diff --git a/libnd4j/include/loops/legacy_ops.h b/libnd4j/include/loops/legacy_ops.h
index 5108ba4a7..7de54a858 100644
--- a/libnd4j/include/loops/legacy_ops.h
+++ b/libnd4j/include/loops/legacy_ops.h
@@ -112,7 +112,8 @@
         (4, IsInfOrNan), \
         (5, MatchConditionBool), \
         (6, IsPositive) , \
-        (7, Not)
+        (7, Not), \
+        (8, IsNegative)
 
 
 #define TRANSFORM_STRICT_OPS \
@@ -279,7 +280,8 @@
         (3, IsInfOrNan), \
         (4, IsNan), \
         (5, IsInf), \
-        (6, IsPositive)
+        (6, IsPositive), \
+        (7, IsNegative)
 
 #define REDUCE_SAME_OPS \
         (0, Sum), \
diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp
index d790dd9c2..1aa0c5249 100644
--- a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp
@@ -27,7 +27,8 @@
 namespace nd4j {
 namespace ops {
 
-CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, -2, 0) {
+////////////////////////////////////////////////////////////////////
+CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, 0, 0) {
 
     auto input  = INPUT_VARIABLE(0);
     auto output = OUTPUT_VARIABLE(0);
@@ -37,23 +38,31 @@ CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, -2, 0) {
         return Status::OK();
 
     REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_CONTRAST: Scale factor required");
-
-    const double factor = block.width() > 1 ? INPUT_VARIABLE(1)->e<double>(0) : T_ARG(0);
-
     REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST: op expects rank of input array to be >= 3, but got %i instead", input->rankOf());
     REQUIRE_TRUE(input->sizeAt(-1) == 3, 0, "ADJUST_CONTRAST: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(-1));
-    // compute mean before
+
+    NDArray* factor = nullptr;
+
+    if(block.width() > 1)
+        factor = INPUT_VARIABLE(1);
+    else {
+        factor = new NDArray(output->dataType(), block.launchContext());
+        factor->p(0, T_ARG(0));
+    }
+
     // fill up axes vector first
     std::vector<int> axes(input->rankOf() - 1);
     for (auto i = 0; i < axes.size(); ++i)
         axes[i] = i;
+
     // mean as reduction for last dimension set
     auto mean = input->reduceAlongDims(reduce::Mean, axes);
 
-    NDArray factorT(output->dataType(), block.launchContext()); // = NDArrayFactory::create(factor, block.launchContext());
-    factorT.p(0, factor);
     // this is contrast calculation
-    output->assign((*input - mean) * factorT + mean);
+    output->assign((*input - mean) * (*factor) + mean);
+
+    if(block.width() == 1)
+        delete factor;
 
     return Status::OK();
 }
@@ -64,45 +73,54 @@ DECLARE_TYPES(adjust_contrast) {
                      ->setSameMode(true);
 }
 
+////////////////////////////////////////////////////////////////////
+CONFIGURABLE_OP_IMPL(adjust_contrast_v2, 1, 1, true, 0, 0) {
 
-    CONFIGURABLE_OP_IMPL(adjust_contrast_v2, 1, 1, true, -2, 0) {
-
-        auto input  = INPUT_VARIABLE(0);
-        auto output = OUTPUT_VARIABLE(0);
-
-        REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_CONTRAST_V2: Scale factor required");
-
-        const double factor = block.width() > 1 ? INPUT_VARIABLE(1)->e<double>(0) : T_ARG(0);
-
-        // just skip op if input is empty
-        if (input->isEmpty())
-            return Status::OK();
-
-        REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST_V2: op expects rank of input array to be >= 3, but got %i instead", input->rankOf());
-        REQUIRE_TRUE(input->sizeAt(-1) == 3, 0, "ADJUST_CONTRAST_V2: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(-1));
-
-        // compute mean before
-        std::vector<int> axes(input->rankOf() - 1);
-        for (auto i = 0; i < axes.size(); ++i)
-            axes[i] = i;
-
-        // mean as reduction for last dimension set
-        auto mean = input->reduceAlongDims(reduce::Mean, axes);
-
-        // result as (x - mean) * factor + mean
-        auto temp = input->ulike();
-        input->applyTrueBroadcast(BroadcastOpsTuple::Subtract(), &mean, &temp);
-        temp.applyScalar(scalar::Multiply, factor);
-        temp.applyTrueBroadcast(BroadcastOpsTuple::Add(), &mean, output);
+    auto input  = INPUT_VARIABLE(0);
+    auto output = OUTPUT_VARIABLE(0);
 
+    // just skip op if input is empty
+    if (input->isEmpty())
         return Status::OK();
+
+    REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST_V2: op expects rank of input array to be >= 3, but got %i instead", input->rankOf());
+    REQUIRE_TRUE(input->sizeAt(-1) == 3, 0, "ADJUST_CONTRAST_V2: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(-1));
+    REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_CONTRAST_V2: Scale factor required");
+
+    NDArray* factor = nullptr;
+
+    if(block.width() > 1)
+        factor = INPUT_VARIABLE(1);
+    else {
+        factor = new NDArray(output->dataType(), block.launchContext());
+        factor->p(0, T_ARG(0));
     }
 
-    DECLARE_TYPES(adjust_contrast_v2) {
-        getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)
-                ->setAllowedOutputTypes({ALL_FLOATS})
-                ->setSameMode(true);
-    }
+    // compute mean before
+    std::vector<int> axes(input->rankOf() - 1);
+    for (auto i = 0; i < axes.size(); ++i)
+        axes[i] = i;
+
+    // mean as reduction for last dimension set
+    auto mean = input->reduceAlongDims(reduce::Mean, axes);
+
+    // result as (x - mean) * factor + mean
+    auto temp = input->ulike();
+    input->applyTrueBroadcast(BroadcastOpsTuple::Subtract(), &mean, &temp);
+    temp.applyScalarArr(scalar::Multiply, factor);
+    temp.applyTrueBroadcast(BroadcastOpsTuple::Add(), &mean, output);
+
+    if(block.width() == 1)
+        delete factor;
+
+    return Status::OK();
+}
+
+DECLARE_TYPES(adjust_contrast_v2) {
+    getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)
+            ->setAllowedOutputTypes({ALL_FLOATS})
+            ->setSameMode(true);
+}
 
 }
 }
diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp
index d1d81acf8..32e51bdb9 100644
--- a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp
@@ -24,13 +24,12 @@
 
 #include <ops/declarable/headers/parity_ops.h>
 #include <ops/declarable/helpers/adjust_hue.h>
-#include <NDArrayFactory.h>
 
 namespace nd4j {
 namespace ops {
 
 
-CONFIGURABLE_OP_IMPL(adjust_hue, 1, 1, true, 1, -2) {
+CONFIGURABLE_OP_IMPL(adjust_hue, 1, 1, true, 0, 0) {
 
     auto input  = INPUT_VARIABLE(0);
     auto output = OUTPUT_VARIABLE(0);
@@ -41,15 +40,26 @@ CONFIGURABLE_OP_IMPL(adjust_hue, 1, 1, true, 1, -2) {
 
     const int rank     = input->rankOf();
     const int dimC     = block.getIArguments()->size() > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1;
-    const double delta = T_ARG(0);
 
+    REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_HUE: delta factor is required !");
     REQUIRE_TRUE(rank >= 3, 0, "ADJUST_HUE: op expects rank of input array to be >= 3, but got %i instead", rank);
     REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "ADJUST_HUE: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(dimC));
-    REQUIRE_TRUE(-1. <= delta && delta <= 1., 0, "ADJUST_HUE: parameter delta must be within [-1, 1] interval, but got %f instead", delta);
 
-    NDArray deltaScalarArr = NDArrayFactory::create<double>(delta, block.launchContext());
+    NDArray* delta = nullptr;
 
-    helpers::adjustHue(block.launchContext(), input, &deltaScalarArr, output, dimC);
+    if(block.width() > 1)
+        delta = INPUT_VARIABLE(1);
+    else {
+        delta = new NDArray(output->dataType(), block.launchContext());
+        delta->p(0, T_ARG(0));
+    }
+
+    REQUIRE_TRUE(-1. <= delta->e<double>(0) && delta->e<double>(0) <= 1., 0, "ADJUST_HUE: parameter delta must be within [-1, 1] interval, but got %f instead", delta);
+
+    helpers::adjustHue(block.launchContext(), input, delta, output, dimC);
+
+    if(block.width() == 1)
+        delete delta;
 
     return Status::OK();
 }
diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp
index 5030e5952..de947c9ae 100644
--- a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp
@@ -28,7 +28,7 @@
 namespace nd4j {
 namespace ops {
 
-CONFIGURABLE_OP_IMPL(adjust_saturation, 1, 1, true, 1, -2) {
+CONFIGURABLE_OP_IMPL(adjust_saturation, 1, 1, true, 0, 0) {
 
     auto input  = INPUT_VARIABLE(0);
     auto output = OUTPUT_VARIABLE(0);
@@ -37,16 +37,26 @@ CONFIGURABLE_OP_IMPL(adjust_saturation, 1, 1, true, 1, -2) {
     if (input->isEmpty())
         return Status::OK();
 
-    const int rank     = input->rankOf();
-    const int dimC     = block.getIArguments()->size() > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1;
-    const double factor = T_ARG(0);
+    const int rank = input->rankOf();
+    const int dimC = block.getIArguments()->size() > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1;
 
     REQUIRE_TRUE(rank >= 3, 0, "ADJUST_SATURATION: op expects rank of input array to be >= 3, but got %i instead", rank);
     REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "ADJUST_SATURATION: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(dimC));
+    REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_SATURATION: scale factor is required !");
 
-    NDArray factorScalarArr = NDArrayFactory::create<double>(factor, block.launchContext());
+    NDArray* factor = nullptr;
 
-    helpers::adjustSaturation(block.launchContext(), input, &factorScalarArr, output, dimC);
+    if(block.width() > 1)
+        factor = INPUT_VARIABLE(1);
+    else {
+        factor = new NDArray(output->dataType(), block.launchContext());
+        factor->p(0, T_ARG(0));
+    }
+
+    helpers::adjustSaturation(block.launchContext(), input, factor, output, dimC);
+
+    if(block.width() == 1)
+        delete factor;
 
     return Status::OK();
 }
diff --git a/libnd4j/include/ops/declarable/helpers/polyGamma.h b/libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp
similarity index 57%
rename from libnd4j/include/ops/declarable/helpers/polyGamma.h
rename to libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp
index 5681d68b7..8a2894be7 100644
--- a/libnd4j/include/ops/declarable/helpers/polyGamma.h
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp
@@ -1,5 +1,6 @@
 /*******************************************************************************
  * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2019 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -15,27 +16,35 @@
  ******************************************************************************/
 
 //
-// Created by Yurii Shyrma on 13.12.2017.
+// @author Yurii Shyrma (iuriish@yahoo.com)
 //
 
-#ifndef LIBND4J_POLYGAMMA_H
-#define LIBND4J_POLYGAMMA_H
+#include <op_boilerplate.h>
+#if NOT_EXCLUDED(OP_digamma)
 
-#include <ops/declarable/helpers/helpers.h>
-#include "NDArray.h"
+#include <ops/declarable/CustomOperations.h>
+#include <ops/declarable/helpers/gammaMathFunc.h>
 
 namespace nd4j {
-namespace ops {
-namespace helpers {
+namespace ops  {
 
+CONFIGURABLE_OP_IMPL(digamma, 1, 1, false, 0, 0) {
 
-	// calculate the polygamma function
-    void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output);
-    
+    auto x = INPUT_VARIABLE(0);
+    auto z = OUTPUT_VARIABLE(0);
+
+    helpers::diGamma(block.launchContext(), *x, *z);
+
+    return Status::OK();
+}
+
+DECLARE_TYPES(digamma) {
+    getOpDescriptor()
+            ->setAllowedInputTypes({ALL_FLOATS, ALL_INTS})
+            ->setSameMode(true);
+}
 
 }
 }
-}
 
-
-#endif //LIBND4J_POLYGAMMA_H
+#endif
\ No newline at end of file
diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp
index 0f850cd4b..1cfd86a26 100644
--- a/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp
@@ -15,14 +15,14 @@
  ******************************************************************************/
 
 //
-// @author Yurii Shyrma (iuriish@yahoo.com), created on 13.12.2017
+// @author Yurii Shyrma (iuriish@yahoo.com)
 //
 
 #include <op_boilerplate.h>
 #if NOT_EXCLUDED(OP_polygamma)
 
 #include <ops/declarable/CustomOperations.h>
-#include <ops/declarable/helpers/polyGamma.h>
+#include <ops/declarable/helpers/gammaMathFunc.h>
 
 namespace nd4j {
 namespace ops  {
@@ -37,11 +37,11 @@ CONFIGURABLE_OP_IMPL(polygamma, 2, 1, false, 0, 0) {
 
     Nd4jLong arrLen = n->lengthOf();
     // FIXME: this shit should be single op call, not a loop!
-    auto nPositive =  n->reduceNumber(nd4j::reduce::IsPositive, nullptr);
-    auto xPositive =  x->reduceNumber(nd4j::reduce::IsPositive, nullptr);
-    bool nPositiveFlag = nPositive.e<bool>(0);
-    bool xPositiveFlag = xPositive.e<bool>(0);
-    REQUIRE_TRUE(nPositiveFlag, 0, "POLYGAMMA op: all elements of n array must be > 0 !");
+    auto nNegative = n->reduceNumber(nd4j::reduce::IsNegative, nullptr);
+    auto xPositive = x->reduceNumber(nd4j::reduce::IsPositive, nullptr);
+    bool nPositiveFlag = !nNegative.e<bool>(0);                             // require all n >= 0
+    bool xPositiveFlag = xPositive.e<bool>(0);                              // require all x > 0
+    REQUIRE_TRUE(nPositiveFlag, 0, "POLYGAMMA op: all elements of n array must be >= 0 !");
     REQUIRE_TRUE(xPositiveFlag, 0, "POLYGAMMA op: all elements of x array must be > 0 !");
 
     helpers::polyGamma(block.launchContext(), *n, *x, *output);
diff --git a/libnd4j/include/ops/declarable/headers/parity_ops.h b/libnd4j/include/ops/declarable/headers/parity_ops.h
index 0d354630c..e56ba9d6e 100644
--- a/libnd4j/include/ops/declarable/headers/parity_ops.h
+++ b/libnd4j/include/ops/declarable/headers/parity_ops.h
@@ -513,7 +513,6 @@ namespace nd4j {
         /**
         * This op calculates polygamma function psi^(n)(x). Implementation is based on serial representation written in
         * terms of the Hurwitz zeta function: polygamma = (-1)^{n+1} * n! * zeta(n+1, x).
-        * Currently the case n = 0 is not supported.
         *
         * Input arrays:
         *    0: n - define derivative order (n+1), type integer (however currently is implemented as float casted to integer)
@@ -528,6 +527,20 @@ namespace nd4j {
         DECLARE_CONFIGURABLE_OP(polygamma, 2, 1, false, 0, 0);
         #endif
 
