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tensorflow/c/eager/abstract_function.h
// representation between various supported formats e.g. FunctionDef and Mlir // function. class AbstractFunction : public core::RefCounted { protected: enum AbstractFunctionKind { kGraph, kMlir }; explicit AbstractFunction(AbstractFunctionKind kind) : kind_(kind) {} public: // Returns which subclass is this instance of.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Mar 04 19:49:06 GMT 2024 - 1.9K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
const int batch_size = 4; const int num_dims = 2; int64_t* dims = new int64_t[num_dims]; int64_t num_elements = 1; dims[0] = batch_size; dims[1] = 1; for (int64_t i = 0; i < num_dims; ++i) { num_elements *= dims[i]; } TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims, sizeof(TF_TString) * num_elements); delete[] dims;
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 96.9K bytes - Viewed (3) -
tensorflow/c/eager/unified_api_testutil.h
// Return a tensor handle with given type, values and dimensions. template <class T, TF_DataType datatype> Status TestTensorHandleWithDims(AbstractContext* ctx, const T* data, const int64_t* dims, int num_dims, AbstractTensorHandle** tensor) { std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status( TF_NewStatus(), TF_DeleteStatus); TFE_Context* eager_ctx =
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tensorflow/c/c_api.cc
int num_shapes) { std::vector<PartialTensorShape> shapes; shapes.reserve(num_shapes); for (int i = 0; i < num_shapes; ++i) { if (num_dims[i] < 0) { shapes.emplace_back(); } else { shapes.emplace_back(ArraySlice<int64_t>( reinterpret_cast<const int64_t*>(dims[i]), num_dims[i])); } }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 102.3K bytes - Viewed (0) -
RELEASE.md
* TF XLA * Add new enum value `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED` to `tf.config.experimental.mlir_bridge_rollout` to enable a \"safe\" mode. This runs the MLIR bridge only when an analysis of the graph only when an analysis of the graph determines that it is safe to run. * Add new enum value `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED'
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
ArraySlice<const bool>(b.get(), num_values)); return absl::OkStatus(); } Status SetAttrShapeList(const char* attr_name, const int64_t** dims, const int* num_dims, int num_values) override { std::vector<PartialTensorShape> shapes; shapes.reserve(num_values); for (int i = 0; i < num_values; ++i) { if (num_dims[i] < 0) { shapes.emplace_back();
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 12 20:00:09 GMT 2024 - 15.4K bytes - Viewed (1) -
tensorflow/c/eager/c_api.cc
return -1; } int num_dims = -1; status->status = tensorflow::unwrap(h)->NumDims(&num_dims); return num_dims; } int64_t TFE_TensorHandleNumElements(TFE_TensorHandle* h, TF_Status* status) { if (h == nullptr) { status->status = tensorflow::errors::InvalidArgument("Invalid handle"); return -1; } int64_t num_elements = -1;
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 12 20:00:09 GMT 2024 - 43.9K bytes - Viewed (2) -
tensorflow/c/eager/c_api_experimental.cc
} TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype, const int64_t* dims, int num_dims, TF_Status* status) { std::vector<int64_t> dimvec(num_dims); for (int i = 0; i < num_dims; ++i) { dimvec[i] = static_cast<int64_t>(dims[i]); } if (ctx == nullptr) {
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Apr 11 23:52:39 GMT 2024 - 35.9K bytes - Viewed (3) -
tensorflow/c/eager/unified_api_testutil.cc
AbstractFunction* func = nullptr; OutputList output_list; output_list.expected_num_outputs = 0; output_list.outputs.reserve(outputs.size()); for (int i = 0; i < model_outputs.size(); i++) { if (model_outputs[i]) { output_list.outputs.emplace_back(model_outputs[i]); output_list.expected_num_outputs += 1; } else { null_indices.insert(i); } }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Feb 27 13:57:45 GMT 2024 - 5.7K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/grad_test_helper.cc
int64_t num_elem_analytical = 1; auto num_dims_analytical = TF_NumDims(analytical_tensor); ASSERT_EQ(dims.size(), num_dims_analytical); for (int j = 0; j < num_dims_analytical; j++) { auto dim_analytical = TF_Dim(analytical_tensor, j); ASSERT_EQ(dims[j], dim_analytical); num_elem_analytical *= dim_analytical; } float* danalytical = new float[num_elem_analytical]{0};
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 5K bytes - Viewed (0)