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tensorflow/c/eager/parallel_device/parallel_device_lib_test.cc
TensorHandlePtr three_vector = VectorFloatTensorHandle({5., 6., 7.}, status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); std::vector<TensorHandlePtr> vector_handles; vector_handles.reserve(2); vector_handles.push_back(std::move(two_vector)); vector_handles.push_back(std::move(three_vector)); std::unique_ptr<ParallelTensor> unknown_length_vector =
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jul 08 23:47:35 GMT 2021 - 15.3K bytes - Viewed (0) -
tensorflow/c/c_api_experimental.cc
if (!status->status.ok()) return; // Initialize a input_tensor vector with `nullptr` values. std::vector<const Tensor*> input_tensors_vector(num_inputs, nullptr); // A vector to keep track of newly created `tf::Tensor` objects. std::vector<Tensor> all_input_tensors; // Update the vector with information from `input_tensors` if provided. if (input_tensors != nullptr) {
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 29.4K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/custom_gradient_test.cc
#include "tensorflow/c/tf_status_helper.h" #include "tensorflow/core/platform/errors.h" #include "tensorflow/core/platform/test.h" namespace tensorflow { namespace gradients { namespace internal { namespace { using std::vector; class CustomGradientTest : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> { protected: void SetUp() override { TF_StatusPtr status(TF_NewStatus());
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 4.8K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/grad_test_helper.cc
Model model, Model grad_model, AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs, bool use_function, double abs_error) { auto num_inputs = inputs.size(); std::vector<AbstractTensorHandle*> outputs(num_inputs); auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs), /*use_function=*/use_function); ASSERT_EQ(errors::OK, s.code()) << s.message();
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 5K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_eager.cc
See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include <vector> #include "tensorflow/c/eager/abstract_context.h" #include "tensorflow/c/eager/abstract_tensor_handle.h" #include "tensorflow/c/eager/c_api.h" #include "tensorflow/c/eager/c_api_unified_experimental.h"
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jun 25 04:40:46 GMT 2020 - 3.2K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/math_grad_test.cc
return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0], transpose_a, transpose_b, "MatMul"); }; Model MatMulGradModel = BuildGradModel(MatMulModel, registry_); std::vector<AbstractTensorHandle*> outputs(2); status_ = RunModel(MatMulGradModel, immediate_execution_ctx_.get(), {A.get(), B.get()}, absl::MakeSpan(outputs), UseFunction());
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Thu Apr 13 17:32:14 GMT 2023 - 16.3K bytes - Viewed (0) -
tensorflow/c/eager/c_api_cluster_test.cc
job_def->mutable_tasks()->at(task_index) = tensorflow::strings::StrCat("localhost:", port); } void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle, const std::vector<float>& expected_values) { std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status( TF_NewStatus(), TF_DeleteStatus); TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Apr 14 10:03:59 GMT 2023 - 19.3K bytes - Viewed (0) -
.bazelrc
build:release_arm64_linux --config=linux_arm64 build:release_arm64_linux --crosstool_top="@ml2014_clang_aarch64_config_aarch64//crosstool:toolchain" build:release_arm64_linux --config=mkl_aarch64_threadpool build:release_arm64_linux --copt=-flax-vector-conversions test:release_arm64_linux --flaky_test_attempts=3 # The old gcc linux build options are preserved in the unsupported_*_linux # configs. If your project fails to build with Clang, you can use these
Plain Text - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Thu May 02 19:34:20 GMT 2024 - 52.8K bytes - Viewed (2) -
tensorflow/c/eager/parallel_device/parallel_device_lib.h
Status Shape(const std::vector<int64_t>** shape) const; TF_DataType dtype() const { return dtype_; } // Sets its output argument to a summary of the values of this tensor on every // component device. Status SummarizeValue(std::string& summary); std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); } std::vector<TFE_TensorHandle*> tensors() const {
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Apr 25 15:21:13 GMT 2023 - 12.9K bytes - Viewed (0) -
tensorflow/c/eager/parallel_device/parallel_device_lib.cc
} } absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> ParallelDevice::Join( const std::vector<PartialTensorShape>& expected_output_shapes, TF_Status* status) const { absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result; // Compute per-device per-output tensors std::vector<std::vector<TensorHandlePtr>> per_device_output_tensors;
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Feb 09 07:47:20 GMT 2024 - 25.4K bytes - Viewed (1)