops::floormod op;
auto result = op.execute({&x, &y}, {}, {});
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
[WIP] More of CUDA (#95) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
2019-08-02 19:01:03 +02:00
TEST_F(DeclarableOpsTests2, Test_FloorDiv_1) {
auto x = NDArrayFactory::create<float>('c', {1, 3}, {3.0, 6.0, -3.0});
auto y = NDArrayFactory::create<float>('c', {1, 3}, {-2.0, 2.0, -2.0});
auto exp = NDArrayFactory::create<float>('c', {1, 3}, {-2., 3., 1.,});
nd4j::ops::floordiv op;
auto result = op.execute({&x, &y}, {}, {});
auto z = result->at(0);
// z->printShapeInfo("FloorDiv1 shape");
// z->printIndexedBuffer("FloorDiv1");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests2, Test_FloorDiv_2) {
auto x = NDArrayFactory::create<float>('c', {1, 3}, {3.0, 6.0, -3.0});
auto y = NDArrayFactory::create<float>('c', {1, 3}, {-2.0, 2.0, -2.0});
auto eps = NDArrayFactory::create<float>('c', {1, 3}, {1, 2, 3});
[WIP] cuda concat (#107) * - correct cuda concat Signed-off-by: Yurii <yurii@skymind.io> * - pooling 2d/3d : take into account possible case when input and gradI have different strides Signed-off-by: Yurii <yurii@skymind.io> * master pulled in Signed-off-by: raver119 <raver119@gmail.com> * floordiv_bp test reverted Signed-off-by: raver119 <raver119@gmail.com> * - add NDArray::printLinearBuffer method Signed-off-by: Yurii <yurii@skymind.io>
2019-08-08 17:05:21 +02:00
auto exp1 = NDArrayFactory::create<float>('c', {1, 3}, {0.f, 0.f, 0.f});
auto exp2 = NDArrayFactory::create<float>('c', {1, 3}, {0.f, 0.f, 0.f});
[WIP] More of CUDA (#95) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
2019-08-02 19:01:03 +02:00
nd4j::ops::floordiv_bp op;
auto result = op.execute({&x, &y, &eps}, {}, {});
ASSERT_EQ(result->status(), Status::OK());
auto z1 = result->at(0);
auto z2 = result->at(1);
// z->printShapeInfo("FloorDiv1 shape");
// z1->printIndexedBuffer("FloorDiv2_1");
// z2->printIndexedBuffer("FloorDiv2_2");
ASSERT_TRUE(exp1.equalsTo(z1));
ASSERT_TRUE(exp2.equalsTo(z2));
delete result;
}
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
TEST_F(DeclarableOpsTests2, Test_CRelu_1) {
auto x = NDArrayFactory::create<float>('c', {2, 2}, {1.0, 2.0, 3.0, 4.0});
auto exp = NDArrayFactory::create<float>('c', {2, 4}, {1.0, 2.0, 0, 0, 3.0, 4.0, 0, 0});
nd4j::ops::crelu op;
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests2, Test_CRelu_BP_2) {
auto x = NDArrayFactory::create<float>('c', {2, 2}, {1.0, 2.0, -3.0, 4.0});
auto eps = NDArrayFactory::create<float>('c', {2, 4}, {1.0, 2.0, 4, 3, 3.0, 4.0, 2, 1});
auto exp = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, -2, 4});
nd4j::ops::crelu_bp op;
auto result = op.execute({&x, &eps}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_EQ(1, result->size());
auto z = result->at(0);
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(DeclarableOpsTests2, Test_Concat_BP_1) {
auto x = NDArrayFactory::create<float>('c', {2, 2});
auto y = NDArrayFactory::create<float>('c', {2, 2});
auto eps = NDArrayFactory::create<float>('c', {2, 4}, {1.0, 2.0, 0, 1, 3.0, 4.0, 0, 1});
auto expEX = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 3, 4});
auto expEY = NDArrayFactory::create<float>('c', {2, 2}, {0, 1, 0, 1});
nd4j::ops::concat_bp op;
auto result = op.execute({&x, &y, &eps}, {}, {-1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_EQ(2, result->size());
auto epsX = result->at(0);
auto epsY = result->at(1);
ASSERT_TRUE(expEX.isSameShape(epsX));
ASSERT_TRUE(expEX.equalsTo(epsX));
ASSERT_TRUE(expEY.isSameShape(epsY));
ASSERT_TRUE(expEY.equalsTo(epsY));
delete result;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot5) {
auto x = NDArrayFactory::create<float>('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('c', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {2,4,2,4}, {44,110,160, 66,132, 38, 88,154, 68,170,224,102,204, 82,136,238, 92,230,288,138,276,126,184,322, 116,290,352,174,348,170,232,406, 76,190,160,114,228,182,152,266, 100,250,224,150,300,226,200,350, 124,310,288,186,372,270,248,434, 148,370,352,222,444,314,296,518});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {1,1,1,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot6) {
auto x = NDArrayFactory::create<float>('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {2,4,2,4}, {22, 66,110,154, 44, 88,132,176, 34,102,170,238, 68,136,204,272, 46,138,230,322, 92,184,276,368, 58,174,290,406,116,232,348,464, 38,114,190,266, 76,152,228,304, 50,150,250,350,100,200,300,400, 62,186,310,434,124,248,372,496, 74,222,370,518,148,296,444,592});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {1,1,1,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot7) {
auto x = NDArrayFactory::create<float>('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('c', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {2,4,2,4}, {76,166,112,106,196, 62,136,226, 60,174,208, 98,212,230,136,250, 76,214,336,122,260,174,168,306, 124,286,240,178,340,150,232,394, 100,226,176,142,268,106,184,310, 84,234,272,134,284,274,184,334, 100,274,400,158,332,218,216,390, 148,346,304,214,412,194,280,478});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {1,1,1,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot8) {
auto x = NDArrayFactory::create<float>('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {2,4,2,4}, {30, 90,150,210, 60,120,180,240, 38,114,190,266, 76,152,228,304, 46,138,230,322, 92,184,276,368, 54,162,270,378,108,216,324,432, 42,126,210,294, 84,168,252,336, 50,150,250,350,100,200,300,400, 58,174,290,406,116,232,348,464, 66,198,330,462,132,264,396,528});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {1,1,1,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot9) {
// NDArray z('f',{2,2,3}, nd4j::DataType::DOUBLE);
// z.linspace(1);
// z.printShapeInfo();
// z.printIndexedBuffer();
// z.reshapei('c', {4,3});
// z.printShapeInfo();
// z.printIndexedBuffer();
auto x = NDArrayFactory::create<float>('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {3,4,4,3}, {14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422, 62, 62, 62,142,142,142,222,222,222,302,302,302, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422, 62, 62, 62,142,142,142,222,222,222,302,302,302, 62, 62, 62,142,142,142,222,222,222,302,302,302, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {1,0,1,0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot10) {
auto x = NDArrayFactory::create<float>('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {4,4}, {114,258,402,546, 138,314,490,666, 162,370,578,786, 186,426,666,906});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {2,0,1, 2,0,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot11) {
auto x = NDArrayFactory::create<float>('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {4,4}, {98,218,338,458, 134,302,470,638, 170,386,602,818, 206,470,734,998});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {2,0,1, 2,0,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot12) {