+        /**
+        * This op calculates digamma function psi(x) = derivative of log(Gamma(x))
+        *
+        * Input arrays:
+        *    0: x - abscissa points where to evaluate the digamma function, type float
+        *
+        * Output array:
+        *    0: values of digamma function at corresponding x, type float
+        *
+        */
+        #if NOT_EXCLUDED(OP_digamma)
+        DECLARE_CONFIGURABLE_OP(digamma, 1, 1, false, 0, 0);
+        #endif
+
         /**
          * This operation takes shape as first argument, and returns new NDArray filled with specific scalar value.
          * Input arrays:
@@ -575,44 +588,47 @@ namespace nd4j {
          * This operation adjusts image hue by delta
          * Input arrays:
          * 0 - input array with rank >= 3, must have at least one dimension equal 3, that is dimension containing channels.
+         * 1 - optional argument, input scalar-array containing delta
          *
          * T arguments:
-         * 0 - delta value
+         * 0 - optional argument, delta value
          *
          * Int arguments:
          * 0 - optional argument, corresponds to dimension with 3 channels
          */
         #if NOT_EXCLUDED(OP_adjust_hue)
-        DECLARE_CONFIGURABLE_OP(adjust_hue, 1, 1, true, 1, -2);
+        DECLARE_CONFIGURABLE_OP(adjust_hue, 1, 1, true, 0, 0);
         #endif
 
         /**
          * This operation adjusts image saturation by delta
          * Input arrays:
          * 0 - input array with rank >= 3, must have at least one dimension equal 3, that is dimension containing channels.
+         * 1 - optional argument, input scalar-array containing saturation factor
          *
          * T arguments:
-         * 0 - saturation factor
+         * 0 - optional argument, saturation factor
          *
          * Int arguments:
          * 0 - optional argument, corresponds to dimension with 3 channels
          */
         #if NOT_EXCLUDED(OP_adjust_saturation)
-        DECLARE_CONFIGURABLE_OP(adjust_saturation, 1, 1, true, 1, -2);
+        DECLARE_CONFIGURABLE_OP(adjust_saturation, 1, 1, true, 0, 0);
         #endif
 
         /**
          * This operation adjusts image contrast by given factor ( z = (x - mean) * factor + mean )
          * Input arrays:
          * 0 - input array with rank >= 3, must have last one dimension equal 3, that is dimension containing channels.
+         * 1 - optional argument, input scalar-array containing saturation contrast factor
          *
          * T arguments:
-         * 0 - contrast factor
+         * 0 - optional argument, contrast factor
          *
          */
         #if NOT_EXCLUDED(OP_adjust_contrast)
-        DECLARE_CONFIGURABLE_OP(adjust_contrast, 1, 1, true, -2, 0);
-        DECLARE_CONFIGURABLE_OP(adjust_contrast_v2, 1, 1, true, -2, 0);
+        DECLARE_CONFIGURABLE_OP(adjust_contrast, 1, 1, true, 0, 0);
+        DECLARE_CONFIGURABLE_OP(adjust_contrast_v2, 1, 1, true, 0, 0);
         #endif
 
 
@@ -1832,7 +1848,7 @@ namespace nd4j {
         #endif
 
         /**
-         * compare_and_bitpack - compare with greater and pack result with uint8 
+         * compare_and_bitpack - compare with greater and pack result with uint8
          *
          * input params:
          *    0 - NDArray (input)
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp b/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp
index ddd1ad892..88186b62a 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp
@@ -107,12 +107,12 @@ static T betaIncCore(T a, T b, T x) {
 		return x;
 
 	const T gammaPart = lgamma(a) + lgamma(b) - lgamma(a + b);
-    const T front = math::nd4j_exp<T,T>(math::nd4j_log<T, T>(x) * a + math::nd4j_log<T, T>(1 - x) * b - gammaPart) / a;
+    const T front = math::nd4j_exp<T,T>(math::nd4j_log<T, T>(x) * a + math::nd4j_log<T, T>(1.f - x) * b - gammaPart);
 
 	if (x <= (a + static_cast<T>(1)) / (a + b + static_cast<T>(2)))
-		return front * continuedFraction(a, b, x);
+		return front * continuedFraction(a, b, x) / a;
 	else // symmetry relation
-		return static_cast<T>(1) - front * continuedFraction(b, a, static_cast<T>(1) - x);
+		return static_cast<T>(1) - front * continuedFraction(b, a, static_cast<T>(1) - x) / b;
 
 }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp b/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp
new file mode 100644
index 000000000..8035f8216
--- /dev/null
+++ b/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp
@@ -0,0 +1,53 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2019 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//
+// @author Yurii Shyrma (iuriish@yahoo.com)
+//
+
+#include<ops/declarable/helpers/gammaMathFunc.h>
+#include <execution/Threads.h>
+
+namespace nd4j {
+namespace ops {
+namespace helpers {
+
+//////////////////////////////////////////////////////////////////////////
+// calculate digamma function for array elements
+template <typename T>
+static void diGamma_(const NDArray& x, NDArray& z) {
+
+	auto func = PRAGMA_THREADS_FOR {
+        for (auto i = start; i < stop; i += increment)
+            z.p(i, diGammaScalar<T>(x.e<T>(i)));
+    };
+	samediff::Threads::parallel_for(func, 0, x.lengthOf());
+}
+
+void diGamma(nd4j::LaunchContext* context, const NDArray& x, NDArray& z) {
+
+	BUILD_SINGLE_SELECTOR(x.dataType(), diGamma_, (x, z), FLOAT_TYPES);
+}
+
+BUILD_SINGLE_TEMPLATE(template void diGamma_, (const NDArray& x, NDArray& z), FLOAT_TYPES);
+
+
+
+}
+}
+}
+
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp b/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp
index cb97ffe1e..fc572677e 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp
@@ -18,7 +18,7 @@
 // Created by Yurii Shyrma on 12.12.2017
 //
 
-#include<ops/declarable/helpers/polyGamma.h>
+#include<ops/declarable/helpers/gammaMathFunc.h>
 #include<ops/declarable/helpers/zeta.h>
 #include <NDArrayFactory.h>
 #include <execution/Threads.h>
@@ -42,7 +42,7 @@ static FORCEINLINE T getFactorial(const int n) {
 
 	for(int i = 2; i <= n; ++i)
 		result *= i;
-	
+
 	return result;
 }
 
@@ -50,17 +50,15 @@ static FORCEINLINE T getFactorial(const int n) {
 // implementation is based on serial representation written in terms of the Hurwitz zeta function as polygamma = (-1)^{n+1} * n! * zeta(n+1, x)
 template <typename T>
 static FORCEINLINE T polyGammaScalar(nd4j::LaunchContext * context, const int n, const T x) {
-	
-	// if (n < 0) 
+
+	// if (n < 0)
 	// 	throw("polyGamma function: n must be >= 0 !");
 
-	// if (x <= (T)0.) 
+	// if (x <= (T)0.)
 	// 	throw("polyGamma function: x must be > 0 !");
-	
-	// TODO case for n = 0 (digamma)
 
 	int sign = (n + 1) % 2  ?  -1 : 1;
-	// T factorial = (T)std::tgamma(n + 1);		
+	// T factorial = (T)std::tgamma(n + 1);
 
 	return sign * getFactorial<T>(n) * zetaScalar<T>((T)(n + 1), x);
 }
@@ -71,17 +69,18 @@ static FORCEINLINE T polyGammaScalar(nd4j::LaunchContext * context, const int n,
 template <typename T>
 static void polyGamma_(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output) {
 
-	NDArray& result = output;
-
-	int xLen = x.lengthOf();
-
 	auto func = PRAGMA_THREADS_FOR {
-        for (auto i = start; i < stop; i += increment)
-            result.p(i, polyGammaScalar<T>(context, n.e<int>(i), x.e<T>(i)));
+        for (auto i = start; i < stop; i += increment) {
+        	const T order = n.e<T>(i);
+        	if(order != static_cast<int>(order))						// if order has fractional part then do not perform calculations and return NAN
+        		output.p(i, std::numeric_limits<T>::quiet_NaN());
+        	else if (order == 0)										// polygamma function of zero order is digamma function
+        		output.p(i, diGammaScalar<T>(x.e<T>(i)));
+        	else
+            	output.p(i, polyGammaScalar<T>(context, order, x.e<T>(i)));
+        }
     };
 	samediff::Threads::parallel_for(func, 0, x.lengthOf());
-
-//	return result;
 }
 
 	void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output) {
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu b/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu
index 90619c76c..e7541a005 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu
@@ -89,20 +89,6 @@ __device__ T continuedFractionCuda(const T a, const T b, const T x) {
     return 1.f / 0.f;	// no convergence, more iterations is required
 }
 
-///////////////////////////////////////////////////////////////////
-// evaluates incomplete beta function for positive a and b, and x between 0 and 1.
-template <typename T>
-__device__ T betaIncCoreCuda(T a, T b, T x) {
-
-	const T gammaPart = lgamma(a) + lgamma(b) - lgamma(a + b);
-    const T front = math::nd4j_exp<T,T>(math::nd4j_log<T, T>(x) * a + math::nd4j_log<T, T>(1 - x) * b - gammaPart) / a;
-
-	if (x <= (a + static_cast<T>(1)) / (a + b + static_cast<T>(2)))
-		return front * continuedFractionCuda(a, b, x);
-	else  // symmetry relation
-		return static_cast<T>(1) - front * continuedFractionCuda(b, a, static_cast<T>(1) - x);
-}
-
 ///////////////////////////////////////////////////////////////////
 template<typename T>
 __global__ void betaIncForArrayCuda(const void* va, const Nd4jLong* aShapeInfo,
@@ -115,12 +101,21 @@ __global__ void betaIncForArrayCuda(const void* va, const Nd4jLong* aShapeInfo,
 
     const Nd4jLong j = blockIdx.x;			// one block per each element
 
-    Nd4jLong len = shape::length(xShapeInfo);
+    T& z = *(reinterpret_cast<T*>(vz) + shape::getIndexOffset(j, zShapeInfo));
 
-    const T  a = *(reinterpret_cast<const T*>(va) + shape::getIndexOffset(j, aShapeInfo));
-    const T  b = *(reinterpret_cast<const T*>(vb) + shape::getIndexOffset(j, bShapeInfo));
-    const T  x = *(reinterpret_cast<const T*>(vx) + shape::getIndexOffset(j, xShapeInfo));
-    	  T& z = *(reinterpret_cast<T*>(vz) 	  + shape::getIndexOffset(j, zShapeInfo));
+    __shared__ T a, b, x;
+    __shared__ bool symmCond;
+
+    if (threadIdx.x == 0) {
+
+    	a = *(reinterpret_cast<const T*>(va) + shape::getIndexOffset(j, aShapeInfo));
+    	b = *(reinterpret_cast<const T*>(vb) + shape::getIndexOffset(j, bShapeInfo));
+    	x = *(reinterpret_cast<const T*>(vx) + shape::getIndexOffset(j, xShapeInfo));
+
+    	symmCond = x <= (a + static_cast<T>(1)) / (a + b + static_cast<T>(2));
+
+    }
+    __syncthreads();
 
     // t^{n-1} * (1 - t)^{n-1} is symmetric function with respect to x = 0.5
    	if(a == b && x == static_cast<T>(0.5)) {
@@ -135,17 +130,31 @@ __global__ void betaIncForArrayCuda(const void* va, const Nd4jLong* aShapeInfo,
 
    	if(threadIdx.x % 2 == 0) { 	/***** even part *****/
 		const int m = threadIdx.x + 1;
-		sharedMem[threadIdx.x] = m * (b - m) * x / ((a + 2 * m - static_cast<T>(1)) * (a + 2 * m));
+		if(symmCond)
+			sharedMem[threadIdx.x] = m * (b - m) * x / ((a + 2 * m - static_cast<T>(1)) * (a + 2 * m));
+		else
+			sharedMem[threadIdx.x] = m * (a - m) * (1.f-x) / ((b + 2 * m - static_cast<T>(1)) * (b + 2 * m));
 	}
 	else {						/***** odd part *****/
 		const int m = threadIdx.x;
-		sharedMem[threadIdx.x] = -(a + m) * (a + b + m) * x / ((a + 2 * m + static_cast<T>(1)) * (a + 2 * m));
+		if(symmCond)
+			sharedMem[threadIdx.x] = -(a + m) * (a + b + m) * x / ((a + 2 * m + static_cast<T>(1)) * (a + 2 * m));
+		else
+			sharedMem[threadIdx.x] = -(b + m) * (a + b + m) * (1.f-x) / ((b + 2 * m + static_cast<T>(1)) * (b + 2 * m));
 	}
 
 	__syncthreads();
 
-	if(threadIdx.x == 0)
-		z = betaIncCoreCuda(a, b, x);
+	if(threadIdx.x == 0) {
+
+		const T gammaPart = lgamma(a) + lgamma(b) - lgamma(a + b);
+	    const T front = math::nd4j_exp<T,T>(math::nd4j_log<T, T>(x) * a + math::nd4j_log<T, T>(1.f - x) * b - gammaPart);
+
+		if (symmCond)
+			z =  front * continuedFractionCuda(a, b, x) / a;
+		else  // symmetry relation
+			z =  static_cast<T>(1) - front * continuedFractionCuda(b, a, static_cast<T>(1) - x) / b;
+	}
 }
 
 ///////////////////////////////////////////////////////////////////
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu b/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu
new file mode 100644
index 000000000..3edd59ecc
--- /dev/null
+++ b/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu
@@ -0,0 +1,78 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2019 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//
+// @author Yurii Shyrma (iuriish@yahoo.com)
+//
+
+#include<ops/declarable/helpers/gammaMathFunc.h>
+#include <NDArrayFactory.h>
+
+namespace nd4j {
+namespace ops {
+namespace helpers {
+
+///////////////////////////////////////////////////////////////////
+template<typename T>
+__global__ static void diGammaCuda(const void *vx, const Nd4jLong *xShapeInfo,
+                                     	 void *vz, const Nd4jLong *zShapeInfo) {
+
+    const auto x = reinterpret_cast<const T*>(vx);
+          auto z = reinterpret_cast<T*>(vz);
+
+    __shared__ Nd4jLong len;
+    __shared__ bool sameOffset;
+
+    if (threadIdx.x == 0) {
+        len = shape::length(xShapeInfo);
+        sameOffset = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
+    }
+    __syncthreads();
+
+    for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < len; i += gridDim.x * blockDim.x) {
+
+        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+        const auto zOffset = sameOffset ? xOffset : shape::getIndexOffset(i, zShapeInfo);
+
+        z[zOffset] = diGammaScalar<T>(x[xOffset]);
+    }
+}
+
+///////////////////////////////////////////////////////////////////
+template<typename T>
+static void diGammaCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo) {
+
+    diGammaCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo);
+}
+
+///////////////////////////////////////////////////////////////////
+void diGamma(nd4j::LaunchContext* context, const NDArray& x, NDArray& z) {
+
+    int threadsPerBlock = MAX_NUM_THREADS / 2;
+    int blocksPerGrid = (z.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
+
+    NDArray::prepareSpecialUse({&z}, {&x});
+    BUILD_SINGLE_SELECTOR(x.dataType(), diGammaCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), x.getSpecialBuffer(), x.getSpecialShapeInfo(), z.getSpecialBuffer(), z.getSpecialShapeInfo()), FLOAT_TYPES);
+    NDArray::registerSpecialUse({&z}, {&x});
+}
+
+BUILD_SINGLE_TEMPLATE(template void diGammaCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo), FLOAT_TYPES);
+
+}
+}
+}
+
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu b/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu
index 01b9464fa..6a62246a3 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu
@@ -18,7 +18,7 @@
 // @author Yurii Shyrma (iuriish@yahoo.com), created on 26.04.2019
 //
 
