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tensorflow/c/c_api_internal.h
struct TF_Library { void* lib_handle; TF_Buffer op_list; }; struct TF_Graph { TF_Graph(); mutable tensorflow::mutex mu; tensorflow::Graph graph TF_GUARDED_BY(mu); // Runs shape inference. tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu); // Maps from name of an operation to the Node* in 'graph'. std::unordered_map<tensorflow::string, tensorflow::Node*> name_map TF_GUARDED_BY(mu);
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tensorflow/c/c_api_experimental.cc
using tensorflow::NodeDef; using tensorflow::OpRegistrationData; using tensorflow::Tensor; using tensorflow::shape_inference::DimensionHandle; using tensorflow::shape_inference::InferenceContext; using tensorflow::shape_inference::ShapeAndType; using tensorflow::shape_inference::ShapeHandle; const int num_inputs = input_shapes->num_items; NodeDef node_def;
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tensorflow/c/c_api_experimental.h
// `shape_inference::InferenceContext` constructor. Note the following: // - The inputs of the `op` are not used for shape inference. So, it is // OK to not have the inputs properly set in `op`. See `input_tensors` // if you want shape inference to consider the input tensors of the // op for shape inference. // - The types need not be set in `input_shapes` as it is not used.
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tensorflow/c/c_api.cc
} namespace { // Helper method that creates a shape handle for a shape described by dims. tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims( tensorflow::shape_inference::InferenceContext* ic, int num_dims, const int64_t* dims) { if (num_dims != -1) { std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec; dim_vec.reserve(num_dims); for (int i = 0; i < num_dims; ++i) {
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tensorflow/c/c_api_test.cc
.Attr("v: " #type) \ .SetShapeFn(tensorflow::shape_inference::UnknownShape); \ REGISTER_OP("CApiAttributesTestOpList" #type) \ .Attr("v: list(" #type ")") \ .SetShapeFn(tensorflow::shape_inference::UnknownShape) ATTR_TEST_REGISTER_OP(string); ATTR_TEST_REGISTER_OP(int); ATTR_TEST_REGISTER_OP(float);
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
### 2. Code Provide code to help us reproduce your issues using one of the following options: #### Option A: Reference colab notebooks 1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
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CITATION.cff
“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
namespace tensorflow { namespace tracing { namespace graph { class GraphContext; class GraphOperation; class GraphTensor; auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim; auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank; // GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index // into the list of outputs for the operation.
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SECURITY.md
inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed. The security impact of skipping those checks should be low, since the attack
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tensorflow/c/experimental/grappler/grappler.h
// If assume_valid_feeds is true, it can help infer shapes in the fanout of fed // nodes. This may cause incorrectness in graph analyses, but is useful for // simulation or scheduling. // If aggressive_shape_inference is true, nodes are executed on the host to // identify output values when possible and does other aggressive strategies. // This may cause incorrectness in graph analyses, but is useful for simulation // or scheduling.
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