auto x = NDArrayFactory::create<float>('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('c', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {4,4}, {272,292,312,332, 368,396,424,452, 464,500,536,572, 560,604,648,692});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {2,0,1, 2,0,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot13) {
auto x = NDArrayFactory::create<float>('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('c', {4,2,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {3,3}, {640,560,640, 576,624,576, 640,560,640});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {2,0,2, 2,1,0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot14) {
auto x = NDArrayFactory::create<float>('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('c', {4,2,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {3,3}, {648,600,520, 648,536,648, 520,600,648});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {2,0,2, 2,1,0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, TestTensorDot15) {
auto x = NDArrayFactory::create<float>('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15});
auto y = NDArrayFactory::create<float>('f', {4,2,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16});
auto expected = NDArrayFactory::create<float>('c', {3,3}, {624,624,624, 656,656,656, 624,624,624});
nd4j::ops::tensormmul op;
auto results = op.execute({&x, &y}, {}, {2,0,2, 2,1,0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_1) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
auto expected = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
expected.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_2) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1,4,5});
auto expected = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
expected.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
// result->printIndexedBuffer("ADL test2");
// expected.printIndexedBuffer("ADL expec");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_3) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1,1,5});
auto expected = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
expected.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_4) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,1,1,5});
auto expected = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
expected.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_5) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
auto expected = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
expected.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_6) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
auto expected = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.f);
expected.assign(0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_7) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 60.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_8) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 0.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_9) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,1,4,1});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 60.);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_10) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 60.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_11) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 1.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_12) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 0.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_13) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 1.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_14) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5);
weights.p(1, 0.f);
weights.p(2, 0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 1.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_15) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 2.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_16) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5f);
predictions.p(0, 0.f);
predictions.p(1, 0.f);
predictions.p(2, 0.f);
predictions.p(3, 0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 2.01667, 1e-5);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_17) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5f);
predictions.p(0, 0.f);
predictions.p(1, 0.f);
predictions.p(2, 0.f);
predictions.p(3, 0.f);
labels.p(0, 0.f);
labels.p(1, 0.f);
labels.p(2, 0.f);
labels.p(3, 0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<float>(0), 1.93333, 1e-5);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_18) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,1,1,5});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5f);
predictions.p(0, 0.f);
predictions.p(1, 0.f);
predictions.p(2, 0.f);
predictions.p(3, 0.);
labels.p(0, 0.f);
labels.p(1, 0.f);
labels.p(2, 0.f);
labels.p(3, 0.f);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<float>(0), 1.93333f, 1e-5);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_19) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == 1.);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_20) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == 1.);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_21) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,1,1});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 1.f);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_22) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == 0.);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_23) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4,5});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4,5});
auto weights = NDArrayFactory::create<float>('c', {2,3,4,5});
labels.linspace(1);
predictions.linspace(3);
weights.assign(0.5);
predictions.p(0, 0.);
predictions.p(1, 0.);
predictions.p(2, 0.);
predictions.p(3, 0.);
labels.p(0, 0.);
labels.p(1, 0.);
labels.p(2, 0.);
labels.p(3, 0.);
weights.p(40+0, 0.);
weights.p(40+1, 0.);
weights.p(40+2, 0.);
weights.p(40+3, 0.);
nd4j::ops::absolute_difference_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.965517, 1e-5);
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test1) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,3,4});
auto expected = NDArrayFactory::create<float>('c', {1,3,4}, {-91.5,-107.5,-125.5,-145.5, -167.5,-191.5,-217.5,-245.5, -275.5,-307.5,-341.5,-377.5});
labels.linspace(1);
predictions.linspace(2);
weights.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0,0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test2) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,1,4});
auto expected = NDArrayFactory::create<float>('c', {2,1,4}, {-3.25, -4., -4.75, -5.5,-12.25,-13.,-13.75,-14.5});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0,1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test3) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,1});
auto expected = NDArrayFactory::create<float>('c', {2,3,1}, {-2., -6.,-10.,-14.,-18.,-22.});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
////////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test4) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