-#include<ops/declarable/helpers/polyGamma.h>
+#include<ops/declarable/helpers/gammaMathFunc.h>
 #include<ops/declarable/helpers/zeta.h>
 #include <NDArrayFactory.h>
 
@@ -37,9 +37,13 @@ __global__ static void polyGammaCuda(const void *vn, const Nd4jLong *nShapeInfo,
           auto z = reinterpret_cast<T*>(vz);
 
     __shared__ Nd4jLong len;
+    __shared__ bool sameOffsetNX, sameOffsetNZ;
 
-    if (threadIdx.x == 0)
+    if (threadIdx.x == 0) {
         len = shape::length(nShapeInfo);
+        sameOffsetNX = shape::haveSameShapeAndStrides(xShapeInfo, nShapeInfo);
+        sameOffsetNZ = shape::haveSameShapeAndStrides(zShapeInfo, nShapeInfo);
+    }
     __syncthreads();
 
     const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
@@ -48,19 +52,26 @@ __global__ static void polyGammaCuda(const void *vn, const Nd4jLong *nShapeInfo,
     for (int i = tid; i < len; i += totalThreads) {
 
         const auto nOffset = shape::getIndexOffset(i, nShapeInfo);
-        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
-        const auto zOffset = shape::getIndexOffset(i, zShapeInfo);
+        const auto xOffset = sameOffsetNX ? nOffset : shape::getIndexOffset(i, xShapeInfo);
+        const auto zOffset = sameOffsetNZ ? nOffset : shape::getIndexOffset(i, zShapeInfo);
 
-        const T nVal = n[nOffset];
+        const T order = n[nOffset];
 
-        int sign = (static_cast<int>(nVal) + 1) % 2  ?  -1 : 1;
+        int sign = (static_cast<int>(order) + 1) % 2  ?  -1 : 1;
 
-        T factorial = 1;
-        if(nVal != 0 && nVal != 1)
-        	for(int i = 2; i <= nVal; ++i)
-				factorial *= i;
+        if(order != static_cast<int>(order)) {
+            z[zOffset] = DataTypeUtils::nanOrZero<T>();
+        }
+        else if(order == 0) {
+            z[zOffset] = diGammaScalar<T>(x[xOffset]);
+        }
+        else {
+            T factorial = 1;
+            for(int i = 2; i <= order; ++i)
+                factorial *= i;
 
-        z[zOffset] = sign * factorial * zetaScalar<T>(nVal + 1, x[xOffset]);
+            z[zOffset] = sign * factorial * zetaScalar<T>(order + 1, x[xOffset]);
+        }
     }
 }
 
@@ -76,7 +87,7 @@ void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x
 
     NDArray::prepareSpecialUse({&z}, {&n, &x});
 
-    int threadsPerBlock = MAX_NUM_THREADS;
+    int threadsPerBlock = MAX_NUM_THREADS / 2;
     int blocksPerGrid = (z.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
 
     BUILD_SINGLE_SELECTOR(n.dataType(), polyGammaCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), n.getSpecialBuffer(), n.getSpecialShapeInfo(), x.getSpecialBuffer(), x.getSpecialShapeInfo(), z.getSpecialBuffer(), z.getSpecialShapeInfo()), FLOAT_TYPES);
diff --git a/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h b/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h
new file mode 100644
index 000000000..2ad540409
--- /dev/null
+++ b/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h
@@ -0,0 +1,100 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2019 Konduit K.K.
+ *
+ * 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
+ ******************************************************************************/
+
+//
+// @author Yurii Shyrma (iuriish@yahoo.com)
+//
+
+#ifndef LIBND4J_GAMMAMATHFUNC_H
+#define LIBND4J_GAMMAMATHFUNC_H
+
+#include <ops/declarable/helpers/helpers.h>
+#include "NDArray.h"
+
+namespace nd4j {
+namespace ops {
+namespace helpers {
+
+    // calculate the digamma function for each element for array
+    void diGamma(nd4j::LaunchContext* context, const NDArray& x, NDArray& z);
+
+	// calculate the polygamma function
+    void polyGamma(nd4j::LaunchContext* context, const NDArray& n, const NDArray& x, NDArray& z);
+
+    // calculate the digamma function for one element
+	// implementation is based on serial representation written in terms of the Hurwitz zeta function as polygamma = (-1)^{n+1} * n! * zeta(n+1, x)
+	template <typename T>
+	_CUDA_HD T diGammaScalar(T x) {
+
+		const int xInt = static_cast<int>(x);
+
+		// negative and zero
+		if(x <= 0) {
+			if(x == xInt)	// integer
+				return DataTypeUtils::infOrMax<T>();
+			else
+				return diGammaScalar<T>(1 - x) - M_PI / nd4j::math::nd4j_tan<T,T>(M_PI * x); // use reflection formula psi(1-x) = psi(x) + pi*cot(pi*x)
+		}
+
+		// positive integer
+		if(x == xInt && xInt <= 20) {		// psi(n) = -Euler_Mascheroni_const + sum_from_k=1_to_n-1( 1/k ), for n = 1,2,3,...inf, we use this formula only for n <= 20 to avoid time consuming sum calculation for bigger n
+			T result = -0.577215664901532;
+			for (uint i = 1; i <= xInt - 1; ++i) {
+				result += static_cast<T>(1) / i;
+			}
+			return result;
+		}
+
+		// positive half-integer
+		if(x - xInt == 0.5 && xInt <= 20) {		// psi(n+0.5) = -Euler_Mascheroni_const - 2*ln(2) + sum_from_k=1_to_n( 2/(2*k-1) )	, for n = 1,2,3,...inf, we use this formula only for n <= 20 to avoid time consuming sum calculation for bigger n
+			T result = -0.577215664901532 - 2 * nd4j::math::nd4j_log<T,T>(2);
+			for (uint i = 1; i <= xInt; ++i) {
+				result += static_cast<T>(2) / (2*i - 1);
+			}
+			return result;
+		}
+
+		// positive, smaller then 5; we should use number > 5 in order to have satisfactory accuracy in asymptotic expansion
+		if(x < 5)
+			return diGammaScalar<T>(1 + x) - static_cast<T>(1) / x;		 // recurrence formula  psi(x) = psi(x+1) - 1/x.
+
+		// *** other positive **** //
+
+		// truncated expansion formula (from wiki)
+		// psi(x) = log(x) - 1/(2*x) - 1/(12*x^2) + 1/(120*x^4) - 1/(252*x^6) + 1/(240*x^8) - 5/(660*x^10) + 691/(32760*x^12) - 1/(12*x^14) + ...
+
+		if(x >= (sizeof(T) > 4 ? 1.e16 : 1.e8))		// if x is too big take into account only log(x)
+			return nd4j::math::nd4j_log<T,T>(x);
+
+		// coefficients used in truncated asymptotic expansion formula
+		const T coeffs[7] = {-(T)1/12, (T)1/120, -(T)1/252, (T)1/240, -(T)5/660, (T)691/32760, -(T)1/12};
+		// const T coeffs[7] = {-0.0833333333333333, 0.00833333333333333, -0.00396825396825397, 0.00416666666666667, -0.00757575757575758, 0.0210927960927961, -0.0833333333333333};
+
+		const T x2Inv = static_cast<T>(1) / (x * x);
+		T result = 0;
+
+		for (int i = 6; i >= 0; --i)
+			result = (result + coeffs[i]) * x2Inv;
+		return result + nd4j::math::nd4j_log<T,T>(x) - static_cast<T>(0.5) / x;
+	}
+
+}
+}
+}
+
+
+#endif //LIBND4J_GAMMAMATHFUNC_H
diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp
index 027c62484..4531fda81 100644
--- a/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp
+++ b/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp
@@ -36,7 +36,7 @@ namespace platforms {
 //////////////////////////////////////////////////////////////////////
 static void conv2d_mkldnn(nd4j::graph::Context &block, const NDArray *input, const NDArray *weights,
                           const NDArray *bias, NDArray *output, const int kH, const int kW, const int sH,
-                          const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode,
+                          const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode,
                           const int isNCHW) {
 
     int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
@@ -44,8 +44,7 @@ static void conv2d_mkldnn(nd4j::graph::Context &block, const NDArray *input, con
     ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW,
                                                indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
 
-    if(isSameMode)                       // SAME
-        ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
+    ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
 
     dnnl_memory_desc_t empty;
     dnnl::memory::desc conv_src_md(empty), conv_weights_md(empty), conv_bias_md(empty), conv_dst_md(
@@ -53,7 +52,7 @@ static void conv2d_mkldnn(nd4j::graph::Context &block, const NDArray *input, con
     dnnl::memory::desc user_src_md(empty), user_weights_md(empty), user_bias_md(empty), user_dst_md(
             empty);
     dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation;
-    mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW,
+    mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
                                            bS, iC, iH, iW, oC, oH, oW, input, nullptr, weights, nullptr,
                                            bias, output,
                                            &conv_src_md, nullptr, &conv_weights_md, nullptr,
@@ -115,13 +114,11 @@ static void conv2d_mkldnn(nd4j::graph::Context &block, const NDArray *input, con
 
 //////////////////////////////////////////////////////////////////////
 PLATFORM_IMPL(conv2d) {
-    auto input = INPUT_VARIABLE(
-            0);                                    // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
+    auto input = INPUT_VARIABLE(0);                                    // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
     auto weights = INPUT_VARIABLE(1);                                    // [kH, kW, iC, oC] always
     auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;      // [oC]
 
-    auto output = OUTPUT_VARIABLE(
-            0);                                   // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
+    auto output = OUTPUT_VARIABLE(0);                                   // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
 
     int sH = INT_ARG(2);                                                        // strides height
     int sW = INT_ARG(3);                                                        // strides width
@@ -129,13 +126,13 @@ PLATFORM_IMPL(conv2d) {
     int pW = INT_ARG(5);                                                        // paddings width
     int dH = INT_ARG(6);                                                        // dilations height
     int dW = INT_ARG(7);                                                        // dilations width
-    int isSameMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
+    int paddingMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
     bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;       // INT_ARG(9): 0-NCHW,  1-NHWC
 
     int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
     int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
 
-    conv2d_mkldnn(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW);
+    conv2d_mkldnn(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
 
     return Status::OK();
 }
@@ -155,18 +152,13 @@ PLATFORM_CHECK(conv2d) {
 
 //////////////////////////////////////////////////////////////////////
 PLATFORM_IMPL(conv2d_bp) {
-    auto input = INPUT_VARIABLE(
-            0);                                                // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
-    auto weights = INPUT_VARIABLE(
-            1);                                                // [kH, kW, iC, oC] always
+    auto input = INPUT_VARIABLE(0);                                                // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
+    auto weights = INPUT_VARIABLE(1);                                                // [kH, kW, iC, oC] always
     auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
-    auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(
-            2);        // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
+    auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2);        // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
 
-    auto gradI = OUTPUT_VARIABLE(
-            0);                                                 // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
-    auto gradW = OUTPUT_VARIABLE(
-            1);                                                 // [kH, kW, iC, oC] always
+    auto gradI = OUTPUT_VARIABLE(0);                                                 // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
+    auto gradW = OUTPUT_VARIABLE(1);                                                 // [kH, kW, iC, oC] always
     auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]
 
     int kH = INT_ARG(0);                                                        // filter(kernel) height
@@ -177,7 +169,7 @@ PLATFORM_IMPL(conv2d_bp) {
     int pW = INT_ARG(5);                                                        // paddings width
     int dH = INT_ARG(6);                                                        // dilations height
     int dW = INT_ARG(7);                                                        // dilations width
-    int isSameMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
+    int paddingMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
     int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;          // INT_ARG(9): 0-NCHW, 1-NHWC
 
     REQUIRE_TRUE(input->rankOf() == 4, 0,
@@ -195,8 +187,7 @@ PLATFORM_IMPL(conv2d_bp) {
     ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                                indIiH, indWiC, indWoC, indWkH, indOoH);
 
-    if (isSameMode)                       // SAME
-        ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
+    ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
 
     dnnl_memory_desc_t empty;
     dnnl::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty),
@@ -204,7 +195,7 @@ PLATFORM_IMPL(conv2d_bp) {
     dnnl::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty),
             user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
     dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation;
-    mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW,
+    mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
                                            bS, iC, iH, iW, oC, oH, oW, input, gradI, weights, gradW,
                                            gradB, gradO,
                                            &conv_src_md, &conv_diff_src_md, &conv_weights_md,
@@ -342,18 +333,13 @@ PLATFORM_CHECK(conv2d_bp) {
     if (::optimalLevel() < 2)
         return false;
 
-    auto input = INPUT_VARIABLE(
-            0);                                                // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
-    auto weights = INPUT_VARIABLE(
-            1);                                                // [kH, kW, iC, oC] always
+    auto input = INPUT_VARIABLE(0);                                                // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
+    auto weights = INPUT_VARIABLE(1);                                                // [kH, kW, iC, oC] always
     auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;                  // [oC]
-    auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(
-            2);        // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
+    auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2);        // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
 
-    auto gradI = OUTPUT_VARIABLE(
-            0);                                                 // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
-    auto gradW = OUTPUT_VARIABLE(
-            1);                                                 // [kH, kW, iC, oC] always
+    auto gradI = OUTPUT_VARIABLE(0);                                                 // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
+    auto gradW = OUTPUT_VARIABLE(1);                                                 // [kH, kW, iC, oC] always
     auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr;                   // [oC]
 
 
diff --git a/libnd4j/include/ops/ops.h b/libnd4j/include/ops/ops.h
index f3f9f9699..1b54889d4 100644
--- a/libnd4j/include/ops/ops.h
+++ b/libnd4j/include/ops/ops.h
@@ -1568,11 +1568,39 @@ namespace simdOps {
             return opOutput + old;
         }
 
-
         op_def static Z update(X old, X opOutput, X *extraParams) {
             return opOutput + old;
         }
 
+        op_def static Z postProcess(X reduction, Nd4jLong n, X *extraParams) {
+            return reduction;
+        }
+	};
+
+	template <typename X, typename Z>
+	class IsNegative {
+	public:
+		no_op_exec_special_bool
+		no_op_exec_special_bool_cuda
+
+		no_op_exec_special_accumulation
+		no_op_exec_special_accumulation_cuda
+
+		op_def static Z op(X d1, X *params) {
+			return d1 < (X)0.f;
+		}
+
+        op_def static X startingValue(const X *input) {
+            return static_cast<X>(0);
+        }
+
+        op_def static Z merge(X old, X opOutput, X *extraParams) {
+            return opOutput + old;
+        }
+
+        op_def static Z update(X old, X opOutput, X *extraParams) {
+            return opOutput + old;
+        }
 
         op_def static Z postProcess(X reduction, Nd4jLong n, X *extraParams) {
             return reduction;
diff --git a/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp b/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
index b3552a00f..99092b37d 100644
--- a/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
+++ b/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
@@ -1008,6 +1008,38 @@ TEST_F(ConvolutionTests1, conv1d_causal_6) {
     delete results;
 }
 
