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tensorflow/c/c_api_experimental.cc
} TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type, void* data, size_t len, TF_Status* status) { auto dtype = static_cast<tensorflow::DataType>(data_type); DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype)); tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
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/math_grad.cc
auto dtype = input->DataType(); if (DataTypeIsFloating(BaseType(dtype)) || DataTypeIsInteger(BaseType(dtype))) { return tensorflow::ops::Identity(ctx, input, output, name); } else if (!DataTypeIsComplex(BaseType(dtype)) && BaseType(dtype) != DT_VARIANT) { return errors::InvalidArgument( "Expected numeric or variant tensor, got dtype ", dtype); }
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) -
tensorflow/c/eager/c_api_test.cc
name_and_attrs.attr().find("dtype")->second.type()); TF_DeleteBuffer(serialized_attr_values); TFE_Op* var_op_2 = TFE_NewOp(ctx, "VarHandleOp", status); string serialized_dtype; ASSERT_TRUE(name_and_attrs.attr().find("dtype")->second.SerializeToString( &serialized_dtype)); TFE_OpSetAttrValueProto( var_op_2, "dtype",
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/eager/tape.h
// least one input of the op is watched and has trainable dtype. // // op_type is used to decide which of the incoming gradients can be left as // nullptr instead of building zeros when build_default_zeros_grads == true. void RecordOperation( const string& op_type, const std::vector<TapeTensor>& output_tensors, absl::Span<const int64_t> input_tensor_id,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Apr 02 12:40:29 GMT 2024 - 47.2K bytes - Viewed (1) -
tensorflow/c/eager/parallel_device/parallel_device_lib_test.cc
TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_OpSetAttrType(handle_op.get(), "dtype", TF_FLOAT); TFE_OpSetAttrShape(handle_op.get(), "shape", /*dims=*/nullptr, /*num_dims=*/0, status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); auto outputs =
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/eager/abstract_tensor_handle.cc
shape_string = "<error computing shape>"; } else { shape_string = shape.DebugString(); } return absl::StrCat("TensorHandle(shape=", shape_string, ", dtype=", DataType_Name(DataType()), ", type=", FullType().DebugString(), ")"); } Status AbstractTensorHandle::TensorHandleStatus() const { // Tensor handles in current runtime don't carry error info and this method
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 1.4K bytes - Viewed (0) -
tensorflow/c/eager/custom_device_test.cc
TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_OpSetAttrType(op.get(), "dtype", TF_FLOAT); TFE_OpSetAttrShape(op.get(), "shape", {}, 0, status.get()); TFE_OpSetAttrString(op.get(), "container", "", 0); TFE_OpSetAttrString(op.get(), "shared_name", "", 0);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 27 23:39:24 GMT 2020 - 18.4K bytes - Viewed (0) -
tensorflow/c/eager/parallel_device/parallel_device_testlib.cc
Variable* Variable::Create(TFE_Context* context, TF_DataType type, const int64_t* dims, const int num_dims, const char* device, TF_Status* status) { std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op( TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op.get(), "dtype", type);
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/custom_device_testutil.cc
auto dtype = TFE_TensorHandleDataType(t->tensor); TFE_CustomDeviceTensorHandleMethods handle_methods; handle_methods.num_dims = &LoggedTensorNumDims; handle_methods.dim = &LoggedTensorDim; handle_methods.deallocator = &LoggedTensorDeallocator; return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(), dtype, t.release(), handle_methods,
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Mar 03 20:47:31 GMT 2021 - 8.3K bytes - Viewed (0) -
tensorflow/c/eager/gradients.h
void Watch(const AbstractTensorHandle*); // Records an operation with given inputs and outputs // on the tape and marks all its outputs as watched if at // least one input of the op is watched and has a trainable dtype. // op_name is optional and is used for debugging only. void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle* const> outputs,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Sep 26 10:27:05 GMT 2022 - 6.9K bytes - Viewed (0)