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tensorflow/c/eager/dlpack.cc
std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape; std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides; shape_arr->resize(ndim); stride_arr->resize(ndim, 1); for (int i = 0; i < ndim; i++) { (*shape_arr)[i] = tensor->dim_size(i); } for (int i = ndim - 2; i >= 0; --i) { (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1]; }
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 12.8K bytes - Viewed (0) -
tensorflow/c/eager/c_api_experimental.h
// Computes the rank of the tensor handle. // // Shapes are specified via callbacks because retrieving the shape of a tensor // is a blocking operation for async eager; custom devices should avoid // retrieving shapes of tensors they wrap until the custom device tensor's // shape is explicitly requested where possible. int (*num_dims)(void* data, TF_Status* status); // Computes the axis length at `dim_index`.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
tensorflow/c/BUILD
], ) cc_library( name = "tf_shape", srcs = ["tf_shape.cc"], hdrs = ["tf_shape.h"], copts = tf_copts(), visibility = ["//visibility:public"], deps = [ ":c_api_macros", ":tf_shape_internal", "//tensorflow/core:framework", ], ) cc_library( name = "tf_shape_internal", hdrs = ["tf_shape_internal.h"], copts = tf_copts(),
Plain Text - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Mar 27 18:00:18 GMT 2024 - 30.3K bytes - Viewed (0) -
tensorflow/c/eager/c_api.cc
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]); for (int i = 0; i < num_dims; ++i) { dims[i] = tensor_shape.dim(i).size(); }
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/gradients.cc
gtl::ArraySlice<AbstractTensorHandle*> output_gradients, absl::Span<AbstractTensorHandle*> result) const override; // Builds a tensor filled with ones with the same shape and dtype as `t`. Status BuildOnesLike(const TapeTensor& t, AbstractTensorHandle** result) const override; // Looks up the ID of a Gradient.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 19.3K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_graph.cc
TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype)); if (!shape.unknown_rank()) { TF_RETURN_IF_ERROR(operation->SetAttrShape( "shape", reinterpret_cast<int64_t*>(shape.dim_sizes().data()), shape.dims())); } int num_outputs = 1; std::vector<AbstractTensorHandle*> outputs(num_outputs); TF_RETURN_IF_ERROR(operation->Execute(
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_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.cc
} TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale, double growth_factor, int bucket_count) { return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() { return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
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/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) -
tensorflow/c/c_api_experimental.cc
ShapeHandle shape_handle = c.output(i); TF_ShapeAndType& shape = output_shapes_result->items[i]; shape.num_dims = c.Rank(shape_handle); if (shape.num_dims == InferenceContext::kUnknownRank) { shape.dims = nullptr; continue; } shape.dims = new int64_t[shape.num_dims]; for (size_t j = 0; j < shape.num_dims; ++j) { shape.dims[j] = c.Value(c.Dim(shape_handle, j));
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)