+//////////////////////////////////////////////////////////////////////
+TEST_F(ConvolutionTests1, conv1d_causal_7) {
+
+    int bS=2, iW=8,  iC=3,oC=4,  kW=2,  sW=1,  pW=0,  dW=1;
+    int oW = (iW-1)/sW + 1;
+    int paddingMode = 2;             // CAUSAL
+    int dataFormat  = 1;             // 1-NHWC, 0-NCHW
+
+    NDArray input('c', {bS, iW, iC}, nd4j::DataType::FLOAT32);
+    NDArray weights('c', {kW, iC, oC}, nd4j::DataType::FLOAT32);
+
+    NDArray expOutput('c', {bS, oW, oC}, {11.000000, 11.600000, 12.200000, 12.800000, 30.099998, 32.200001, 34.299999, 36.400002, 49.899998, 53.800003, 57.699997,
+        61.599998, 69.699997, 75.400002, 81.099998, 86.800003, 89.500000, 97.000000, 104.500000, 112.000000, 109.300003, 118.600006, 127.899994, 137.199997, 129.100006,
+        140.199997, 151.300003, 162.399994, 148.899994, 161.800003, 174.699997, 187.600006, 133.399994, 141.200012, 149.000000, 156.800003, 188.500000, 205.000000,
+        221.500000, 238.000000, 208.299988, 226.600006, 244.899994, 263.200012, 228.100006, 248.200012, 268.299988, 288.399994, 247.899994, 269.799988, 291.700012,
+        313.600006, 267.700012, 291.399994, 315.100006, 338.799988, 287.500000, 313.000000, 338.500000, 364.000000, 307.299988, 334.600006, 361.899994, 389.200012}, nd4j::DataType::FLOAT32);
+
+    input.linspace(1., 1.);
+    weights.linspace(0.1, 0.1);
+
+    nd4j::ops::conv1d op;
+    auto results = op.execute({&input, &weights}, {}, {kW, sW, pW, dW,  paddingMode, dataFormat});
+    auto output = results->at(0);
+
+    ASSERT_EQ(Status::OK(), results->status());
+
+    ASSERT_TRUE(expOutput.isSameShape(output));
+    ASSERT_TRUE(expOutput.equalsTo(output));
+
+    delete results;
+}
+
 //////////////////////////////////////////////////////////////////////
 TEST_F(ConvolutionTests1, conv1d_causal_bp_1) {
 
@@ -1174,14 +1206,14 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_bp_test3) {
     auto bias = NDArrayFactory::create<TypeParam>('c', {oC}, {1,2,3});
     auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oC, oH, oW});
 
-    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW},{ 0.567f, 1.224f, 0.66f, 1.314f, 2.82f, 1.512f, 1.386f, 2.976f, 1.596f, 0.801f, 1.71f, 0.912f, 0.657f, 1.422f, 0.768f, 1.53f, 3.288f, 1.764f, 1.602f, 3.444f, 1.848f, 0.927f, 1.98f, 1.056f, 
-                                                     0.747f, 1.62f, 0.876f, 1.746f, 3.756f, 2.016f, 1.818f, 3.912f, 2.1f, 1.053f, 2.25f, 1.2f, 0.837f, 1.818f, 0.984f, 1.962f, 4.224f, 2.268f, 2.034f, 4.38f, 2.352f, 1.179f, 2.52f, 1.344f, 
-                                                     1.467f, 3.06f, 1.596f, 3.186f, 6.636f, 3.456f, 3.402f, 7.08f, 3.684f, 1.845f, 3.834f, 1.992f, 1.773f, 3.69f, 1.92f, 3.834f, 7.968f, 4.14f, 4.05f, 8.412f, 4.368f, 2.187f, 4.536f, 2.352f, 
+    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW},{ 0.567f, 1.224f, 0.66f, 1.314f, 2.82f, 1.512f, 1.386f, 2.976f, 1.596f, 0.801f, 1.71f, 0.912f, 0.657f, 1.422f, 0.768f, 1.53f, 3.288f, 1.764f, 1.602f, 3.444f, 1.848f, 0.927f, 1.98f, 1.056f,
+                                                     0.747f, 1.62f, 0.876f, 1.746f, 3.756f, 2.016f, 1.818f, 3.912f, 2.1f, 1.053f, 2.25f, 1.2f, 0.837f, 1.818f, 0.984f, 1.962f, 4.224f, 2.268f, 2.034f, 4.38f, 2.352f, 1.179f, 2.52f, 1.344f,
+                                                     1.467f, 3.06f, 1.596f, 3.186f, 6.636f, 3.456f, 3.402f, 7.08f, 3.684f, 1.845f, 3.834f, 1.992f, 1.773f, 3.69f, 1.92f, 3.834f, 7.968f, 4.14f, 4.05f, 8.412f, 4.368f, 2.187f, 4.536f, 2.352f,
                                                      2.079f, 4.32f, 2.244f, 4.482f, 9.3f, 4.824f, 4.698f, 9.744f, 5.052f, 2.529f, 5.238f, 2.712f, 2.385f, 4.95f, 2.568f, 5.13f, 10.632f, 5.508f, 5.346f, 11.076f, 5.736f, 2.871f, 5.94f, 3.072f});
 
-    auto expGradW = NDArrayFactory::create<TypeParam>('c', {oC, iC, kH, kW},{1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 
-                                                    1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 
-                                                    2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 
+    auto expGradW = NDArrayFactory::create<TypeParam>('c', {oC, iC, kH, kW},{1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f,
+                                                    1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 1.3600e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f,
+                                                    2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.0000e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f,
                                                     2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f, 2.6400e+00f});
     auto expGradB = NDArrayFactory::create<TypeParam>('c', {oC},{0.68f, 1.f, 1.32f});
 
@@ -1252,20 +1284,20 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test1) {
     auto bias     = NDArrayFactory::create<TypeParam>('c', {oC}, {1,2,3});
     auto gradO    = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, oC});
 
-    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC},{0.226f, 0.343f, 0.46f, 0.577f, 1.172f, 1.46f, 1.748f, 2.036f, 1.892f, 2.288f, 2.684f, 3.08f, 1.284f, 1.581f, 1.878f, 2.175f, 4.458f, 5.133f, 5.808f, 6.483f, 6.186f, 7.023f, 7.86f, 8.697f, 3.39f, 3.93f, 4.47f, 5.01f, 9.642f, 10.803f, 11.964f, 13.125f, 11.37f, 12.693f, 14.016f, 15.339f, 
-                                                        5.266f, 5.707f, 6.148f, 6.589f, 12.98f, 13.916f, 14.852f, 15.788f, 14.564f, 15.608f, 16.652f, 17.696f, 6.284f, 7.166f, 8.048f, 8.93f, 17.896f, 19.768f, 21.64f, 23.512f, 21.928f, 24.016f, 26.104f, 28.192f, 18.12f, 19.686f, 21.252f, 22.818f, 45.852f, 49.146f, 52.44f, 55.734f, 53.196f, 56.814f, 60.432f, 64.05f, 
-                                                       28.164f, 30.216f, 32.268f, 34.32f, 67.884f, 72.15f, 76.416f, 80.682f, 75.228f, 79.818f, 84.408f, 88.998f, 29.324f, 30.854f, 32.384f, 33.914f, 67.432f, 70.6f, 73.768f, 76.936f, 73.192f, 76.576f, 79.96f, 83.344f, 27.884f, 30.062f, 32.24f, 34.418f, 66.28f, 70.744f, 75.208f, 79.672f, 70.312f, 74.992f, 79.672f, 84.352f, 
-                                                       58.296f, 61.806f, 65.316f, 68.826f, 133.98f, 141.162f, 148.344f, 155.526f, 141.324f, 148.83f, 156.336f, 163.842f, 68.34f, 72.336f, 76.332f, 80.328f, 156.012f, 164.166f, 172.32f, 180.474f, 163.356f, 171.834f, 180.312f, 188.79f, 61.292f, 64.118f, 66.944f, 69.77f, 136.552f, 142.312f, 148.072f, 153.832f, 142.312f, 148.288f, 154.264f, 160.24f, 
-                                                        9.298f, 11.359f, 13.42f, 15.481f, 27.092f, 31.268f, 35.444f, 39.62f, 27.812f, 32.096f, 36.38f, 40.664f, 26.556f, 29.769f, 32.982f, 36.195f, 66.666f, 73.173f, 79.68f, 86.187f, 68.394f, 75.063f, 81.732f, 88.401f, 28.662f, 32.118f, 35.574f, 39.03f, 71.85f, 78.843f, 85.836f, 92.829f, 73.578f, 80.733f, 87.888f, 95.043f, 
-                                                       29.89f, 32.275f, 34.66f, 37.045f, 70.004f, 74.828f, 79.652f, 84.476f, 71.588f, 76.52f, 81.452f, 86.384f, 71.084f, 75.854f, 80.624f, 85.394f, 163.048f, 172.696f, 182.344f, 191.992f, 167.08f, 176.944f, 186.808f, 196.672f, 138.648f, 146.046f, 153.444f, 160.842f, 310.236f, 325.194f, 340.152f, 355.11f, 317.58f, 332.862f, 348.144f, 363.426f, 
-                                                      148.692f, 156.576f, 164.46f, 172.344f, 332.268f, 348.198f, 364.128f, 380.058f, 339.612f, 355.866f, 372.12f, 388.374f, 125.228f, 130.646f, 136.064f, 141.482f, 274.792f, 285.736f, 296.68f, 307.624f, 280.552f, 291.712f, 302.872f, 314.032f, 92.684f, 98.75f, 104.816f, 110.882f, 211.432f, 223.672f, 235.912f, 248.152f, 215.464f, 227.92f, 240.376f, 252.832f, 
+    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC},{0.226f, 0.343f, 0.46f, 0.577f, 1.172f, 1.46f, 1.748f, 2.036f, 1.892f, 2.288f, 2.684f, 3.08f, 1.284f, 1.581f, 1.878f, 2.175f, 4.458f, 5.133f, 5.808f, 6.483f, 6.186f, 7.023f, 7.86f, 8.697f, 3.39f, 3.93f, 4.47f, 5.01f, 9.642f, 10.803f, 11.964f, 13.125f, 11.37f, 12.693f, 14.016f, 15.339f,
+                                                        5.266f, 5.707f, 6.148f, 6.589f, 12.98f, 13.916f, 14.852f, 15.788f, 14.564f, 15.608f, 16.652f, 17.696f, 6.284f, 7.166f, 8.048f, 8.93f, 17.896f, 19.768f, 21.64f, 23.512f, 21.928f, 24.016f, 26.104f, 28.192f, 18.12f, 19.686f, 21.252f, 22.818f, 45.852f, 49.146f, 52.44f, 55.734f, 53.196f, 56.814f, 60.432f, 64.05f,
+                                                       28.164f, 30.216f, 32.268f, 34.32f, 67.884f, 72.15f, 76.416f, 80.682f, 75.228f, 79.818f, 84.408f, 88.998f, 29.324f, 30.854f, 32.384f, 33.914f, 67.432f, 70.6f, 73.768f, 76.936f, 73.192f, 76.576f, 79.96f, 83.344f, 27.884f, 30.062f, 32.24f, 34.418f, 66.28f, 70.744f, 75.208f, 79.672f, 70.312f, 74.992f, 79.672f, 84.352f,
+                                                       58.296f, 61.806f, 65.316f, 68.826f, 133.98f, 141.162f, 148.344f, 155.526f, 141.324f, 148.83f, 156.336f, 163.842f, 68.34f, 72.336f, 76.332f, 80.328f, 156.012f, 164.166f, 172.32f, 180.474f, 163.356f, 171.834f, 180.312f, 188.79f, 61.292f, 64.118f, 66.944f, 69.77f, 136.552f, 142.312f, 148.072f, 153.832f, 142.312f, 148.288f, 154.264f, 160.24f,
+                                                        9.298f, 11.359f, 13.42f, 15.481f, 27.092f, 31.268f, 35.444f, 39.62f, 27.812f, 32.096f, 36.38f, 40.664f, 26.556f, 29.769f, 32.982f, 36.195f, 66.666f, 73.173f, 79.68f, 86.187f, 68.394f, 75.063f, 81.732f, 88.401f, 28.662f, 32.118f, 35.574f, 39.03f, 71.85f, 78.843f, 85.836f, 92.829f, 73.578f, 80.733f, 87.888f, 95.043f,
+                                                       29.89f, 32.275f, 34.66f, 37.045f, 70.004f, 74.828f, 79.652f, 84.476f, 71.588f, 76.52f, 81.452f, 86.384f, 71.084f, 75.854f, 80.624f, 85.394f, 163.048f, 172.696f, 182.344f, 191.992f, 167.08f, 176.944f, 186.808f, 196.672f, 138.648f, 146.046f, 153.444f, 160.842f, 310.236f, 325.194f, 340.152f, 355.11f, 317.58f, 332.862f, 348.144f, 363.426f,
+                                                      148.692f, 156.576f, 164.46f, 172.344f, 332.268f, 348.198f, 364.128f, 380.058f, 339.612f, 355.866f, 372.12f, 388.374f, 125.228f, 130.646f, 136.064f, 141.482f, 274.792f, 285.736f, 296.68f, 307.624f, 280.552f, 291.712f, 302.872f, 314.032f, 92.684f, 98.75f, 104.816f, 110.882f, 211.432f, 223.672f, 235.912f, 248.152f, 215.464f, 227.92f, 240.376f, 252.832f,
                                                       178.824f, 188.166f, 197.508f, 206.85f, 398.364f, 417.21f, 436.056f, 454.902f, 405.708f, 424.878f, 444.048f, 463.218f, 188.868f, 198.696f, 208.524f, 218.352f, 420.396f, 440.214f, 460.032f, 479.85f, 427.74f, 447.882f, 468.024f, 488.166f, 157.196f, 163.91f, 170.624f, 177.338f, 343.912f, 357.448f, 370.984f, 384.52f, 349.672f, 363.424f, 377.176f, 390.928f});
 