auto expected = NDArrayFactory::create<float>('c', {2,3,1}, {-2., -6.,-10.,-14.,-18.,-22.});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test5) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,1,4});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1,1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == -71.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test6) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1,1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == -71.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test7) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1,4});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1,0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == -69.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test8) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,1});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<float>(0) == -24.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test9) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == -24.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, cosine_distance_loss_test10) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4});
auto predictions = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,1});
labels.linspace(1);
weights.assign(0.5);
predictions.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
nd4j::ops::cosine_distance_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2,2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == -32.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test1) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,4});
auto expected = NDArrayFactory::create<float>('c', {2,3,4}, {1., 0. , 0., 2.5,0., 3.5, 0., 4.5,0., 5.5, 0., 6.5, 0., 7.5, 0., 8.5,0., 9.5,10., 0. ,0.,11.5, 0.,12.5});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
// result->printBuffer();
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test2) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
auto expected = NDArrayFactory::create<float>('c', {2,3,4}, {1., 0. , 0., 2.5,0., 3.5, 0., 4.5,0., 5.5, 0., 6.5, 0., 7.5, 0., 8.5,0., 9.5,10., 0. ,0.,11.5, 0.,12.5});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
// result->printBuffer();
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test3) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,3,1});
auto expected = NDArrayFactory::create<float>('c', {2,3,4}, {1., 0. , 0., 2.5,0., 3.5, 0., 4.5,0., 5.5, 0., 6.5, 0., 7.5, 0., 8.5,0., 9.5,10., 0. ,0.,11.5, 0.,12.5});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
// result->printBuffer();
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test4) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,4});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == 83.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test5) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == 83.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test6) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,1,1});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == 83.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test7) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,4});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 6.91667, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test8) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 6.91667, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test9) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1,4});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 6.91667, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test10) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,4});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 3.45833, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test11) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,1,4});
logits.linspace(1);
weights.assign(0.5);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 3.45833, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test12) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {2,3,4});
logits.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 3.975, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, hinge_loss_test13) {
auto labels = NDArrayFactory::create<float>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<float>('c', {2,3,4});
auto weights = NDArrayFactory::create<float>('c', {1,1});
logits.linspace(1);
weights.assign(0.);
nd4j::ops::hinge_loss op;
auto results = op.execute({&logits, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_TRUE(result->e<double>(0) == 0.);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test1) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.0425 ,0.0875 ,0.13250001,0.17749999,0.22250001,0.26750001,0.31250003,0.35749999,0.4025 ,0.44749999,0.49249998,0.53750002, 0.58249998,0.6275 ,0.67250001,0.71749997,0.76249999,0.8075 ,0.85250002,0.89749998,0.9425 ,0.98749995,1.03250015,1.0775001});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test2) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.0425 ,0.0875 ,0.13250001,0.17749999,0.22250001,0.26750001,0.31250003,0.35749999,0.4025 ,0.44749999,0.49249998,0.53750002, 0.58249998,0.6275 ,0.67250001,0.71749997,0.76249999,0.8075 ,0.85250002,0.89749998,0.9425 ,0.98749995,1.03250015,1.0775001});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test3) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.0425 ,0.0875 ,0.13250001,0.17749999,0.22250001,0.26750001,0.31250003,0.35749999,0.4025 ,0.44749999,0.49249998,0.53750002, 0.58249998,0.6275 ,0.67250001,0.71749997,0.76249999,0.8075 ,0.85250002,0.89749998,0.9425 ,0.98749995,1.03250015,1.0775001});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test4) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 13.44, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test5) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 13.44, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test6) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 1.12, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test7) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1,1});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 1.12, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test8) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 1.3, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test9) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.56, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test10) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.56, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, huber_loss_test11) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
labels.linspace(0.1, 0.1);
predictions.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::huber_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {0.1}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.65, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test1) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {1.60943663, 2.48403668, 3.05256081, 3.40363169, 3.57730675, 3.59525585, 3.46986699, 3.20791793, 2.81228209, 2.28273821, 1.61630058, 0.80721998, -0.15329313, -1.27764463, -2.5828433 , -4.09208679, -5.83734226, -7.8636713 ,-10.23689461,-13.05822182,-16.49509811,-20.85659218,-26.82411766,-36.52717209});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test2) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {1.60943663, 2.48403668, 3.05256081, 3.40363169, 3.57730675, 3.59525585, 3.46986699, 3.20791793, 2.81228209, 2.28273821, 1.61630058, 0.80721998, -0.15329313, -1.27764463, -2.5828433 , -4.09208679, -5.83734226, -7.8636713 ,-10.23689461,-13.05822182,-16.49509811,-20.85659218,-26.82411766,-36.52717209});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test3) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
