Discover the Thrills of the Basketball BNXT League International
The Basketball BNXT League International is revolutionizing the way fans engage with basketball. With fresh matches updated daily and expert betting predictions, it's a haven for enthusiasts seeking the latest in high-stakes competition. This league brings together top talent from across Europe, offering a unique blend of skill, strategy, and excitement. Whether you're a seasoned fan or new to the sport, the BNXT League International provides an unparalleled viewing experience.
Understanding the Basketball BNXT League International
The Basketball BNXT League International is a premier basketball competition that unites clubs from Belgium and the Netherlands. It represents a merger of the former Belgian Pro League and Dutch Basketball League (DBL), creating a dynamic and competitive environment. The league features some of Europe's finest teams and players, making it a must-watch for basketball aficionados.
- Competitive Structure: The league operates on a round-robin format, ensuring that each team competes against every other team multiple times throughout the season.
- Diverse Talent: With players hailing from various countries, the league showcases a rich diversity of playing styles and strategies.
- High-Level Play: The BNXT League International is known for its fast-paced and high-scoring games, providing fans with thrilling entertainment.
Daily Match Updates: Stay Informed
One of the standout features of the Basketball BNXT League International is its commitment to keeping fans informed with daily match updates. Whether you're following your favorite team or exploring new contenders, you can rely on timely information to enhance your viewing experience.
- Live Scores: Access real-time scores to keep track of your team's performance throughout the game.
- Match Highlights: Watch key moments and plays that define each game, ensuring you don't miss any action.
- Post-Match Analysis: Gain insights into game strategies and player performances with expert commentary.
Expert Betting Predictions: Enhance Your Experience
Betting adds an extra layer of excitement to watching basketball. The BNXT League International offers expert betting predictions to help you make informed decisions. These predictions are crafted by seasoned analysts who consider various factors such as team form, player injuries, and historical performance.
- Data-Driven Insights: Predictions are based on comprehensive data analysis, providing a reliable guide for bettors.
- Diverse Betting Options: From match outcomes to player statistics, explore a wide range of betting opportunities.
- Informed Decision-Making: Use expert insights to enhance your betting strategy and increase your chances of success.
The Teams: A Showcase of European Talent
The Basketball BNXT League International features some of Europe's most talented teams. Each club brings its unique style and philosophy to the court, creating a diverse and competitive landscape.
- Oostende: Known for their disciplined play and strong defense, Oostende consistently challenges opponents with their strategic approach.
- Anvers BC: With a focus on fast-paced offense, Anvers BC excels in creating scoring opportunities through dynamic plays.
- Spirou Charleroi: Spirou Charleroi combines experienced veterans with young talent, making them a formidable force in the league.
- AZ Alkmaar: AZ Alkmaar's emphasis on teamwork and resilience has earned them a reputation as one of the league's top contenders.
The Players: Stars of the Court
The success of any basketball league is built on its players. The BNXT League International boasts an impressive roster of athletes who bring skill, passion, and dedication to every game.
- Nicolas De Jong: A versatile guard known for his sharpshooting abilities and leadership on the court.
- Marcos Knight: With his impressive athleticism and defensive prowess, Knight is a key player for his team.
- Luke Nelson: A forward with exceptional rebounding skills and a knack for scoring in clutch moments.
- Jordan Theodore: Renowned for his playmaking skills and court vision, Theodore orchestrates his team's offense with precision.
Fan Engagement: Connecting with the Community
The Basketball BNXT League International places a strong emphasis on fan engagement. Through various initiatives, fans can connect with teams, players, and fellow enthusiasts in meaningful ways.
- Social Media Interaction: Follow your favorite teams and players on social media platforms for updates, behind-the-scenes content, and interactive posts.
- Fan Events: Participate in events such as meet-and-greets, autograph sessions, and fan nights to experience the excitement firsthand.
- Community Forums: Join online forums to discuss games, share opinions, and connect with other fans from around the world.
The Future of Basketball: Innovation in the BNXT League International
The BNXT League International is at the forefront of innovation in basketball. By embracing new technologies and strategies, the league continues to evolve and captivate audiences worldwide.
- Digital Platforms: Utilize advanced digital platforms for streaming games live or on-demand, ensuring fans never miss out on any action.
- Data Analytics: Leverage data analytics to enhance team performance and provide deeper insights into game dynamics.
- Sustainability Initiatives: Commitment to sustainability through eco-friendly practices at venues and events promotes a greener future for sports.
Betting Strategies: Tips for Success
danyelc/tensorflow-tpu<|file_sep|>/tensorflow/compiler/xla/service/gpu/tests/cosine_distance_test.cc
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://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.