-    auto expGradW = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC},{120.96f, 122.04f, 123.12f, 120.96f, 122.04f, 123.12f, 120.96f, 122.04f, 123.12f, 120.96f, 122.04f, 123.12f, 79.56f, 80.28f, 81.f, 79.56f, 80.28f, 81.f, 79.56f, 80.28f, 81.f, 79.56f, 80.28f, 81.f, 
-                                                        154.8f, 156.24f, 157.68f, 154.8f, 156.24f, 157.68f, 154.8f, 156.24f, 157.68f, 154.8f, 156.24f, 157.68f, 101.76f, 102.72f, 103.68f, 101.76f, 102.72f, 103.68f, 101.76f, 102.72f, 103.68f, 101.76f, 102.72f, 103.68f, 
-                                                        111.24f, 112.32f, 113.4f, 111.24f, 112.32f, 113.4f, 111.24f, 112.32f, 113.4f, 111.24f, 112.32f, 113.4f, 73.08f, 73.8f, 74.52f, 73.08f, 73.8f, 74.52f, 73.08f, 73.8f, 74.52f, 73.08f, 73.8f, 74.52f, 
-                                                         67.68f, 68.4f, 69.12f, 67.68f, 68.4f, 69.12f, 67.68f, 68.4f, 69.12f, 67.68f, 68.4f, 69.12f, 44.4f, 44.88f, 45.36f, 44.4f, 44.88f, 45.36f, 44.4f, 44.88f, 45.36f, 44.4f, 44.88f, 45.36f, 
-                                                         85.92f, 86.88f, 87.84f, 85.92f, 86.88f, 87.84f, 85.92f, 86.88f, 87.84f, 85.92f, 86.88f, 87.84f, 56.32f, 56.96f, 57.6f, 56.32f, 56.96f, 57.6f, 56.32f, 56.96f, 57.6f, 56.32f, 56.96f, 57.6f, 
+    auto expGradW = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC},{120.96f, 122.04f, 123.12f, 120.96f, 122.04f, 123.12f, 120.96f, 122.04f, 123.12f, 120.96f, 122.04f, 123.12f, 79.56f, 80.28f, 81.f, 79.56f, 80.28f, 81.f, 79.56f, 80.28f, 81.f, 79.56f, 80.28f, 81.f,
+                                                        154.8f, 156.24f, 157.68f, 154.8f, 156.24f, 157.68f, 154.8f, 156.24f, 157.68f, 154.8f, 156.24f, 157.68f, 101.76f, 102.72f, 103.68f, 101.76f, 102.72f, 103.68f, 101.76f, 102.72f, 103.68f, 101.76f, 102.72f, 103.68f,
+                                                        111.24f, 112.32f, 113.4f, 111.24f, 112.32f, 113.4f, 111.24f, 112.32f, 113.4f, 111.24f, 112.32f, 113.4f, 73.08f, 73.8f, 74.52f, 73.08f, 73.8f, 74.52f, 73.08f, 73.8f, 74.52f, 73.08f, 73.8f, 74.52f,
+                                                         67.68f, 68.4f, 69.12f, 67.68f, 68.4f, 69.12f, 67.68f, 68.4f, 69.12f, 67.68f, 68.4f, 69.12f, 44.4f, 44.88f, 45.36f, 44.4f, 44.88f, 45.36f, 44.4f, 44.88f, 45.36f, 44.4f, 44.88f, 45.36f,
+                                                         85.92f, 86.88f, 87.84f, 85.92f, 86.88f, 87.84f, 85.92f, 86.88f, 87.84f, 85.92f, 86.88f, 87.84f, 56.32f, 56.96f, 57.6f, 56.32f, 56.96f, 57.6f, 56.32f, 56.96f, 57.6f, 56.32f, 56.96f, 57.6f,
                                                          61.2f, 61.92f, 62.64f, 61.2f, 61.92f, 62.64f, 61.2f, 61.92f, 62.64f, 61.2f, 61.92f, 62.64f, 40.08f, 40.56f, 41.04f, 40.08f, 40.56f, 41.04f, 40.08f, 40.56f, 41.04f, 40.08f, 40.56f, 41.04f});
     // auto expGradB('c', {oC},{});
 
@@ -1302,18 +1334,18 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test2) {
     auto bias     = NDArrayFactory::create<TypeParam>('c', {oC}, {1,2,3});
     auto gradO    = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, oC});
 
-    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC},{ 0.014f, 0.032f, 0.05f, 0.068f, 0.118f, 0.181f, 0.244f, 0.307f, 0.212f, 0.257f, 0.302f, 0.347f, 0.208f, 0.298f, 0.388f, 0.478f, 1.028f, 1.262f, 1.496f, 1.73f, 1.036f, 1.18f, 1.324f, 1.468f, 0.928f, 1.018f, 1.108f, 1.198f, 2.9f, 3.134f, 3.368f, 3.602f, 2.188f, 2.332f, 2.476f, 2.62f, 
-                                                         1.202f, 1.274f, 1.346f, 1.418f, 3.142f, 3.313f, 3.484f, 3.655f, 2.048f, 2.147f, 2.246f, 2.345f, 0.532f, 0.676f, 0.82f, 0.964f, 2.324f, 2.666f, 3.008f, 3.35f, 2.008f, 2.206f, 2.404f, 2.602f, 3.584f, 3.98f, 4.376f, 4.772f, 10.552f, 11.452f, 12.352f, 13.252f, 7.4f, 7.904f, 8.408f, 8.912f, 
-                                                         6.752f, 7.148f, 7.544f, 7.94f, 17.752f, 18.652f, 19.552f, 20.452f, 11.432f, 11.936f, 12.44f, 12.944f, 5.932f, 6.184f, 6.436f, 6.688f, 14.42f, 14.978f, 15.536f, 16.094f, 8.704f, 9.01f, 9.316f, 9.622f, 3.11f, 3.236f, 3.362f, 3.488f, 7.39f, 7.669f, 7.948f, 8.227f, 4.388f, 4.541f, 4.694f, 4.847f, 
-                                                         8.56f, 8.866f, 9.172f, 9.478f, 19.892f, 20.558f, 21.224f, 21.89f, 11.548f, 11.908f, 12.268f, 12.628f, 11.008f, 11.314f, 11.62f, 11.926f, 25.22f, 25.886f, 26.552f, 27.218f, 14.428f, 14.788f, 15.148f, 15.508f, 7.322f, 7.502f, 7.682f, 7.862f, 16.462f, 16.849f, 17.236f, 17.623f, 9.248f, 9.455f, 9.662f, 9.869f, 
-                                                         0.158f, 0.392f, 0.626f, 0.86f, 1.27f, 1.765f, 2.26f, 2.755f, 1.22f, 1.481f, 1.742f, 2.003f, 2.224f, 2.746f, 3.268f, 3.79f, 6.788f, 7.886f, 8.984f, 10.082f, 4.78f, 5.356f, 5.932f, 6.508f, 6.4f, 6.922f, 7.444f, 7.966f, 15.572f, 16.67f, 17.768f, 18.866f, 9.388f, 9.964f, 10.54f, 11.116f, 
-                                                         4.802f, 5.09f, 5.378f, 5.666f, 11.206f, 11.809f, 12.412f, 13.015f, 6.512f, 6.827f, 7.142f, 7.457f, 6.004f, 6.58f, 7.156f, 7.732f, 14.996f, 16.202f, 17.408f, 18.614f, 9.208f, 9.838f, 10.468f, 11.098f, 17.984f, 19.244f, 20.504f, 21.764f, 42.808f, 45.436f, 48.064f, 50.692f, 25.256f, 26.624f, 27.992f, 29.36f, 
-                                                        28.064f, 29.324f, 30.584f, 31.844f, 63.832f, 66.46f, 69.088f, 71.716f, 36.2f, 37.568f, 38.936f, 40.304f, 18.316f, 19.f, 19.684f, 20.368f, 40.916f, 42.338f, 43.76f, 45.182f, 22.816f, 23.554f, 24.292f, 25.03f, 8.438f, 8.78f, 9.122f, 9.464f, 18.91f, 19.621f, 20.332f, 21.043f, 10.58f, 10.949f, 11.318f, 11.687f, 
+    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC},{ 0.014f, 0.032f, 0.05f, 0.068f, 0.118f, 0.181f, 0.244f, 0.307f, 0.212f, 0.257f, 0.302f, 0.347f, 0.208f, 0.298f, 0.388f, 0.478f, 1.028f, 1.262f, 1.496f, 1.73f, 1.036f, 1.18f, 1.324f, 1.468f, 0.928f, 1.018f, 1.108f, 1.198f, 2.9f, 3.134f, 3.368f, 3.602f, 2.188f, 2.332f, 2.476f, 2.62f,
+                                                         1.202f, 1.274f, 1.346f, 1.418f, 3.142f, 3.313f, 3.484f, 3.655f, 2.048f, 2.147f, 2.246f, 2.345f, 0.532f, 0.676f, 0.82f, 0.964f, 2.324f, 2.666f, 3.008f, 3.35f, 2.008f, 2.206f, 2.404f, 2.602f, 3.584f, 3.98f, 4.376f, 4.772f, 10.552f, 11.452f, 12.352f, 13.252f, 7.4f, 7.904f, 8.408f, 8.912f,
+                                                         6.752f, 7.148f, 7.544f, 7.94f, 17.752f, 18.652f, 19.552f, 20.452f, 11.432f, 11.936f, 12.44f, 12.944f, 5.932f, 6.184f, 6.436f, 6.688f, 14.42f, 14.978f, 15.536f, 16.094f, 8.704f, 9.01f, 9.316f, 9.622f, 3.11f, 3.236f, 3.362f, 3.488f, 7.39f, 7.669f, 7.948f, 8.227f, 4.388f, 4.541f, 4.694f, 4.847f,
+                                                         8.56f, 8.866f, 9.172f, 9.478f, 19.892f, 20.558f, 21.224f, 21.89f, 11.548f, 11.908f, 12.268f, 12.628f, 11.008f, 11.314f, 11.62f, 11.926f, 25.22f, 25.886f, 26.552f, 27.218f, 14.428f, 14.788f, 15.148f, 15.508f, 7.322f, 7.502f, 7.682f, 7.862f, 16.462f, 16.849f, 17.236f, 17.623f, 9.248f, 9.455f, 9.662f, 9.869f,
+                                                         0.158f, 0.392f, 0.626f, 0.86f, 1.27f, 1.765f, 2.26f, 2.755f, 1.22f, 1.481f, 1.742f, 2.003f, 2.224f, 2.746f, 3.268f, 3.79f, 6.788f, 7.886f, 8.984f, 10.082f, 4.78f, 5.356f, 5.932f, 6.508f, 6.4f, 6.922f, 7.444f, 7.966f, 15.572f, 16.67f, 17.768f, 18.866f, 9.388f, 9.964f, 10.54f, 11.116f,
+                                                         4.802f, 5.09f, 5.378f, 5.666f, 11.206f, 11.809f, 12.412f, 13.015f, 6.512f, 6.827f, 7.142f, 7.457f, 6.004f, 6.58f, 7.156f, 7.732f, 14.996f, 16.202f, 17.408f, 18.614f, 9.208f, 9.838f, 10.468f, 11.098f, 17.984f, 19.244f, 20.504f, 21.764f, 42.808f, 45.436f, 48.064f, 50.692f, 25.256f, 26.624f, 27.992f, 29.36f,
+                                                        28.064f, 29.324f, 30.584f, 31.844f, 63.832f, 66.46f, 69.088f, 71.716f, 36.2f, 37.568f, 38.936f, 40.304f, 18.316f, 19.f, 19.684f, 20.368f, 40.916f, 42.338f, 43.76f, 45.182f, 22.816f, 23.554f, 24.292f, 25.03f, 8.438f, 8.78f, 9.122f, 9.464f, 18.91f, 19.621f, 20.332f, 21.043f, 10.58f, 10.949f, 11.318f, 11.687f,
                                                         20.944f, 21.682f, 22.42f, 23.158f, 46.388f, 47.918f, 49.448f, 50.978f, 25.66f, 26.452f, 27.244f, 28.036f, 26.848f, 27.586f, 28.324f, 29.062f, 58.628f, 60.158f, 61.688f, 63.218f, 31.996f, 32.788f, 33.58f, 34.372f, 16.106f, 16.502f, 16.898f, 17.294f, 34.894f, 35.713f, 36.532f, 37.351f, 18.896f, 19.319f, 19.742f, 20.165f});
 
-    auto expGradW = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC},{7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 
-                                                        7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 
-                                                        7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 
+    auto expGradW = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC},{7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f,
+                                                        7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f,
+                                                        7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f,
                                                         7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f, 7.52f, 7.84f, 8.16f});
     // auto expGradB('c', {oC},{});
 
@@ -1350,20 +1382,20 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test3) {
     auto bias     = NDArrayFactory::create<TypeParam>('c', {oC}, {1,2,3});
     auto gradO    = NDArrayFactory::create<TypeParam>('c', {bS, oC, oD, oH, oW});
 
-    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW},{2.091f, 4.356f, 2.268f, 4.53f, 9.42f, 4.896f, 4.65f, 9.672f, 5.028f, 2.517f, 5.226f, 2.712f, 4.932f, 10.242f, 5.316f, 10.62f, 22.02f, 11.412f, 10.908f, 22.62f, 11.724f, 5.868f, 12.15f, 6.288f, 2.913f, 6.03f, 3.12f, 6.234f, 12.888f, 6.66f, 6.402f, 13.236f, 6.84f, 3.423f, 7.068f, 3.648f, 
-                                                        2.415f, 5.04f, 2.628f, 5.25f, 10.932f, 5.688f, 5.37f, 11.184f, 5.82f, 2.913f, 6.054f, 3.144f, 5.724f, 11.898f, 6.18f, 12.348f, 25.62f, 13.284f, 12.636f, 26.22f, 13.596f, 6.804f, 14.094f, 7.296f, 3.381f, 7.002f, 3.624f, 7.242f, 14.976f, 7.74f, 7.41f, 15.324f, 7.92f, 3.963f, 8.184f, 4.224f, 
-                                                        2.739f, 5.724f, 2.988f, 5.97f, 12.444f, 6.48f, 6.09f, 12.696f, 6.612f, 3.309f, 6.882f, 3.576f, 6.516f, 13.554f, 7.044f, 14.076f, 29.22f, 15.156f, 14.364f, 29.82f, 15.468f, 7.74f, 16.038f, 8.304f, 3.849f, 7.974f, 4.128f, 8.25f, 17.064f, 8.82f, 8.418f, 17.412f, 9.f, 4.503f, 9.3f, 4.8f, 
-                                                        3.063f, 6.408f, 3.348f, 6.69f, 13.956f, 7.272f, 6.81f, 14.208f, 7.404f, 3.705f, 7.71f, 4.008f, 7.308f, 15.21f, 7.908f, 15.804f, 32.82f, 17.028f, 16.092f, 33.42f, 17.34f, 8.676f, 17.982f, 9.312f, 4.317f, 8.946f, 4.632f, 9.258f, 19.152f, 9.9f, 9.426f, 19.5f, 10.08f, 5.043f, 10.416f, 5.376f, 
-                                                        5.619f, 11.484f, 5.868f, 11.73f, 23.964f, 12.24f, 12.138f, 24.792f, 12.66f, 6.333f, 12.93f, 6.6f, 12.42f, 25.362f, 12.948f, 25.884f, 52.836f, 26.964f, 26.748f, 54.588f, 27.852f, 13.932f, 28.422f, 14.496f, 6.873f, 14.022f, 7.152f, 14.298f, 29.16f, 14.868f, 14.754f, 30.084f, 15.336f, 7.671f, 15.636f, 7.968f, 
-                                                        6.807f, 13.896f, 7.092f, 14.178f, 28.932f, 14.76f, 14.586f, 29.76f, 15.18f, 7.593f, 15.486f, 7.896f, 14.94f, 30.474f, 15.54f, 31.068f, 63.348f, 32.292f, 31.932f, 65.1f, 33.18f, 16.596f, 33.822f, 17.232f, 8.205f, 16.722f, 8.52f, 17.034f, 34.704f, 17.676f, 17.49f, 35.628f, 18.144f, 9.075f, 18.48f, 9.408f, 
-                                                        7.995f, 16.308f, 8.316f, 16.626f, 33.9f, 17.28f, 17.034f, 34.728f, 17.7f, 8.853f, 18.042f, 9.192f, 17.46f, 35.586f, 18.132f, 36.252f, 73.86f, 37.62f, 37.116f, 75.612f, 38.508f, 19.26f, 39.222f, 19.968f, 9.537f, 19.422f, 9.888f, 19.77f, 40.248f, 20.484f, 20.226f, 41.172f, 20.952f, 10.479f, 21.324f, 10.848f, 
+    auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW},{2.091f, 4.356f, 2.268f, 4.53f, 9.42f, 4.896f, 4.65f, 9.672f, 5.028f, 2.517f, 5.226f, 2.712f, 4.932f, 10.242f, 5.316f, 10.62f, 22.02f, 11.412f, 10.908f, 22.62f, 11.724f, 5.868f, 12.15f, 6.288f, 2.913f, 6.03f, 3.12f, 6.234f, 12.888f, 6.66f, 6.402f, 13.236f, 6.84f, 3.423f, 7.068f, 3.648f,
+                                                        2.415f, 5.04f, 2.628f, 5.25f, 10.932f, 5.688f, 5.37f, 11.184f, 5.82f, 2.913f, 6.054f, 3.144f, 5.724f, 11.898f, 6.18f, 12.348f, 25.62f, 13.284f, 12.636f, 26.22f, 13.596f, 6.804f, 14.094f, 7.296f, 3.381f, 7.002f, 3.624f, 7.242f, 14.976f, 7.74f, 7.41f, 15.324f, 7.92f, 3.963f, 8.184f, 4.224f,
+                                                        2.739f, 5.724f, 2.988f, 5.97f, 12.444f, 6.48f, 6.09f, 12.696f, 6.612f, 3.309f, 6.882f, 3.576f, 6.516f, 13.554f, 7.044f, 14.076f, 29.22f, 15.156f, 14.364f, 29.82f, 15.468f, 7.74f, 16.038f, 8.304f, 3.849f, 7.974f, 4.128f, 8.25f, 17.064f, 8.82f, 8.418f, 17.412f, 9.f, 4.503f, 9.3f, 4.8f,
+                                                        3.063f, 6.408f, 3.348f, 6.69f, 13.956f, 7.272f, 6.81f, 14.208f, 7.404f, 3.705f, 7.71f, 4.008f, 7.308f, 15.21f, 7.908f, 15.804f, 32.82f, 17.028f, 16.092f, 33.42f, 17.34f, 8.676f, 17.982f, 9.312f, 4.317f, 8.946f, 4.632f, 9.258f, 19.152f, 9.9f, 9.426f, 19.5f, 10.08f, 5.043f, 10.416f, 5.376f,
+                                                        5.619f, 11.484f, 5.868f, 11.73f, 23.964f, 12.24f, 12.138f, 24.792f, 12.66f, 6.333f, 12.93f, 6.6f, 12.42f, 25.362f, 12.948f, 25.884f, 52.836f, 26.964f, 26.748f, 54.588f, 27.852f, 13.932f, 28.422f, 14.496f, 6.873f, 14.022f, 7.152f, 14.298f, 29.16f, 14.868f, 14.754f, 30.084f, 15.336f, 7.671f, 15.636f, 7.968f,
+                                                        6.807f, 13.896f, 7.092f, 14.178f, 28.932f, 14.76f, 14.586f, 29.76f, 15.18f, 7.593f, 15.486f, 7.896f, 14.94f, 30.474f, 15.54f, 31.068f, 63.348f, 32.292f, 31.932f, 65.1f, 33.18f, 16.596f, 33.822f, 17.232f, 8.205f, 16.722f, 8.52f, 17.034f, 34.704f, 17.676f, 17.49f, 35.628f, 18.144f, 9.075f, 18.48f, 9.408f,
+                                                        7.995f, 16.308f, 8.316f, 16.626f, 33.9f, 17.28f, 17.034f, 34.728f, 17.7f, 8.853f, 18.042f, 9.192f, 17.46f, 35.586f, 18.132f, 36.252f, 73.86f, 37.62f, 37.116f, 75.612f, 38.508f, 19.26f, 39.222f, 19.968f, 9.537f, 19.422f, 9.888f, 19.77f, 40.248f, 20.484f, 20.226f, 41.172f, 20.952f, 10.479f, 21.324f, 10.848f,
                                                         9.183f, 18.72f, 9.54f, 19.074f, 38.868f, 19.8f, 19.482f, 39.696f, 20.22f, 10.113f, 20.598f, 10.488f, 19.98f, 40.698f, 20.724f, 41.436f, 84.372f, 42.948f, 42.3f, 86.124f, 43.836f, 21.924f, 44.622f, 22.704f, 10.869f, 22.122f, 11.256f, 22.506f, 45.792f, 23.292f, 22.962f, 46.716f, 23.76f, 11.883f, 24.168f, 12.288f});
 