NDArray weights(nd4j::DataType::DOUBLE);
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {1.60943663, 2.48403668, 3.05256081, 3.40363169, 3.57730675, 3.59525585, 3.46986699, 3.20791793, 2.81228209, 2.28273821, 1.61630058, 0.80721998, -0.15329313, -1.27764463, -2.5828433 , -4.09208679, -5.83734226, -7.8636713 ,-10.23689461,-13.05822182,-16.49509811,-20.85659218,-26.82411766,-36.52717209});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test4) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -113.886429, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test5) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3,1});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -113.886429, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test6) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
NDArray weights(nd4j::DataType::DOUBLE);
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -113.886429, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test7) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -9.490536, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test8) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3,1});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -9.490536, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test9) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
NDArray weights(nd4j::DataType::DOUBLE);
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -9.490536, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test10) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -12.443609, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test11) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -4.745268, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test12) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -4.745268, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, log_loss_test13) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.04, 0.04);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::log_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -6.221805, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test1) {
auto labels = NDArrayFactory::create<double>('c', {1,3}, {0., 0.5, 1.});
auto predictions = NDArrayFactory::create<double>('c', {1,3}, {1., 1., 1.});
auto weights = NDArrayFactory::create<double>('c', {1,1}, {1});
auto expected = NDArrayFactory::create<double>('c', {1,1}, {1.});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test2) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {-0.5533444483384939, -0.4045807428083095, -0.38990808632111873, -1.3367815555936828, 2.2110825342567204, -0.3322538938773163, 0.5683588435736076, 1.401524673423209, -0.2216208609234102, -0.23645194877057543, -1.9319189398422172, 0.6106128799796062, 1.6973842275926025, -2.8306371397325553E-4, -1.1550401544465256, -0.08357706614294765, -0.27784822018757077, 0.8290894318337857, 1.6484476009013025, -0.7752524785358668, -0.9700596207063842, 3.0809371469543207, -0.23684959888998405, 0.22403535560739518, 0.6146150452128438, -1.1250088686147994, -0.5915314787415693, -0.0944090155356556, 0.7995514825959854, -1.2290496239142903, -1.8329592004926936, -0.1694821152623061, -1.7614978090471403, 0.07929168376086736, 0.4086255139492943, 2.045562727396195, -0.48701853719962834, 0.10304152395720723, -0.8993147347502636, -0.49078404206110715});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-0.5982871220907984, 1.2010665656903237, 0.30243355682445544, -0.2070857400459659, 0.6962389393180044, -0.5878034128580758, 0.8325626284025988, -0.3555823702782838, -0.7099759151434476, 1.7971905051128672, -1.1018498592680859, 0.008705918349147959, -1.713038986676157, 0.5029671900704719, 0.7491261275031563, -0.34800067781360444, -1.3529065441284513, -0.6075230577852321, -0.6153583973120907, 1.6014780660677996, 0.6444219215516616, 0.7925830851904783, -0.5006063079380708, 1.7812300901376552, 0.4736193941708224, 1.411502849640833, 0.9555142545037492, -0.03936687661890644, 1.31661624967917, 0.7344531724786305, 0.8388550872918745, 0.7010030219905558, -0.5442944240155373, 0.4437344837841118, -1.7502823958671712, -1.9271369730241665, 0.9256612923554498, 1.9065401403827893, 0.42450175148842717, -0.11783183865542822});
auto weights = NDArrayFactory::create<double>('c', {1,1}, {1});
auto expected = NDArrayFactory::create<double>('c', {10,1}, {1.9665822560405073, 3.806679563402927, 6.185624212589066, 20.237895345263905, 16.739700814450472, 13.655430201400929, 6.473256392322658, 3.9337379694106325, 22.509455553531062, 1.4741234749089487});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test3) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {0.9165069946629816, 0.166426191704143, 0.13873357227527264, -0.5986162145785378, 0.4763504550662989, 1.2259816058633732, -0.4653205175596491, -1.7447031523970766, 1.349525448316014, 2.433089865629357, -2.54858150221601, -0.6060282162911894, 0.2625377104613349, -0.5007107584102752, 0.9576065700956302, -0.35787770401703584, -0.2608532564720665, 0.65688909921908, -0.1705876431948587, 1.2052884124800949, -0.976783296084278, 1.1163504624016534, -0.10545986164581109, -1.0632271027867568, 0.26460250034147065, -0.2299030354616135, -0.418989869909565, 0.7954060747536896, 0.37934127200736545, 0.8550487997440007, 0.2984909806904042, 0.1329065864221682, 1.478600294413247, 0.05421279873635542, -1.0552978360622536, -0.743808639782604, -1.3371851696151362, 2.7752972493355963, -1.6107187893743549, 1.5030902829432997});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-3.398114657004427, 0.40587455906092945, 1.587706448479039, 0.27394335709083156, 1.0463122023764637, -0.6552570653663903, -0.26929204111727345, -2.710461824817806, 0.9141296064806023, -0.7632270851454939, -0.4077235519855459, 0.5555107559107472, -0.6776140976423888, 1.2422270521180823, 0.2372445100636733, 0.08522757123963924, -2.708523129389936, 0.09738215252575103, -0.8797837670498875, 0.8714091607391934, -0.628958978867591, 0.49380147969660415, -0.6663578349373824, 0.14570184758600965, -0.4710388511314244, 0.7708214742640788, 0.06836525442683238, -1.2786368797129386, -0.5077556003990912, 0.45383439418987664, 1.1686877788409553, -0.3078567969393852, -2.2375730522738198, 1.0108200459611192, 0.21955367964983963, 1.2268011099696847, 0.48061693077695455, -0.5306373077054981, 1.5005367299570744, -2.1005486985463966});
auto weights = NDArrayFactory::create<double>('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0});