==============================================================================*/
#include "tensorflow/compiler/xla/service/gpu/tests/cosine_distance_test_util.h"
#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h"
#include "tensorflow/compiler/xla/service/hlo_module_config.h"
namespace xla {
namespace gpu {
class CosineDistanceTest : public GpuCodegenTest {};
TEST_F(CosineDistanceTest,
TestCosineDistanceSmallBatch1D) {
#if XLA_ENABLE_XCBLAS
#define COSINE_DISTANCE_TEST_CASE(F)
{
std::vector v1 = {F(1.f), F(2.f), F(5.f)};
std::vector v2 = {F(2.f), F(1.f), F(5.f)};
TF_ASSERT_OK_AND_ASSIGN(auto result, RunCosineDistanceTest(
/*batch_size=*/1 /*nvec=*/1 /*mvec=*/3 /*v1=*/v1
/*v2=*/v2));
EXPECT_EQ(result.size(), v1.size());
EXPECT_NEAR(result[0], F(0.28867513f), kTolerance);
EXPECT_NEAR(result[1], F(0.91914503f), kTolerance);
}
#else // XLA_ENABLE_XCBLAS
#define COSINE_DISTANCE_TEST_CASE(F)
{
std::vector
v1 = {F(1.f), F(2.f), F(5.f)};
std::vector
v2 = {F(2.f), F(1.f), F(5.f)};
TF_ASSERT_OK_AND_ASSIGN(auto result16x8x8x4x4x4x4x4x4x4x4,
RunCosineDistanceTest16x8x8x4x4x4x4x4x4x4x4(
/*batch_size=*/1 /*nvec=*/1 /*mvec=*/3 /*v1=*/v1
/*v2=*/v2));
EXPECT_EQ(result16x8x8x4x4x4x4x4x4x4x416->size(), v1.size());
EXPECT_NEAR((*result16x8x8x4x4x4x4x4x4x416)[0], F(0.28867513f), kTolerance);
EXPECT_NEAR((*result16x8x8x4x4x4x4x4x8_416)[0], F(0.91914503f), kTolerance);
}
#endif
#define RUN(F) COSINE_DISTANCE_TEST_CASE(F)
#define RUN_WITH_FLOAT(F) COSINE_DISTANCE_TEST_CASE(F)
#define RUN_WITH_HALF(F) COSINE_DISTANCE_TEST_CASE(F)
#define RUN_WITH_QUARTER(F) COSINE_DISTANCE_TEST_CASE(F)
#define RUN_WITH_EIGHTH(F) COSINE_DISTANCE_TEST_CASE(F)
#define RUN_WITH_SIXTEENTH(F) COSINE_DISTANCE_TEST_CASE(F)
RUN_WITH_FLOAT(1.)
RUN_WITH_HALF(2.)
RUN_WITH_QUARTER(5.)
RUN_WITH_EIGHTH(-7.)
RUN_WITH_SIXTEENTH(-11.)
#undef COSINE_DISTANCE_TEST_CASE
#undef RUN
#undef RUN_WITH_FLOAT
#undef RUN_WITH_HALF
#undef RUN_WITH_QUARTER
#undef RUN_WITH_EIGHTH
#undef RUN_WITH_SIXTEENTH
} // namespace gpu
} // namespace xla
<|file_sep|>// Copyright (c) Microsoft Corporation.
// Licensed under the MIT license.
#include "src/ops/nn/pooling_ops.h"
#include "src/common/shape_inference.h"
#include "src/common/utils.h"
#include "src/common/value_utils.h"
#include "src/ops/nn/activation_ops.h"
namespace mshadow {
namespace op {
DML_OP_IMPL(pooling_nchw,
["ReduceMax", "ReduceMean", "ReduceSum"],
[reduce_type]{
if (reduce_type == "ReduceMax") return DML_POOLING_TYPE_MAX;
if (reduce_type == "ReduceMean") return DML_POOLING_TYPE_AVERAGE;
if (reduce_type == "ReduceSum") return DML_POOLING_TYPE_SUM;
LOG(FATAL) << reduce_type << " not supported";
}) {
#if MSHADOW_USE_DML
using namespace dml;
auto input_desc = input[0]->GetTensorDesc();
auto input_tensor = input[0]->GetTensor();
CHECK(input_desc.data_type == mshadow::kFloat32 || input_desc.data_type == mshadow::kFloat16)
<< "Only support float32 or float16 data type";
const TensorShape& input_shape = input_desc.shape;
int num_axes = input_shape.NumAxes();
CHECK_GE(num_axes, 2U && num_axes <= 5U)
<< "Input rank should be between [2-5], but got " << num_axes;
int num_channels = -1;
if (num_axes == input_shape.NumAxes()) {
num_channels = input_shape[1];
CHECK_GT(num_channels,
-1)
<< "The channel dimension size should be greater than zero.";
CHECK_EQ(num_channels % GetDmlDevice().GetComputeUnitCount(), 0)
<< "The number of channels (" << num_channels << ") must be divisible "
"by number of compute units (" << GetDmlDevice().GetComputeUnitCount() << ").";
}
int kernel_h = static_cast(attrs.pooled_height);
int kernel_w = static_cast(attrs.pooled_width);
int stride_h = static_cast(attrs.stride_h);
int stride_w = static_cast(attrs.stride_w);
int pad_h = static_cast(attrs.pad_h);
int pad_w = static_cast(attrs.pad_w);
TensorShape output_shape{input_shape};