-    auto expGradW = NDArrayFactory::create<TypeParam>('c', {oC, iC, kD, kH, kW},{5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 
-                                                        5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 
-                                                        7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 
-                                                        7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 
-                                                        10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 
+    auto expGradW = NDArrayFactory::create<TypeParam>('c', {oC, iC, kD, kH, kW},{5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f,
+                                                        5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f, 5.28f,
+                                                        7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f,
+                                                        7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f, 7.84f,
+                                                        10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f,
                                                         10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f, 10.4f});
 
     auto expGradB = NDArrayFactory::create<TypeParam>('c', {oC},{2.64f, 3.92f, 5.2f});
@@ -1407,9 +1439,9 @@ TYPED_TEST(TypedConvolutionTests1, depthwise_conv2d_1) {
     auto weights  = NDArrayFactory::create<TypeParam>('c', {kH, kW, iC, mC});
 
 
-    auto expOutput = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, oC},{12.f, 12.8f, 13.6f, 14.4f, 12.f, 12.8f, 13.6f, 14.4f, 5.2f, 5.6f, 6.f, 6.4f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 
-                                                     13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 5.6f, 6.4f, 7.2f, 8.f, 5.6f, 6.4f, 7.2f, 8.f, 2.f, 2.4f, 2.8f, 3.2f, 
-                                                     12.f, 12.8f, 13.6f, 14.4f, 12.f, 12.8f, 13.6f, 14.4f, 5.2f, 5.6f, 6.f, 6.4f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 
+    auto expOutput = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, oC},{12.f, 12.8f, 13.6f, 14.4f, 12.f, 12.8f, 13.6f, 14.4f, 5.2f, 5.6f, 6.f, 6.4f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f,
+                                                     13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 5.6f, 6.4f, 7.2f, 8.f, 5.6f, 6.4f, 7.2f, 8.f, 2.f, 2.4f, 2.8f, 3.2f,
+                                                     12.f, 12.8f, 13.6f, 14.4f, 12.f, 12.8f, 13.6f, 14.4f, 5.2f, 5.6f, 6.f, 6.4f, 13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f,
                                                      13.2f, 14.4f, 15.6f, 16.8f, 13.2f, 14.4f, 15.6f, 16.8f, 5.4f, 6.f, 6.6f, 7.2f, 5.6f, 6.4f, 7.2f, 8.f, 5.6f, 6.4f, 7.2f, 8.f, 2.f, 2.4f, 2.8f, 3.2f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -1439,7 +1471,7 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_2) {
     auto weights  = NDArrayFactory::create<double>('c', {kH, kW, iC, mC});
 
 
-    auto expOutput = NDArrayFactory::create<double>('c', {bS, oH, oW, oC},{13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f, 
+    auto expOutput = NDArrayFactory::create<double>('c', {bS, oH, oW, oC},{13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f,
                                                      13.2f,  14.4f,  15.6f,  16.8f, 13.2f,  14.4f,  15.6f,  16.8f,  13.2f,  14.4f,  15.6f,  16.8f, 13.2f,  14.4f,  15.6f,  16.8f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -1697,13 +1729,13 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test1) {
 
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC});
-    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 4, 3, 3}, {64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 
-                                                   64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 
-                                                   96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f, 
-                                                   48.f, 48.f, 48.f, 48.f, 48.f, 48.f, 24.f, 24.f, 24.f, 48.f, 48.f, 48.f, 48.f, 48.f, 48.f, 24.f, 24.f, 24.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f, 
-                                                   64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 
-                                                   64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 
-                                                   96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f, 
+    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 4, 3, 3}, {64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f,
+                                                   64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f,
+                                                   96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f,
+                                                   48.f, 48.f, 48.f, 48.f, 48.f, 48.f, 24.f, 24.f, 24.f, 48.f, 48.f, 48.f, 48.f, 48.f, 48.f, 24.f, 24.f, 24.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f,
+                                                   64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f,
+                                                   64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f,
+                                                   96.f, 96.f, 96.f, 96.f, 96.f, 96.f, 48.f, 48.f, 48.f, 64.f, 64.f, 64.f, 64.f, 64.f, 64.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f,
                                                    48.f, 48.f, 48.f, 48.f, 48.f, 48.f, 24.f, 24.f, 24.f, 48.f, 48.f, 48.f, 48.f, 48.f, 48.f, 24.f, 24.f, 24.f, 32.f, 32.f, 32.f, 32.f, 32.f, 32.f, 16.f, 16.f, 16.f});
     input = 2.;
     weights = 1.;
@@ -1729,13 +1761,13 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test2) {
 
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC});
-    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 4, 3, 3}, {534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 
-                                                   380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 
-                                                   686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 152.f, 155.2f, 158.4f, 152.f, 155.2f, 158.4f, 66.4f, 68.f, 69.6f, 
-                                                   170.4f, 175.2f, 180.f, 170.4f, 175.2f, 180.f, 70.8f, 73.2f, 75.6f, 170.4f, 175.2f, 180.f, 170.4f, 175.2f, 180.f, 70.8f, 73.2f, 75.6f, 75.2f, 78.4f, 81.6f, 75.2f, 78.4f, 81.6f, 28.f, 29.6f, 31.2f, 
-                                                   534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 
-                                                   380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 
-                                                   686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 152.f, 155.2f, 158.4f, 152.f, 155.2f, 158.4f, 66.4f, 68.f, 69.6f, 
+    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 4, 3, 3}, {534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f,
+                                                   380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f,
+                                                   686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 152.f, 155.2f, 158.4f, 152.f, 155.2f, 158.4f, 66.4f, 68.f, 69.6f,
+                                                   170.4f, 175.2f, 180.f, 170.4f, 175.2f, 180.f, 70.8f, 73.2f, 75.6f, 170.4f, 175.2f, 180.f, 170.4f, 175.2f, 180.f, 70.8f, 73.2f, 75.6f, 75.2f, 78.4f, 81.6f, 75.2f, 78.4f, 81.6f, 28.f, 29.6f, 31.2f,
+                                                   534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f,
+                                                   380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 534.4f, 540.8f, 547.2f, 534.4f, 540.8f, 547.2f, 248.f, 251.2f, 254.4f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f,
+                                                   686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 314.4f, 319.2f, 324.f, 380.8f, 387.2f, 393.6f, 380.8f, 387.2f, 393.6f, 171.2f, 174.4f, 177.6f, 152.f, 155.2f, 158.4f, 152.f, 155.2f, 158.4f, 66.4f, 68.f, 69.6f,
                                                    170.4f, 175.2f, 180.f, 170.4f, 175.2f, 180.f, 70.8f, 73.2f, 75.6f, 170.4f, 175.2f, 180.f, 170.4f, 175.2f, 180.f, 70.8f, 73.2f, 75.6f, 75.2f, 78.4f, 81.6f, 75.2f, 78.4f, 81.6f, 28.f, 29.6f, 31.2f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -1760,9 +1792,9 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test3) {
 
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC});
-    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2, 3},  {686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 
-                                                    686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 
-                                                    686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 
+    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2, 3},  {686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f,
+                                                    686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f,
+                                                    686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f,
                                                     686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f, 686.4f, 696.f, 705.6f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -1844,8 +1876,8 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test6) {
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {kD, kH, kW, iC, oC});
     auto bias     = NDArrayFactory::create<TypeParam>('c', {oC},{1.f, 2.f, 3.f});
-    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 2, 2, 2},{49.f,  49.f, 49.f,  49.f,  49.f,  49.f, 49.f,  49.f,  50.f,  50.f, 50.f,  50.f,  50.f,  50.f, 50.f,  50.f, 
-                                                  51.f,  51.f, 51.f,  51.f,  51.f,  51.f, 51.f,  51.f,  49.f,  49.f, 49.f,  49.f,  49.f,  49.f, 49.f,  49.f, 
+    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 2, 2, 2},{49.f,  49.f, 49.f,  49.f,  49.f,  49.f, 49.f,  49.f,  50.f,  50.f, 50.f,  50.f,  50.f,  50.f, 50.f,  50.f,
+                                                  51.f,  51.f, 51.f,  51.f,  51.f,  51.f, 51.f,  51.f,  49.f,  49.f, 49.f,  49.f,  49.f,  49.f, 49.f,  49.f,
                                                   50.f,  50.f, 50.f,  50.f,  50.f,  50.f, 50.f,  50.f,  51.f,  51.f, 51.f,  51.f,  51.f,  51.f, 51.f,  51.f});
     input = 2.;
     weights = 0.5;
@@ -1873,9 +1905,9 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test7) {
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {oC, iC, kD, kH, kW});
     auto bias     = NDArrayFactory::create<TypeParam>('c', {oC},{1.f, 2.f, 3.f});
-    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 2, 2, 2},{236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 698.f, 698.f, 698.f, 698.f, 
-                                                  698.f, 698.f, 698.f, 698.f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 
-                                                  236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 698.f, 698.f, 698.f, 698.f, 
+    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 2, 2, 2},{236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 698.f, 698.f, 698.f, 698.f,
+                                                  698.f, 698.f, 698.f, 698.f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f,
+                                                  236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 236.2f, 698.f, 698.f, 698.f, 698.f,
                                                   698.f, 698.f, 698.f, 698.f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f, 1159.8f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -1903,8 +1935,8 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test8) {
 
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {oC, iC, kD, kH, kW});
-    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 2, 2, 2},{235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 
-                                                  1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 
+    auto expected = NDArrayFactory::create<TypeParam>('c', {2, 3, 2, 2, 2},{235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f,
+                                                  1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f, 235.2f,
                                                   696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 696.f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f, 1156.8f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -1997,9 +2029,9 @@ TYPED_TEST(TypedConvolutionTests1, pointwise_conv2d_test1) {
     auto bias     = NDArrayFactory::create<TypeParam>('c', {oC});
 