auto expected = NDArrayFactory::create<double>('c', {10,1}, {0.0, 0.0, 21.748459867092496, 6.090581568657439, 7.51315897553838, 5.999534225166869, 22.58050883748054, 6.8600435676788605, 107.5976928688877, 191.56864939172544});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test4) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {-1.9540657282602247, -0.37099621218123746, 0.24959541842365968, 0.4125896396216978, -0.8661959659606203, 0.3651479206362867, -1.7475031047706964, -1.0962133982440159, 0.8451229874730279, 0.6876932162478913, 1.2598782790596628, 0.9372328828104118, 1.383555504464105, -0.816048166961237, 0.009041816630426176, -0.004376554457540983, -0.2386352931506252, -0.6494407817111416, 1.7888273635934742, -1.2157303560822368, -0.2446697859467434, -0.3040881765177774, -0.25843499040765916, -0.16479617511053568, 1.8063435075905592, 0.36002291874022285, -0.43317974028771883, 1.070086390817373, -1.0788479808458253, -0.3364318348487324, -0.859106579072977, 0.43984270049845064, -0.23662331183489546, -1.263417124724063, -0.3123732566483939, -0.125249623799724, -1.951308433393268, -0.4925779190927575, -1.081735149025745, -1.9910331435034687});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-1.7053977111021588, 1.7704125629388408, -0.0876171627499475, 0.9428762101237441, 0.9080108618240852, -0.478732892339118, -0.8189639230649537, 1.3359668242925342, -0.07499867017894829, 0.6169780756804321, -1.1891117691972148, -0.319354110980483, -1.4287263424900434, -0.3556443786879834, 0.6389682186473912, 0.3161742985911756, 0.9047447733840537, -1.9974117226910393, 2.1067775658502326, 0.17035521714679938, -1.1393894489992826, 1.4570837278971687, 0.6312249731754015, -0.42793125692777634, -1.0685964336386844, -0.3590636581851568, -0.19147354841437528, -0.10128937266756889, -0.5714869078294972, 0.2682604831358205, 0.6608524575561853, 0.35658907103040305, -0.7053263272861181, -0.6318441042427088, 2.131292677079184, -0.3624048087249232, 1.6008209804575328, 0.1245980660014825, 1.0685424462364297, -0.5672594432046791});
auto weights = NDArrayFactory::create<double>('c', {1,1}, {1});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 60.74394998193965, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test5) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {0.9165069946629816, 0.166426191704143, 0.13873357227527264, -0.5986162145785378, 0.4763504550662989, 1.2259816058633732, -0.4653205175596491, -1.7447031523970766, 1.349525448316014, 2.433089865629357, -2.54858150221601, -0.6060282162911894, 0.2625377104613349, -0.5007107584102752, 0.9576065700956302, -0.35787770401703584, -0.2608532564720665, 0.65688909921908, -0.1705876431948587, 1.2052884124800949, -0.976783296084278, 1.1163504624016534, -0.10545986164581109, -1.0632271027867568, 0.26460250034147065, -0.2299030354616135, -0.418989869909565, 0.7954060747536896, 0.37934127200736545, 0.8550487997440007, 0.2984909806904042, 0.1329065864221682, 1.478600294413247, 0.05421279873635542, -1.0552978360622536, -0.743808639782604, -1.3371851696151362, 2.7752972493355963, -1.6107187893743549, 1.5030902829432997});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-3.398114657004427, 0.40587455906092945, 1.587706448479039, 0.27394335709083156, 1.0463122023764637, -0.6552570653663903, -0.26929204111727345, -2.710461824817806, 0.9141296064806023, -0.7632270851454939, -0.4077235519855459, 0.5555107559107472, -0.6776140976423888, 1.2422270521180823, 0.2372445100636733, 0.08522757123963924, -2.708523129389936, 0.09738215252575103, -0.8797837670498875, 0.8714091607391934, -0.628958978867591, 0.49380147969660415, -0.6663578349373824, 0.14570184758600965, -0.4710388511314244, 0.7708214742640788, 0.06836525442683238, -1.2786368797129386, -0.5077556003990912, 0.45383439418987664, 1.1686877788409553, -0.3078567969393852, -2.2375730522738198, 1.0108200459611192, 0.21955367964983963, 1.2268011099696847, 0.48061693077695455, -0.5306373077054981, 1.5005367299570744, -2.1005486985463966});
auto weights = NDArrayFactory::create<double>('c', {1,1}, {1});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 15.189082270182983, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test6) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {0.7712557146220891, 0.37344724586647443, -1.465944048516541, 0.3226845250222374, 0.3153238532645865, -0.6453963287132424, -1.7695663855309438, -0.31350813714835285, 0.6209850696184357, -1.0632582557661083, 0.8971205782356552, -0.7361143357044725, 0.4349813432397299, 1.1012674501462072, -1.846028584047857, -0.04711049067212126, 0.3511384383511822, -1.5908669452488973, 0.6271232025632083, -0.5370025878354387, 0.09775855957778733, 0.8465118033582384, -0.5118005514773271, -0.8215749768059044, -0.5154271246850248, -0.6614138367887438, -2.721743038982485, -0.20634785234624944, 1.074134378795222, -0.515671736473577, 0.33574452224656587, -0.4258992514621533, -1.6946210614398756, 2.0853105493575246, -0.23223717047374226, -1.3145231337861756, -0.307739072607248, -0.13713627422120406, -0.05615471338688221, -0.7031780205843188});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-0.8253096544930751, 0.81324545672996, 1.2530858908292535, 0.6881658781201572, 0.11626814971230247, 0.810096847233213, -0.41726775033902014, -0.07246036077805246, -0.3491325803119671, -0.7381717490678714, -1.258884944199858, 2.6195012275145992, 0.3241066697239042, -1.3306435333372646, -0.3413119919683999, 0.13167356361127197, -0.3992424507051653, 0.14454163796541403, -2.4931643208872316, 1.8740911656038526, -2.3404306490682956, -0.8036392545918644, -1.9726177395274997, -0.20128619801149433, -1.0680828820641624, -0.6228179015361869, 1.0785520122486962, -0.26148573195062036, -0.9154287856620913, 0.6612224269248097, -0.21735407368781667, 0.5584864652543093, 1.0208212201167435, -0.7560947201084579, -0.9092906572495081, 0.47525819203475833, 1.2215678456801444, -0.39319465979983964, 1.9435677135606038, 1.4540100039010526});
auto weights = NDArrayFactory::create<double>('c', {1,1}, {1});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 13.568564090650312, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test7) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {-0.06125002348040258, 0.5143643450377119, 2.6790723358660036, -0.8032552006036418, -2.4374371040644163, -0.1562964773317163, -1.3957988654288038, 1.2791626503391635, -1.433421873294552, -1.1819478586737284, 0.05162930965054662, -0.538650473505593, -0.548171720093084, -0.3103900587344872, -2.3955103171953342, 0.7127238680062526, 0.7182079438418053, 1.1842662402382182, 0.09585189676958715, 0.9276146067349225, 0.7856673461867428, 0.41368195133354113, -0.2939280190178078, -2.400566355562181, -1.1841519118039245, -1.066170501847581, -0.9274507409610022, 1.7671863041813334, -1.2849985781031494, -1.275990164491566, -0.8866824403466698, -0.6074077385015517, 0.7647344603897107, -1.048099070426831, 0.9433828938345293, -0.5591415819237762, 1.7962773615541947, -0.42365710367758247, -0.0385518907389571, -1.109959713481321});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-0.7445687252538243, 0.2293875300325241, -1.0231630280206505, -0.18532545069458992, -0.07797403344353356, -0.9132035669873787, 0.9352296415512886, -1.7406458535354787, 0.8578334648119594, -0.6186274065269556, 0.4874824473654153, -0.9285817343788997, 0.1654680500853023, -0.6371334533926012, 1.3115245864160707, -2.072558735678832, 0.660795731844733, -0.34942292767044864, 0.05787182311194333, -0.12939210444705632, -0.6457028552461069, -0.6048992126598505, -0.17179604529778109, 1.292989642826032, -0.28867767615688045, 0.7635565516046265, -1.5464151753137487, -1.273368390129285, -1.074046012825826, -0.3534580692302915, 0.5757285568118223, 1.823271242883469, 0.31618576929075215, 0.5422847605415213, -0.7836698021860683, -0.6292022623165172, 2.1114596721927508, 0.4634986528550097, 0.08922001427846013, 1.5767749644913223});