output_shape[0] = attrs.batch_size;
output_shape[1] = attrs.num_pooled;
output_shape[2] = attrs.pooled_height;
output_shape[3] = attrs.pooled_width;
#if MXNET_USE_INT64_TENSOR_DESC
auto desc_maker =
[](DataType dtype,
const TensorShape& shape,
bool is_dynamic,
bool force_static) {
return TensorDesc::Create(
shape,
DataTypeImpl::MakeType(DataTypeEnumToDmlType(dtype)),
force_static ? TensorDescBindingFlags::kForceStatic : TensorDescBindingFlags::kNone);
};
#else // MXNET_USE_INT64_TENSOR_DESC
auto desc_maker =
[](DataType dtype,
const TensorShape& shape,
bool is_dynamic,
bool force_static) {
return TensorDesc::Create(
shape.AsShapeT(),
DataTypeImpl::MakeType(DataTypeEnumToDmlType(dtype)),
force_static ? TensorDescBindingFlags::kForceStatic : TensorDescBindingFlags::kNone);
};
#endif
#if MXNET_USE_INT64_TENSOR_DESC
auto desc_updater =
[](TensorDesc& desc,
const TensorShape& shape,
bool is_dynamic,
bool force_static) {
desc.SetShape(shape);
desc.SetDataType(DataTypeImpl::MakeType(desc.GetDataType()));
desc.SetFlags(
force_static ? TensorDescBindingFlags::kForceStatic : TensorDescBindingFlags::kNone);
};
#else // MXNET_USE_INT64_TENSOR_DESC
auto desc_updater =
[](TensorDesc& desc,
const TensorShape& shape,
bool is_dynamic,
bool force_static) {
desc.SetShape(shape.AsShapeT());
desc.SetDataType(DataTypeImpl::MakeType(desc.GetDataType()));
desc.SetFlags(
force_static ? TensorDescBindingFlags::kForceStatic : TensorDescBindingFlags::kNone);
};
#endif
#if MXNET_USE_INT64_TENSOR_DESC
output->SetTensorDesc(desc_maker(input_desc.data_type,
output_shape,
false /*is_dynamic*/,
true /*force_static*/));
#else // MXNET_USE_INT64_TENSOR_DESC
output->SetTensorDesc(desc_maker(input_desc.data_type,
output_shape.AsShapeT(),
false /*is_dynamic*/,
true /*force_static*/));
#endif
#if MXNET_USE_INT64_TENSOR_DESC
auto output_desc = output->GetTensorDesc();
#else // MXNET_USE_INT64_TENSOR_DESC
auto output_desc = output->GetTensorDesc();
#endif
#if MXNET_USE_INT64_TENSOR_DESC
auto input_desc_reshaped =
desc_updater(input_desc,
{input_shape[0],
num_channels / GetDmlDevice().GetComputeUnitCount(),
input_shape[2],
input_shape[3],
num_channels % GetDmlDevice().GetComputeUnitCount()},
false /*is_dynamic*/,
true /*force_static*/);
#else // MXNET_USE_INT64_TENSOR_DESC
auto input_desc_reshaped =
desc_updater(input_desc,
{input_shape[0],
num_channels / GetDmlDevice().GetComputeUnitCount(),
input_shape[2],
input_shape[3],
num_channels % GetDmlDevice().GetComputeUnitCount()}.AsShapeT(),
false /*is_dynamic*/,
true /*force_static*/);
#endif
#if MXNET_USE_INT64_TENSOR_DESC
auto output_desc_reshaped =
desc_updater(output_desc,
{output_shape[0],
num_channels / GetDmlDevice().GetComputeUnitCount(),
output_shape[2],
output_shape[3],
num_channels % GetDmlDevice().GetComputeUnitCount()},
false /*is_dynamic*/,
true /*force_static*/);
#else // MXNET_USE_INT64_TENSOR_DESC
auto output_desc_reshaped =
desc_updater(output_desc,
{output_shape[0],
num_channels / GetDmlDevice().GetComputeUnitCount(),
output_shape[2],
output_shape[3],
num_channels % GetDmlDevice().GetComputeUnitCount()}.AsShapeT(),
false /*is_dynamic*/,
true /*force_static*/);
#endif
#if MXNET_USE_INT64_TENSOR_DESC
#if !defined(__GNUC__) || (__GNUC__ >= 6)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
#endif
#ifdef _MSC_VER
#ifdef __INTELLISENSE__
#pragma warning(disable : _SCL_SECURE_NO_WARNINGS)
#pragma warning(disable : _CRT_SECURE_NO_WARNINGS)
#endif
#endif
#ifdef _MSC_VER
#ifdef __INTELLISENSE__
#pragma warning(push)
#pragma warning(disable : _SCL_SECURE_NO_WARNINGS)
#pragma warning(disable : _CRT_SECURE_NO_WARNINGS)
#endif
#endif
#if defined(__INTEL_COMPILER) && (__INTEL_COMPILER <= 1900)
#pragma warning(push)
#pragma warning(disable : all)
#pragma warning(disable : int_must_be_positve_const_in_call_to_memcpy_or_memmove)