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Results 21 - 29 of 29 for shape (0.21 sec)
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tensorflow/c/eager/c_api_debug.cc
std::vector<int64_t> shape; int rank = -1; *status = handle.NumDims(&rank); if (!status->ok()) { return shape; } shape.reserve(rank); for (int i = 0; i < rank; ++i) { int64_t dim; *status = handle.Dim(i, &dim); if (!status->ok()) { return shape; } shape.push_back(dim); } return shape; } } // namespace extern "C" {
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Aug 11 01:20:50 GMT 2021 - 2.5K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
EXPECT_TRUE( std::equal(std::begin(partial_shape), std::end(partial_shape), values)); } TEST_F(CApiAttributesTest, ShapeList) { const int64_t shape_1[] = {1, 3}; const int64_t shape_2[] = {2, 4, 6}; const int64_t* list[] = {&shape_1[0], &shape_2[0]}; const size_t list_size = TF_ARRAYSIZE(list); const int ndims[] = {TF_ARRAYSIZE(shape_1), TF_ARRAYSIZE(shape_2)};
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/parallel_device/parallel_device_testlib.cc
TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op.get(), "dtype", type); TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status); TFE_OpSetAttrString(op.get(), "container", "", 0); // Use the special GUID for no buffer sharing // // TODO(allenl): Should we provide a better API for this? AFAIK this is the
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Jun 15 15:44:44 GMT 2021 - 12.5K bytes - Viewed (0) -
tensorflow/c/eager/c_api.cc
break; case tensorflow::AttrValue::kShape: { const auto& tensor_shape = default_value.shape(); if (tensor_shape.unknown_rank()) { TFE_OpSetAttrShape(op, attr_name, nullptr, -1, status); } else { const auto num_dims = tensor_shape.dim_size(); std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);
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/c_api_function_test.cc
TF_DeleteStatus); TF_Tensor* tensor_shape = Int32Tensor({37, 1}); TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape"); TF_Operation* random = RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get()); TF_Output outputs[] = {{random, 0}}; *func = TF_GraphToFunction(func_graph.get(), name,
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jul 20 22:08:54 GMT 2023 - 63.6K bytes - Viewed (6) -
tensorflow/c/eager/c_api_test_util.cc
// Create the variable handle. TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op, "dtype", TF_FLOAT); TFE_OpSetAttrShape(op, "shape", {}, 0, status); TFE_OpSetAttrString(op, "container", "localhost", 0); TFE_OpSetAttrString(op, "shared_name", "", 0); if (!device_name.empty()) { TFE_OpSetDevice(op, device_name.c_str(), status); }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 23.5K bytes - Viewed (2) -
tensorflow/c/eager/c_api_experimental_test.cc
ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_Op* shape_op = ShapeOp(ctx, hgpu); TFE_OpSetDevice(shape_op, gpu_device_name.c_str(), status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_TensorHandle* retvals[1]; int num_retvals = 1; TFE_Execute(shape_op, &retvals[0], &num_retvals, status.get());
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 03 03:14:26 GMT 2023 - 31.5K bytes - Viewed (1) -
tensorflow/c/eager/c_api_test.cc
ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_Op* shape_op = ShapeOp(ctx, hgpu); TFE_OpSetDevice(shape_op, gpu_device_name.c_str(), status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_TensorHandle* retvals[1]; int num_retvals = 1; TFE_Execute(shape_op, &retvals[0], &num_retvals, status.get());
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 03 20:50:20 GMT 2023 - 94.6K bytes - Viewed (1) -
tensorflow/c/experimental/gradients/math_grad.cc
} Status Compute(AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) override { // TODO(vnvo2409): Add shape broadcasting /* Given upstream grad U and a Div op: Z = X/Y, the gradients are: * * dX = U / Y * dY = -U*X / Y^2 = (X/Y) * -U / Y = -U*Z / Y * */
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 15.2K bytes - Viewed (0)