auto weights = NDArrayFactory::create<double>('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 198.318201904499, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test8) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {1.2003157672694111, -1.0738078620687983, 1.4513396266923826, 0.5753935722952708, -0.5424028602429585, 0.9816221437385002, -1.0566397385428794, 1.503481308203513, -0.6543147953583112, 1.7453669976827346, -0.1557689124924227, 0.3387794658137257, -1.2306868494328145, -0.3299042398395769, 0.026464968146954395, -1.5077479623528403, -0.27514168845621795, 0.18739335150879793, 1.7319910646645431, 1.5228099405663476, 0.8522684742808536, 0.2362049362675063, 0.2610756525241469, 0.457998065505686, -2.7342179885912623, -0.10968795695808314, 0.581598742956297, -1.9309885922934567, -1.5775788440607954, -0.04254899350225641, -0.3125858556254039, -1.1328154327730207, 0.00566243314780096, 0.8492052576274621, 0.05945202212214481, 1.4976918834497108, 0.8869512918387292, 0.4014181932175132, -0.015512552855187248, -1.3609667909108454});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-1.1088399463364795, 0.09302972835006071, 0.033839927431215555, -0.39567507675572494, 0.8269497207597863, 1.111162272517752, 0.4930937252630912, -1.4561668998323452, 0.9417715392862969, -1.0553855492735509, 0.05848285303876081, 0.8852337518047972, -0.7472824481835305, 0.404906922583895, -0.2198309547562547, 1.9536515925189717, 0.8165036568007779, -0.19524282774410398, -0.09111693087754393, 1.1604245932512238, -0.6243762858131077, 1.4297003275591034, -0.17220079411538428, -2.3139504326793032, 0.3839796486999712, 2.0287791964679234, 0.1534441713632995, -0.6062103319229825, -0.4965880982906036, -0.373907747810053, -1.6566345746154432, 0.17534987728494222, -1.6713458890334796, 1.254628987947714, 1.914596591838086, -1.0816010467183583, 0.25033738231939673, -1.605752685708275, 1.1029112741353981, 0.3237822320282494});
auto weights = NDArrayFactory::create<double>('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 10.709003499121707, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test9) {
auto labels = NDArrayFactory::create<double>('c', {10,4}, {0.054445708809271035, 2.107634671009908, -0.7906421810578572, -1.075840781788665, 0.11881403008710377, 0.8444812915085994, -0.305754504070933, 1.6429935026781464, 0.8155105031719394, 0.04900134907242568, 0.6847004530975871, 0.23315535615893132, 0.17011663306483038, -1.1865513655938285, 1.5931597087896407, -1.7937514075547496, -0.036695307704292295, -1.6416280650778925, 1.130578912176608, -1.1267224667674058, -0.8690453889645526, 0.6717944721406133, 0.0850200492927782, 1.1294419289013125, 0.2154793028698133, 0.4557382556428947, -0.7343674069166273, -0.20013117860162175, -0.6096905108192562, 0.42022878041905926, -0.7446306649741321, 0.01724811509597817, 1.843091605690758, 1.008879504632424, 1.198292190689489, -0.4474144618813475, 0.25202981742888664, 0.07036737843407408, 1.2400630276444486, -1.1072825235557615});
auto predictions = NDArrayFactory::create<double>('c', {10,4}, {-1.6788168943811437, 1.1823653279081687, -0.3580541857004183, -0.4449970504370699, -1.3031645333940127, 0.5755013195969282, -0.7997343141774744, -0.8806735270004084, 0.9705277499376251, -1.6360067944580943, 0.12579369136710156, 1.0525902242414313, -1.625751312422252, -0.03900152587147075, 0.4112500942756277, 0.6589999986358094, 0.6144107111689617, 2.8561269030217264, 1.5299963640392247, -0.314093051147705, 1.6523278218751989, -0.5504653447714114, 0.53395260877978, 0.409795577698306, 0.4466825218051794, 1.2382059301630401, 0.4834869732526594, -0.635409128905636, -1.9343816841697272, -0.4192523056060229, -1.0662979055059818, 0.4270901960618144, -0.7391311480757151, -0.8268168961897452, -1.0855715553457785, -9.410401291588706E-4, -0.7721838774717349, 0.4784019579457375, -0.6979798841469268, -0.319729737118584});
auto weights = NDArrayFactory::create<double>('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0});
nd4j::ops::mean_pairwssqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 17.686067864414472, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test1) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.125, 0.5, 1.125, 2., 3.125, 4.5, 6.125, 8.,10.125,12.5,15.125,18.,21.125,24.5,28.125,32.,36.125,40.5,45.125,50.,55.125,60.5,66.125,72.});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test2) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.125, 0.5, 1.125, 2., 3.125, 4.5, 6.125, 8.,10.125,12.5,15.125,18.,21.125,24.5,28.125,32.,36.125,40.5,45.125,50.,55.125,60.5,66.125,72.});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test3) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1,1});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.125, 0.5, 1.125, 2., 3.125, 4.5, 6.125, 8.,10.125,12.5,15.125,18.,21.125,24.5,28.125,32.,36.125,40.5,45.125,50.,55.125,60.5,66.125,72.});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test4) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0., 0., 0., 0., 3.125, 4.5, 6.125, 8.,10.125,12.5,15.125,18.,21.125,24.5,28.125,32.,36.125,40.5,45.125,50.,55.125,60.5,66.125,72.});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test5) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 612.5, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test6) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1,4});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 612.5, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test7) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 612.5, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test8) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
weights.p(3, 0.);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 608.75, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test9) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 51.041668, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test10) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3,1});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 51.041668, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test11) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 51.041668, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test12) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 88.541664, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test13) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 25.520834, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test14) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1,4});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 25.520834, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test15) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 25.520834, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test16) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4});