 
-    auto expOutput = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, oC},{ 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 
-                                                      7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 
-                                                      6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 
+    auto expOutput = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, oC},{ 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f,
+                                                      7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f,
+                                                      6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f,
                                                       5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f, 5.4f, 6.2f, 7.0f});
     input = 2.;
     weights.linspace(0.1, 0.1);
@@ -2110,20 +2142,20 @@ TEST_F(ConvolutionTests1, vol2col_test2) {
     auto columns = NDArrayFactory::create<float>('c', {kD, iC, kH, oW, kW, bS, oD, oH});
     columns.permutei({5, 1, 0, 2, 4, 6, 7, 3});
     columns = -1.;
-    auto columnsExpected = NDArrayFactory::create<float>('c', {bS, iC, kD, kH, kW, oD, oH, oW}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 
-10.f, 11.f, 12.f, 2.f, 0.f, 4.f, 0.f, 6.f, 0.f, 8.f, 0.f, 10.f, 0.f, 12.f, 0.f, 3.f, 4.f, 5.f, 6.f, 0.f, 0.f, 9.f, 10.f, 11.f, 12.f, 0.f, 0.f, 4.f, 0.f, 6.f, 0.f, 0.f, 0.f, 10.f, 0.f, 12.f, 0.f, 0.f, 0.f, 5.f, 6.f, 0.f, 0.f, 0.f, 0.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 6.f, 0.f, 0.f, 0.f, 0.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 7.f, 8.f, 
-9.f, 10.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 8.f, 0.f, 10.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 9.f, 10.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 10.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 
-0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 14.f, 0.f, 16.f, 0.f, 18.f, 0.f, 20.f, 0.f, 22.f, 0.f, 24.f, 0.f, 15.f, 16.f, 17.f, 18.f, 0.f, 0.f, 21.f, 22.f, 23.f, 24.f, 0.f, 0.f, 16.f, 0.f, 18.f, 0.f, 0.f, 0.f, 22.f, 0.f, 24.f, 0.f, 0.f, 0.f, 17.f, 18.f, 0.f, 0.f, 0.f, 0.f, 
-23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 18.f, 0.f, 0.f, 0.f, 0.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 20.f, 0.f, 22.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 21.f, 22.f, 23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 22.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 
-0.f, 0.f, 0.f, 23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 26.f, 0.f, 28.f, 0.f, 30.f, 0.f, 32.f, 0.f, 34.f, 0.f, 36.f, 0.f, 27.f, 28.f, 29.f, 30.f, 0.f, 0.f, 33.f, 34.f, 35.f, 36.f, 
-0.f, 0.f, 28.f, 0.f, 30.f, 0.f, 0.f, 0.f, 34.f, 0.f, 36.f, 0.f, 0.f, 0.f, 29.f, 30.f, 0.f, 0.f, 0.f, 0.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 30.f, 0.f, 0.f, 0.f, 0.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 32.f, 0.f, 34.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 33.f, 
-34.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 34.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 38.f, 0.f, 40.f, 
-0.f, 42.f, 0.f, 44.f, 0.f, 46.f, 0.f, 48.f, 0.f, 39.f, 40.f, 41.f, 42.f, 0.f, 0.f, 45.f, 46.f, 47.f, 48.f, 0.f, 0.f, 40.f, 0.f, 42.f, 0.f, 0.f, 0.f, 46.f, 0.f, 48.f, 0.f, 0.f, 0.f, 41.f, 42.f, 0.f, 0.f, 0.f, 0.f, 47.f, 48.f, 0.f, 0.f, 0.f, 0.f, 42.f, 0.f, 0.f, 0.f, 0.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 43.f, 44.f, 45.f, 46.f, 47.f, 
-48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 44.f, 0.f, 46.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 45.f, 46.f, 47.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 46.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 47.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 
-0.f, 0.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 50.f, 0.f, 52.f, 0.f, 54.f, 0.f, 56.f, 0.f, 58.f, 0.f, 60.f, 0.f, 51.f, 52.f, 53.f, 54.f, 0.f, 0.f, 57.f, 58.f, 59.f, 60.f, 0.f, 0.f, 52.f, 0.f, 54.f, 0.f, 0.f, 0.f, 58.f, 0.f, 60.f, 0.f, 0.f, 0.f, 53.f, 54.f, 0.f, 0.f, 0.f, 0.f, 59.f, 60.f, 0.f, 0.f, 
-0.f, 0.f, 54.f, 0.f, 0.f, 0.f, 0.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 56.f, 0.f, 58.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 57.f, 58.f, 59.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 58.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 59.f, 60.f, 
-0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 62.f, 0.f, 64.f, 0.f, 66.f, 0.f, 68.f, 0.f, 70.f, 0.f, 72.f, 0.f, 63.f, 64.f, 65.f, 66.f, 0.f, 0.f, 69.f, 70.f, 71.f, 72.f, 0.f, 0.f, 64.f, 0.f, 66.f, 
-0.f, 0.f, 0.f, 70.f, 0.f, 72.f, 0.f, 0.f, 0.f, 65.f, 66.f, 0.f, 0.f, 0.f, 0.f, 71.f, 72.f, 0.f, 0.f, 0.f, 0.f, 66.f, 0.f, 0.f, 0.f, 0.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 68.f, 0.f, 70.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 69.f, 70.f, 71.f, 72.f, 0.f, 0.f, 
+    auto columnsExpected = NDArrayFactory::create<float>('c', {bS, iC, kD, kH, kW, oD, oH, oW}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f,
+10.f, 11.f, 12.f, 2.f, 0.f, 4.f, 0.f, 6.f, 0.f, 8.f, 0.f, 10.f, 0.f, 12.f, 0.f, 3.f, 4.f, 5.f, 6.f, 0.f, 0.f, 9.f, 10.f, 11.f, 12.f, 0.f, 0.f, 4.f, 0.f, 6.f, 0.f, 0.f, 0.f, 10.f, 0.f, 12.f, 0.f, 0.f, 0.f, 5.f, 6.f, 0.f, 0.f, 0.f, 0.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 6.f, 0.f, 0.f, 0.f, 0.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 7.f, 8.f,
+9.f, 10.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 8.f, 0.f, 10.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 9.f, 10.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 10.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 11.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 12.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 14.f, 0.f, 16.f, 0.f, 18.f, 0.f, 20.f, 0.f, 22.f, 0.f, 24.f, 0.f, 15.f, 16.f, 17.f, 18.f, 0.f, 0.f, 21.f, 22.f, 23.f, 24.f, 0.f, 0.f, 16.f, 0.f, 18.f, 0.f, 0.f, 0.f, 22.f, 0.f, 24.f, 0.f, 0.f, 0.f, 17.f, 18.f, 0.f, 0.f, 0.f, 0.f,
+23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 18.f, 0.f, 0.f, 0.f, 0.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 20.f, 0.f, 22.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 21.f, 22.f, 23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 22.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+0.f, 0.f, 0.f, 23.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 24.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 26.f, 0.f, 28.f, 0.f, 30.f, 0.f, 32.f, 0.f, 34.f, 0.f, 36.f, 0.f, 27.f, 28.f, 29.f, 30.f, 0.f, 0.f, 33.f, 34.f, 35.f, 36.f,
+0.f, 0.f, 28.f, 0.f, 30.f, 0.f, 0.f, 0.f, 34.f, 0.f, 36.f, 0.f, 0.f, 0.f, 29.f, 30.f, 0.f, 0.f, 0.f, 0.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 30.f, 0.f, 0.f, 0.f, 0.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 32.f, 0.f, 34.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 33.f,
+34.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 34.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 35.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 36.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 38.f, 0.f, 40.f,
+0.f, 42.f, 0.f, 44.f, 0.f, 46.f, 0.f, 48.f, 0.f, 39.f, 40.f, 41.f, 42.f, 0.f, 0.f, 45.f, 46.f, 47.f, 48.f, 0.f, 0.f, 40.f, 0.f, 42.f, 0.f, 0.f, 0.f, 46.f, 0.f, 48.f, 0.f, 0.f, 0.f, 41.f, 42.f, 0.f, 0.f, 0.f, 0.f, 47.f, 48.f, 0.f, 0.f, 0.f, 0.f, 42.f, 0.f, 0.f, 0.f, 0.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 43.f, 44.f, 45.f, 46.f, 47.f,
+48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 44.f, 0.f, 46.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 45.f, 46.f, 47.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 46.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 47.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 48.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+0.f, 0.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 50.f, 0.f, 52.f, 0.f, 54.f, 0.f, 56.f, 0.f, 58.f, 0.f, 60.f, 0.f, 51.f, 52.f, 53.f, 54.f, 0.f, 0.f, 57.f, 58.f, 59.f, 60.f, 0.f, 0.f, 52.f, 0.f, 54.f, 0.f, 0.f, 0.f, 58.f, 0.f, 60.f, 0.f, 0.f, 0.f, 53.f, 54.f, 0.f, 0.f, 0.f, 0.f, 59.f, 60.f, 0.f, 0.f,
+0.f, 0.f, 54.f, 0.f, 0.f, 0.f, 0.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 56.f, 0.f, 58.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 57.f, 58.f, 59.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 58.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 59.f, 60.f,
+0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 62.f, 0.f, 64.f, 0.f, 66.f, 0.f, 68.f, 0.f, 70.f, 0.f, 72.f, 0.f, 63.f, 64.f, 65.f, 66.f, 0.f, 0.f, 69.f, 70.f, 71.f, 72.f, 0.f, 0.f, 64.f, 0.f, 66.f,
+0.f, 0.f, 0.f, 70.f, 0.f, 72.f, 0.f, 0.f, 0.f, 65.f, 66.f, 0.f, 0.f, 0.f, 0.f, 71.f, 72.f, 0.f, 0.f, 0.f, 0.f, 66.f, 0.f, 0.f, 0.f, 0.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 68.f, 0.f, 70.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 69.f, 70.f, 71.f, 72.f, 0.f, 0.f,
 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 70.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 71.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f});
 
     graph::Context context(1);
@@ -2164,11 +2196,11 @@ TEST_F(ConvolutionTests1, upsampling2d_test1) {
     auto input  = NDArrayFactory::create<float>('c', {bS, iH, iW, iC});
     input.linspace(1);
 
-    auto expOutput = NDArrayFactory::create<float>('c', {bS, iH*factorH, iW*factorW, iC}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 
-                                         7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 
-                                        13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 
-                                        19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 
-                                        25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 
+    auto expOutput = NDArrayFactory::create<float>('c', {bS, iH*factorH, iW*factorW, iC}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f,
+                                         7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f,
+                                        13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
+                                        19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f,
+                                        25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f,
                                         31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f});
 
     nd4j::ops::upsampling2d op;
@@ -2192,11 +2224,11 @@ TEST_F(ConvolutionTests1, upsampling2d_test2) {
     auto input  = NDArrayFactory::create<float>('c', {bS, iC, iH, iW});
     input.linspace(1);
 
-    auto expOutput = NDArrayFactory::create<float>('c', {bS, iC, iH*factorH, iW*factorW}, {1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 
-                                 5.f, 5.f, 5.f, 6.f, 6.f, 6.f, 5.f, 5.f, 5.f, 6.f, 6.f, 6.f, 7.f, 7.f, 7.f, 8.f, 8.f, 8.f, 7.f, 7.f, 7.f, 8.f, 8.f, 8.f, 9.f, 9.f, 9.f, 10.f, 10.f, 10.f, 9.f, 9.f, 9.f, 10.f, 10.f, 10.f, 11.f, 11.f, 11.f, 12.f, 12.f, 12.f, 11.f, 11.f, 11.f, 12.f, 12.f, 12.f, 
-                                13.f, 13.f, 13.f, 14.f, 14.f, 14.f, 13.f, 13.f, 13.f, 14.f, 14.f, 14.f, 15.f, 15.f, 15.f, 16.f, 16.f, 16.f, 15.f, 15.f, 15.f, 16.f, 16.f, 16.f, 17.f, 17.f, 17.f, 18.f, 18.f, 18.f, 17.f, 17.f, 17.f, 18.f, 18.f, 18.f, 19.f, 19.f, 19.f, 20.f, 20.f, 20.f, 19.f, 19.f, 19.f, 20.f, 20.f, 20.f, 
-                                21.f, 21.f, 21.f, 22.f, 22.f, 22.f, 21.f, 21.f, 21.f, 22.f, 22.f, 22.f, 23.f, 23.f, 23.f, 24.f, 24.f, 24.f, 23.f, 23.f, 23.f, 24.f, 24.f, 24.f, 25.f, 25.f, 25.f, 26.f, 26.f, 26.f, 25.f, 25.f, 25.f, 26.f, 26.f, 26.f, 27.f, 27.f, 27.f, 28.f, 28.f, 28.f, 27.f, 27.f, 27.f, 28.f, 28.f, 28.f, 
-                                29.f, 29.f, 29.f, 30.f, 30.f, 30.f, 29.f, 29.f, 29.f, 30.f, 30.f, 30.f, 31.f, 31.f, 31.f, 32.f, 32.f, 32.f, 31.f, 31.f, 31.f, 32.f, 32.f, 32.f, 
+    auto expOutput = NDArrayFactory::create<float>('c', {bS, iC, iH*factorH, iW*factorW}, {1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f,
+                                 5.f, 5.f, 5.f, 6.f, 6.f, 6.f, 5.f, 5.f, 5.f, 6.f, 6.f, 6.f, 7.f, 7.f, 7.f, 8.f, 8.f, 8.f, 7.f, 7.f, 7.f, 8.f, 8.f, 8.f, 9.f, 9.f, 9.f, 10.f, 10.f, 10.f, 9.f, 9.f, 9.f, 10.f, 10.f, 10.f, 11.f, 11.f, 11.f, 12.f, 12.f, 12.f, 11.f, 11.f, 11.f, 12.f, 12.f, 12.f,
+                                13.f, 13.f, 13.f, 14.f, 14.f, 14.f, 13.f, 13.f, 13.f, 14.f, 14.f, 14.f, 15.f, 15.f, 15.f, 16.f, 16.f, 16.f, 15.f, 15.f, 15.f, 16.f, 16.f, 16.f, 17.f, 17.f, 17.f, 18.f, 18.f, 18.f, 17.f, 17.f, 17.f, 18.f, 18.f, 18.f, 19.f, 19.f, 19.f, 20.f, 20.f, 20.f, 19.f, 19.f, 19.f, 20.f, 20.f, 20.f,
+                                21.f, 21.f, 21.f, 22.f, 22.f, 22.f, 21.f, 21.f, 21.f, 22.f, 22.f, 22.f, 23.f, 23.f, 23.f, 24.f, 24.f, 24.f, 23.f, 23.f, 23.f, 24.f, 24.f, 24.f, 25.f, 25.f, 25.f, 26.f, 26.f, 26.f, 25.f, 25.f, 25.f, 26.f, 26.f, 26.f, 27.f, 27.f, 27.f, 28.f, 28.f, 28.f, 27.f, 27.f, 27.f, 28.f, 28.f, 28.f,
+                                29.f, 29.f, 29.f, 30.f, 30.f, 30.f, 29.f, 29.f, 29.f, 30.f, 30.f, 30.f, 31.f, 31.f, 31.f, 32.f, 32.f, 32.f, 31.f, 31.f, 31.f, 32.f, 32.f, 32.f,
                                 33.f, 33.f, 33.f, 34.f, 34.f, 34.f, 33.f, 33.f, 33.f, 34.f, 34.f, 34.f, 35.f, 35.f, 35.f, 36.f, 36.f, 36.f, 35.f, 35.f, 35.f, 36.f, 36.f, 36.f});
 
     nd4j::ops::upsampling2d op;
@@ -2221,20 +2253,20 @@ TEST_F(ConvolutionTests1, upsampling3d_test1) {
     auto input  = NDArrayFactory::create<float>('c', {bS, iD, iH, iW, iC});
     input.linspace(1);
 
-    auto expOutput = NDArrayFactory::create<float>('c', {bS, iD*factorD, iH*factorH, iW*factorW, iC}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 
-             7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 
-             7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 
-            19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 
-            13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 
-            25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 
-            25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 
-            31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 
-            43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 
-            43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 
-            49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 
-            49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 
-            61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 
-            67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 
+    auto expOutput = NDArrayFactory::create<float>('c', {bS, iD*factorD, iH*factorH, iW*factorW, iC}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f,
+             7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 1.f, 2.f, 3.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f,
+             7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 7.f, 8.f, 9.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
+            19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f,
+            13.f, 14.f, 15.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 19.f, 20.f, 21.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f,
+            25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f,
+            25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f,
+            31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f,
+            43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f, 37.f, 38.f, 39.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 40.f, 41.f, 42.f,
+            43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 43.f, 44.f, 45.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 46.f, 47.f, 48.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f,
+            49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f,
+            49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 49.f, 50.f, 51.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f, 55.f, 56.f, 57.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 58.f, 59.f, 60.f,
+            61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f,
+            67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f,
             67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f});
 
     nd4j::ops::upsampling3d op;
@@ -2258,17 +2290,17 @@ TEST_F(ConvolutionTests1, upsampling3d_test2) {
     auto input = NDArrayFactory::create<float>('c', {bS, iC, iD, iH, iW});
     input.linspace(1);
 
