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tensorflow/c/eager/gradients.cc
std::vector<int64_t> source_tensor_ids = MakeTensorIDList(sources); tensorflow::gtl::FlatSet<int64_t> sources_set(source_tensor_ids.begin(), source_tensor_ids.end()); std::unordered_map<int64_t, TapeTensor> sources_that_are_targets; for (int i = 0; i < target_tensor_ids.size(); ++i) { int64_t target_id = target_tensor_ids[i];
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/tape.h
} if (result.size() != source_tensor_ids.size()) { return errors::Internal("Expected result Span to be of size ", source_tensor_ids.size(), " found ", result.size(), " in call to Tape::ComputeGradient."); } std::unordered_set<int64_t> used_gradient_ids(source_tensor_ids.size()); for (int i = 0; i < source_tensor_ids.size(); i++) {
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/c_api.cc
const int last_node_id = graph->graph.num_node_ids(); tensorflow::ImportGraphDefResults results; status->status = tensorflow::ImportGraphDef(opts->opts, def, &graph->graph, &graph->refiner, &results); if (!status->status.ok()) return; // Add new nodes to name_map for (int i = last_node_id; i < graph->graph.num_node_ids(); ++i) { auto* node = graph->graph.FindNodeId(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) -
tensorflow/c/eager/parallel_device/parallel_device_lib.cc
std::unique_ptr<ParallelTensor> ParallelDevice::DeviceIDs( TFE_Context* context, TF_Status* status) const { std::vector<int32_t> ids; ids.reserve(num_underlying_devices()); for (int i = 0; i < num_underlying_devices(); ++i) { ids.push_back(i); } return ScalarsFromSequence<int32_t>(ids, context, status); } absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> ParallelDevice::Execute(TFE_Context* context,
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) -
RELEASE.md
* `tf.tpu.experimental.embedding.TPUEmbeddingV2` * Add `compute_sparse_core_stats` for sparse core users to profile the data with this API to get the `max_ids` and `max_unique_ids`. These numbers will be needed to configure the sparse core embedding mid level api. * Remove the `preprocess_features` method since that's no longer needed. ## Thanks to our Contributors
Plain Text - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Mon Apr 29 19:17:57 GMT 2024 - 727.7K bytes - Viewed (8)