auto predictions = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
predictions.linspace(0.5, 0.5);
labels.linspace(1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
nd4j::ops::mean_sqerr_loss op;
auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 44.270832, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test1) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.37219834,0.29906943,0.27717763,0.45650762,0.23703849,0.51874399,0.20159303,0.58555031,0.17057693,0.65663081,0.14366767,0.73164123,0.12050423,0.81020868,0.10070664,0.89195037,0.08389302,0.97648883,1.01969337,0.06346401,0.05775976,1.15254164,0.04777273,1.2434181 });
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test2) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1,1});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.37219834,0.29906943,0.27717763,0.45650762,0.23703849,0.51874399,0.20159303,0.58555031,0.17057693,0.65663081,0.14366767,0.73164123,0.12050423,0.81020868,0.10070664,0.89195037,0.08389302,0.97648883,1.01969337,0.06346401,0.05775976,1.15254164,0.04777273,1.2434181 });
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test3) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.37219834,0.29906943,0.27717763,0.45650762,0.23703849,0.51874399,0.20159303,0.58555031,0.17057693,0.65663081,0.14366767,0.73164123,0.12050423,0.81020868,0.10070664,0.89195037,0.08389302,0.97648883,1.01969337,0.06346401,0.05775976,1.15254164,0.04777273,1.2434181 });
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test4) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
auto expected = NDArrayFactory::create<double>('c', {2,3,4}, {0.24719833, 0.54906946, 0.65217763,-0.04349237,0.86203849,-0.23125602, 1.07659304,-0.41444966,1.29557693,-0.59336919, 1.5186677 ,-0.76835877,1.74550426,-0.93979132, 1.9757067 ,-1.10804963,2.20889306,-1.27351117,-1.35530663, 2.56346393,2.68275976,-1.59745836, 2.92277265,-1.7565819 });
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test5) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 11.2187976837, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test6) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 11.2187976837, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test7) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 11.2187976837, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test8) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 10.2187976837, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test9) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 6.06840181351, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test10) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.934899806976, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test11) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1,4});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.934899806976, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test12) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.851566493511, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test13) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 1.01140034199, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test14) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,4});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.467449903488, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test15) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.467449903488, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test16) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.425783246756, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test17) {
auto labels = NDArrayFactory::create<double>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
weights.p(0, 0.);
weights.p(1, 0.);
weights.p(2, 0.);
nd4j::ops::sigm_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {3});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 0.505700170994, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test1) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3});
auto expected = NDArrayFactory::create<double>('c', {2,3}, {1.39253557,1.44253552,1.44253552,1.44253552,1.39253557,1.44253552});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
result->printIndexedBuffer("SCEL Output");
expected.printIndexedBuffer("SCEL Expect");
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test2) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3});
auto expected = NDArrayFactory::create<double>('c', {2,3}, {-0.92835701,-1.12835705,-1.12835705,-1.12835705,-0.92835701,-1.12835705});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0}, {});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test3) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,1});
auto expected = NDArrayFactory::create<double>('c', {2,3}, {-0.92835701,-1.12835705,-1.12835705,-1.12835705,-0.92835701,-1.12835705});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test4) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3});
auto expected = NDArrayFactory::create<double>('c', {2,3}, {-0.92835701,-1.12835705,-1.12835705,-1.12835705,-0.92835701,-1.12835705});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test5) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
auto expected = NDArrayFactory::create<double>('c', {2,3}, {-0.92835701,-1.12835705,-1.12835705,-1.12835705,-0.92835701,-1.12835705});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test6) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), 8.55521392822, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test7) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {2,3});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -6.37014198303, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test8) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -6.37014198303, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test9) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -6.37014198303, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test10) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {2});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -2.12338066101, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test11) {
auto labels = NDArrayFactory::create<int>('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,3,4});
auto weights = NDArrayFactory::create<double>('c', {1,3});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {3}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -1.06169033051, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test12) {
auto labels = NDArrayFactory::create<int>('c', {2,4},{0,1,1,0,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,4});
auto weights = NDArrayFactory::create<double>('c', {2,1});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {3}, {}, false, nd4j::DataType::DOUBLE);