-    auto expOutput = NDArrayFactory::create<float>('c', {bS, iC, iD*factorD, iH*factorH, iW*factorW}, { 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 
-             5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 
-            13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 
-            17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 
-            25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 
-            29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 
-            37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 
-            41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 
-            49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 
-            53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 
-            61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 
+    auto expOutput = NDArrayFactory::create<float>('c', {bS, iC, iD*factorD, iH*factorH, iW*factorW}, { 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 3.f, 3.f, 4.f, 4.f, 5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f,
+             5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 5.f, 5.f, 6.f, 6.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 7.f, 7.f, 8.f, 8.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 9.f, 9.f, 10.f, 10.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f, 11.f, 11.f, 12.f, 12.f,
+            13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 13.f, 13.f, 14.f, 14.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 15.f, 15.f, 16.f, 16.f, 17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f,
+            17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 17.f, 17.f, 18.f, 18.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 19.f, 19.f, 20.f, 20.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 21.f, 21.f, 22.f, 22.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f, 23.f, 23.f, 24.f, 24.f,
+            25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 25.f, 25.f, 26.f, 26.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 27.f, 27.f, 28.f, 28.f, 29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f,
+            29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 29.f, 29.f, 30.f, 30.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 31.f, 31.f, 32.f, 32.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 33.f, 33.f, 34.f, 34.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f, 35.f, 35.f, 36.f, 36.f,
+            37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 37.f, 37.f, 38.f, 38.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 39.f, 39.f, 40.f, 40.f, 41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f,
+            41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 41.f, 41.f, 42.f, 42.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 43.f, 43.f, 44.f, 44.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 45.f, 45.f, 46.f, 46.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f, 47.f, 47.f, 48.f, 48.f,
+            49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 49.f, 49.f, 50.f, 50.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 51.f, 51.f, 52.f, 52.f, 53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f,
+            53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 53.f, 53.f, 54.f, 54.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 55.f, 55.f, 56.f, 56.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 57.f, 57.f, 58.f, 58.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f, 59.f, 59.f, 60.f, 60.f,
+            61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f,
             65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f});
 
     nd4j::ops::upsampling3d op;
@@ -2412,13 +2444,13 @@ TEST_F(ConvolutionTests1, deconv2d_test1) {
 
     auto input    = NDArrayFactory::create<float>('c', {bS, iH, iW, iC});
     auto weights  = NDArrayFactory::create<float>('c', {kH, kW, oC, iC});
-    auto exp = NDArrayFactory::create<float>('c', {bS, oH, oW, oC}, {  2.75f, 7.75f, 12.75f, 17.75f, 22.75f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 27.75f, 32.75f, 37.75f, 42.75f, 47.75f, 
-                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f, 
-                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f, 
-                                                  52.75f, 57.75f, 62.75f, 67.75f, 72.75f, 130.5f, 140.5f, 150.5f, 160.5f, 170.5f, 130.5f, 140.5f, 150.5f, 160.5f, 170.5f, 77.75f, 82.75f, 87.75f, 92.75f, 97.75f, 
-                                                   2.75f, 7.75f, 12.75f, 17.75f, 22.75f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 27.75f, 32.75f, 37.75f, 42.75f, 47.75f, 
-                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f, 
-                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f, 
+    auto exp = NDArrayFactory::create<float>('c', {bS, oH, oW, oC}, {  2.75f, 7.75f, 12.75f, 17.75f, 22.75f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 27.75f, 32.75f, 37.75f, 42.75f, 47.75f,
+                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f,
+                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f,
+                                                  52.75f, 57.75f, 62.75f, 67.75f, 72.75f, 130.5f, 140.5f, 150.5f, 160.5f, 170.5f, 130.5f, 140.5f, 150.5f, 160.5f, 170.5f, 77.75f, 82.75f, 87.75f, 92.75f, 97.75f,
+                                                   2.75f, 7.75f, 12.75f, 17.75f, 22.75f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 30.5f, 40.5f, 50.5f, 60.5f, 70.5f, 27.75f, 32.75f, 37.75f, 42.75f, 47.75f,
+                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f,
+                                                  55.5f, 65.5f, 75.5f, 85.5f, 95.5f, 161.f, 181.f, 201.f, 221.f, 241.f, 161.f, 181.f, 201.f, 221.f, 241.f, 105.5f, 115.5f, 125.5f, 135.5f, 145.5f,
                                                   52.75f, 57.75f, 62.75f, 67.75f, 72.75f, 130.5f, 140.5f, 150.5f, 160.5f, 170.5f, 130.5f, 140.5f, 150.5f, 160.5f, 170.5f, 77.75f, 82.75f, 87.75f, 92.75f, 97.75f});
     input = 0.5;
     weights.linspace(0.1, 0.1);
@@ -2445,13 +2477,13 @@ TEST_F(ConvolutionTests1, deconv2d_test2) {
 
     auto input    = NDArrayFactory::create<float>('c', {bS, oH, oW, oC});
     auto weights  = NDArrayFactory::create<float>('c', {kH, kW, iC, oC});
-    auto exp = NDArrayFactory::create<float>('c', {bS, iH, iW, iC}, {2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f, 
-                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 
-                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 
-                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 
-                                                 2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f, 
-                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 
-                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 
+    auto exp = NDArrayFactory::create<float>('c', {bS, iH, iW, iC}, {2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,
+                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f,
+                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f,
+                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f,
+                                                 2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,
+                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f,
+                                                55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f,
                                                 55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f  });
     input = 0.5;
     weights.linspace(0.1, 0.1);
@@ -2673,13 +2705,13 @@ TYPED_TEST(TypedConvolutionTests1, deconv2d_tf_test1) {
     auto input    = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, oC});
     auto weights  = NDArrayFactory::create<TypeParam>('c', {kH, kW, iC, oC});
     auto outShape = NDArrayFactory::create<TypeParam>('c', {4}, {static_cast<TypeParam>(bS), static_cast<TypeParam>(iH), static_cast<TypeParam>(iW), static_cast<TypeParam>(iC)});
-    auto exp = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {  2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  27.75f,   32.75f,   37.75f,   42.75f,   47.75f, 
-                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f, 
-                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f, 
-                                                  52.75f,   57.75f,   62.75f,   67.75f,   72.75f, 130.5f,  140.5f,  150.5f,  160.5f,  170.5f, 130.5f,  140.5f,  150.5f,  160.5f,  170.5f,  77.75f,   82.75f,   87.75f,   92.75f,   97.75f, 
-                                                   2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  27.75f,   32.75f,   37.75f,   42.75f,   47.75f, 
-                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f, 
-                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f, 
+    auto exp = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {  2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  27.75f,   32.75f,   37.75f,   42.75f,   47.75f,
+                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f,
+                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f,
+                                                  52.75f,   57.75f,   62.75f,   67.75f,   72.75f, 130.5f,  140.5f,  150.5f,  160.5f,  170.5f, 130.5f,  140.5f,  150.5f,  160.5f,  170.5f,  77.75f,   82.75f,   87.75f,   92.75f,   97.75f,
+                                                   2.75f,    7.75f,   12.75f,   17.75f,   22.75f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  30.5f,   40.5f,   50.5f,   60.5f,   70.5f,  27.75f,   32.75f,   37.75f,   42.75f,   47.75f,
+                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f,
+                                                  55.5f,   65.5f,   75.5f,   85.5f,   95.5f, 161.f,  181.f,  201.f,  221.f,  241.f, 161.f,  181.f,  201.f,  221.f,  241.f, 105.5f,  115.5f,  125.5f,  135.5f,  145.5f,
                                                   52.75f,   57.75f,   62.75f,   67.75f,   72.75f, 130.5f,  140.5f,  150.5f,  160.5f,  170.5f, 130.5f,  140.5f,  150.5f,  160.5f,  170.5f,  77.75f,   82.75f,   87.75f,   92.75f,   97.75f});
     input = 0.5;
     weights.linspace(0.1, 0.1);
diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp
index 209b16a7a..76a44be0b 100644
--- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp
+++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp
@@ -428,10 +428,11 @@ TEST_F(DeclarableOpsTests13, CellContains_test_1) {
 TEST_F(DeclarableOpsTests13, adjustHue_1) {
 
     NDArray input('c', {2,2,3}, {0,100,56, 17,220,5,  150,97,230, 255,2,13}, nd4j::DataType::FLOAT32);
+    NDArray factor = NDArrayFactory::create<float>(0.5);
     NDArray exp  ('c', {2,2,3}, {100,0,44, 208,5,220, 177,230,97,  2,255,244}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::adjust_hue op;
-    auto results = op.execute({&input}, {0.5}, {2});
+    auto results = op.execute({&input, &factor}, {}, {2});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
@@ -525,10 +526,11 @@ TEST_F(DeclarableOpsTests13, adjustHue_5) {
 TEST_F(DeclarableOpsTests13, adjustSaturation_1) {
 
     NDArray input('c', {2,2,3}, {0,100,56,  17,220,5,         150,97,230,    255,2,13}, nd4j::DataType::FLOAT32);
+    NDArray factor = NDArrayFactory::create<float>(0.5);
     NDArray exp  ('c', {2,2,3}, {50,100,78, 118.5,220,112.5,  190,163.5,230, 255,128.5,134}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::adjust_saturation op;
-    auto results = op.execute({&input}, {0.5}, {2});
+    auto results = op.execute({&input, &factor}, {}, {2});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp
index 41dc12a14..b2ccad86f 100644
--- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp
+++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp
@@ -159,18 +159,19 @@ TEST_F(DeclarableOpsTests15, Test_standarize_bp_1) {
 
 TEST_F(DeclarableOpsTests15, Test_AdjustContrast_1) {
     auto x = NDArrayFactory::create<double>('c', {4,4,3});
-    auto e = NDArrayFactory::create<double>('c', {4,4,3}, {
-        -21.5, -20.5, -19.5,  -15.5, -14.5, -13.5,  -9.5,  -8.5,  -7.5,  -3.5,  -2.5,  -1.5,
-          2.5,   3.5,   4.5,    8.5,   9.5,  10.5,  14.5,  15.5,  16.5,  20.5,  21.5,  22.5,
-         26.5,  27.5,  28.5,   32.5,  33.5,  34.5,  38.5,  39.5,  40.5,  44.5,  45.5,  46.5,
-         50.5,  51.5,  52.5,   56.5,  57.5,  58.5,  62.5,  63.5,  64.5,  68.5,  69.5,  70.5
-    });
+    NDArray factor = NDArrayFactory::create<double>(2.);
+    auto e = NDArrayFactory::create<double>('c', {4,4,3}, {-21.5, -20.5, -19.5,  -15.5, -14.5, -13.5,  -9.5,  -8.5,  -7.5,  -3.5,  -2.5,  -1.5,
+                                     2.5,   3.5,   4.5,    8.5,   9.5,  10.5,  14.5,  15.5,  16.5,  20.5,  21.5,  22.5,
+                                    26.5,  27.5,  28.5,   32.5,  33.5,  34.5,  38.5,  39.5,  40.5,  44.5,  45.5,  46.5,
+                                    50.5,  51.5,  52.5,   56.5,  57.5,  58.5,  62.5,  63.5,  64.5,  68.5,  69.5,  70.5});
+
+
     x.linspace(1.);
     nd4j::ops::adjust_contrast op;
-    auto result = op.execute({&x}, {2.}, {}, {});
+    auto result = op.execute({&x, &factor}, {}, {}, {});
     ASSERT_EQ(Status::OK(), result->status());
     auto out = result->at(0);
-//    out->printIndexedBuffer("Adjusted Constrast");
+
     ASSERT_TRUE(e.equalsTo(out));
     delete result;
 }
diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp
index 269c13f51..a521be97b 100644
--- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp
+++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp
@@ -1774,6 +1774,28 @@ TEST_F(DeclarableOpsTests3, betainc_test10) {
     delete results;
 }
 
+///////////////////////////////////////////////////////////////////
+TEST_F(DeclarableOpsTests3, betainc_test11) {
+
+    NDArray a('c', {4}, {0.7788f, 0.8012f, 0.7244f, 0.2309f}, nd4j::DataType::FLOAT32);
+    NDArray b('c', {4}, {0.7717f, 0.9281f, 0.9846f, 0.4838f}, nd4j::DataType::FLOAT32);
+    NDArray x('c', {4}, {0.9441f, 0.5957f, 0.8669f, 0.3502f}, nd4j::DataType::FLOAT32);
+
+    NDArray expected('c', {4}, {0.912156, 0.634443, 0.898314, 0.624544}, nd4j::DataType::FLOAT32);
+
+    nd4j::ops::betainc op;
+    auto results = op.execute({&a, &b, &x}, {}, {});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    auto *output = results->at(0);
+
+    ASSERT_TRUE(expected.isSameShape(output));
+    ASSERT_TRUE(expected.equalsTo(output));
+
+    delete results;
+}
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests3, zeta_test1) {
 
@@ -2092,8 +2114,26 @@ TEST_F(DeclarableOpsTests3, polygamma_test3) {
     x.linspace(10.);
 
     auto expected= NDArrayFactory::create<double>('c', {3,3}, {1.05166336e-01,-9.04983497e-03, 1.31009323e-03,-2.44459433e-04, 5.31593880e-05,-1.28049888e-05, 3.31755364e-06,-9.07408791e-07, 2.58758130e-07});
+    nd4j::ops::polygamma op;
+    auto results = op.execute({&n, &x}, {}, {});
 
-    //ASSERT_FALSE(true);
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    auto output = results->at(0);
+
+    ASSERT_TRUE(expected.isSameShape(output));
+    ASSERT_TRUE(expected.equalsTo(output));
+
+    delete results;
+}
+
+TEST_F(DeclarableOpsTests3, polygamma_test4) {
+
+    NDArray n('c', {3,4}, {/*0.7788*/0, 0,1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE);
+    NDArray x('c', {3,4}, {0.7717,0.9281,0.9846,0.4838,0.6433,0.6041,0.6501,0.7612,0.7605,0.3948,0.9493,0.8600}, nd4j::DataType::DOUBLE);
+
+    NDArray expected('c', {3,4}, {/*std::numeric_limits<double>::quiet_NaN()*/-1.031918,  -7.021327e-01,  1.682743e+00, -1.851378e+01,3.604167e+01, -3.008293e+02,
+                                1.596005e+03, -4.876665e+03,4.510025e+04, -1.730340e+08,  6.110257e+05, -1.907087e+07}, nd4j::DataType::DOUBLE);
 
     nd4j::ops::polygamma op;
     auto results = op.execute({&n, &x}, {}, {});
@@ -2108,6 +2148,26 @@ TEST_F(DeclarableOpsTests3, polygamma_test3) {
     delete results;
 }
 
+TEST_F(DeclarableOpsTests3, digamma_1) {
+
+    NDArray x('c', {18}, {-25, -24.99999, -21.5, -21.2, -5.5, -4.1, -2.1, -0.5, -0.3, 0., 0.2, 1, 1.5, 2.2, 5.2, 19., 21, 22.2}, nd4j::DataType::DOUBLE);
+
+    NDArray expected('c', {18}, {std::numeric_limits<double>::infinity(), -99996.761229, 3.091129, 7.401432, 1.792911,11.196838,10.630354, 0.03649, 2.11331,
+                                 std::numeric_limits<double>::infinity(),-5.28904,-0.577216, 0.03649, 0.544293, 1.549434,2.917892, 3.020524, 3.077401}, nd4j::DataType::DOUBLE);
+
+    nd4j::ops::digamma op;
+    auto results = op.execute({&x}, {}, {});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    auto output = results->at(0);
+
+    ASSERT_TRUE(expected.isSameShape(output));
+    ASSERT_TRUE(expected.equalsTo(output));
+
+    delete results;
+}
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests3, svd_test1) {