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(result->isScalar());
ASSERT_NEAR(result->e<double>(0), -2.18880319595, 1e-5);
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test13) {
auto labels = NDArrayFactory::create<int>('c', {2,4},{0,1,1,0,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,4});
auto weights = NDArrayFactory::create<double>('c', {2,1});
auto expected = NDArrayFactory::create<double>('c', {2,1}, {1.39253557,1.44253552});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test14) {
auto labels = NDArrayFactory::create<int>('c', {2,4},{0,1,1,0,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,4});
auto weights = NDArrayFactory::create<double>('c', {2,1});
auto expected = NDArrayFactory::create<double>('c', {2,1}, {-2.08880329, -2.28880334});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test15) {
auto labels = NDArrayFactory::create<int>('c', {2,4},{0,1,1,0,1,0,1,0});
auto logits = NDArrayFactory::create<double>('c', {2,4});
auto weights = NDArrayFactory::create<double>('c', {1,1});
auto expected = NDArrayFactory::create<double>('c', {2,1}, {-2.08880329, -2.28880334});
logits.linspace(0.1, 0.1);
weights.assign(0.5);
nd4j::ops::softmax_cross_entropy_loss op;
auto results = op.execute({&logits, &weights, &labels}, {5.}, {0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *result = results->at(0);
ASSERT_TRUE(expected.isSameShape(result));
ASSERT_TRUE(expected.equalsTo(result));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test1) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 4;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.99926789,0.99926789,0.99926789,0.99926789,0.99926789,0.99926789,0.99926789,0.99926789});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.99987108,3.99987108,3.99987108,3.99987108,3.99987108,3.99987108,3.99987108,3.99987108});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 1.}, {0, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test2) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 4;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.95867589,0.95867589,0.95867589,0.95867589,0.95867589,0.95867589,0.95867589,0.95867589});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{1.93001527,1.93001527,1.93001527,1.93001527, 1.93001527,1.93001527,1.93001527,1.93001527});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., -10.5}, {0, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test3) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 4;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0., 1.5}, {0, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test4) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 4;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0.3, 1.5}, {0, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test5) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 3;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.3,0.3,0.3,0.3,0.3,0.3});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0.3, 1.5}, {0, 1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test6) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 3;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {1.99832496,1.99832496,1.99832496,1.99832496,1.99832496,1.99832496});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.99972188,3.99972188,3.99972188,3.99972188,3.99972188,3.99972188,3.99972188,3.99972188});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 1.5}, {0, 1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test7) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 3;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.75977136,0.75977136,0.75977136,0.75977136,0.75977136,0.75977136});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0., 1.5}, {0, 1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test8) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 4;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.99930672,0.99930672,0.99930672,0.99930672, 0.99930672,0.99930672,0.99930672,0.99930672});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.99996277,3.99996277,3.99996277,3.99996277,3.99996277,3.99996277,3.99996277,3.99996277});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 10.5}, {1, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht,1e-4));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct,1e-4));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test9) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 4;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {0.99501777,0.99501777,0.99501777,0.99501777,0.99501777,0.99501777,0.99501777,0.99501777});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.,3.,3.,3.,3.,3.,3.,3.});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {3., 0., 10.5}, {1, 0});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht,1e-4));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test10) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 3;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {1.99861344,1.99861344,1.99861344,1.99861344,1.99861344,1.99861344});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.99996277, 3.99996277, 3.99996277, 3.99996277,3.99996277, 3.99996277, 3.99996277, 3.99996277});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 10.5}, {1, 1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test11) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 3;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {1.99003554,1.99003554,1.99003554,1.99003554,1.99003554,1.99003554});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.,3.,3.,3.,3.,3.,3.,3.});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {3., 0., 10.5}, {1, 1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}
///////////////////////////////////////////////////////////////////
TEST_F(DeclarableOpsTests2, lstmCell_test12) {
const int batchSize = 2;
const int inSize = 10;
const int numProj = 3;
const int numUnits = 4;
auto xt = NDArrayFactory::create<double>('c', {batchSize, inSize});
auto ht_1 = NDArrayFactory::create<double>('c', {batchSize, numProj});
auto ct_1 = NDArrayFactory::create<double>('c', {batchSize, numUnits});
auto Wx = NDArrayFactory::create<double>('c', {inSize, 4*numUnits});
auto Wh = NDArrayFactory::create<double>('c', {numProj, 4*numUnits});
auto Wc = NDArrayFactory::create<double>('c', {3*numUnits});
auto Wp = NDArrayFactory::create<double>('c', {numUnits, numProj});
auto b = NDArrayFactory::create<double>('c', {4*numUnits});
xt.assign(1.);
ht_1.assign(2.);
ct_1.assign(3.);
Wx.assign(0.5);
Wh.assign(0.5);
Wc.assign(0.5);
Wp.assign(0.5);
b.assign(0.7);
auto expHt = NDArrayFactory::create<double>('c', {batchSize, numProj}, {1.,1.,1.,1.,1.,1.});
auto expCt = NDArrayFactory::create<double>('c', {batchSize, numUnits},{3.,3.,3.,3.,3.,3.,3.,3.});
nd4j::ops::lstmCell op;
auto results = op.execute({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {3., 1.,-5.}, {1, 1});
ASSERT_EQ(ND4J_STATUS_OK, results->status());
auto *ht = results->at(0);
auto *ct = results->at(1);
ASSERT_TRUE(expHt.isSameShape(ht));
ASSERT_TRUE(expHt.equalsTo(ht));
ASSERT_TRUE(expCt.isSameShape(ct));
ASSERT_TRUE(expCt.equalsTo(ct));
delete results;
}