Given a pattern, stores in *results the set of paths that matches that pattern. *results is cleared.
pattern must match all of a name, not just a substring. pattern: { term } term: '*': matches any sequence of non-'/' characters '?': matches a single non-'/' character '[' [ '^' ] { match-list } ']': matches any single character (not) on the list c: matches character c (c != '*', '?', '\', '[') '\' c: matches character c character-range: c: matches character c (c != '\', '-', ']') '\' c: matches character c lo '-' hi: matches character c for lo <= c <= hi
Typical return codes
OK - no errors
UNIMPLEMENTED - Some underlying functions (like GetChildren) are not implemented The default implementation uses a combination of GetChildren, MatchPath and IsDirectory.
@@ -6,36 +6,7 @@ When a Session is created with a given target, a new Session object is bound to
Example:
```c++ tensorflow::GraphDef graph;
// ... Create or load graph into "graph".
// This example uses the default options which connects
// to a local runtime.
tensorflow::SessionOptions options;
std::unique_ptr<tensorflow::Session>
session(tensorflow::NewSession(options));
// Create the session with this graph.
tensorflow::Status s = session->Create(graph);
if (!s.ok()) { ... }
// Run the graph and fetch the first output of the "output"
// operation, and also run to but do not return anything
// for the "update_state" operation.
std::vector<tensorflow::Tensor> outputs;
s = session->Run({}, {"output:0"}, {"update_state"}, &outputs);
if (!s.ok()) { ... }
// Map the output as a flattened float tensor, and do something
// with it.
auto output_tensor = outputs[0].flat<float>();
if (output_tensor(0) > 0.5) { ... }
// Close the session to release the resources associated with
// this session.
session->Close();
```
{c++} tensorflow::GraphDef graph; // ... Create or load graph into "graph". // This example uses the default options which connects // to a local runtime. tensorflow::SessionOptions options; std::unique_ptr<tensorflow::Session> session(tensorflow::NewSession(options)); // Create the session with this graph. tensorflow::Status s = session->Create(graph); if (!s.ok()) { ... } // Run the graph and fetch the first output of the "output" // operation, and also run to but do not return anything // for the "update_state" operation. std::vector<tensorflow::Tensor> outputs; s = session->Run({}, {"output:0"}, {"update_state"}, &outputs); if (!s.ok()) { ... } // Map the output as a flattened float tensor, and do something // with it. auto output_tensor = outputs[0].flat<float>(); if (output_tensor(0) > 0.5) { ... } // Close the session to release the resources associated with // this session. session->Close();
A Session allows concurrent calls to Run() , though a Session must be created / extended by a single thread.
@@ -12,9 +12,7 @@ Creates a 1-dimensional, 0-element float tensor.
The returned Tensor is not a scalar (shape {}), but is instead an empty one-dimensional Tensor (shape {0}, NumElements() == 0). Since it has no elements, it does not need to be assigned a value and is initialized by default ( IsInitialized() is true). If this is undesirable, consider creating a one-element scalar which does require initialization:
@@ -184,15 +176,7 @@ Use these methods when you know the data type and the number of dimensions of th
Example:
```c++ typedef float T;
Tensor my_mat(...built with Shape{rows: 3, cols: 5}...);
auto mat = my_mat.matrix<T>(); // 2D Eigen::Tensor, 3 x 5.
auto mat = my_mat.tensor<T, 2>(); // 2D Eigen::Tensor, 3 x 5.
auto vec = my_mat.vec<T>(); // CHECK fails as my_mat is 2D.
auto vec = my_mat.tensor<T, 3>(); // CHECK fails as my_mat is 2D.
auto mat = my_mat.matrix<int32>();// CHECK fails as type mismatch.
```
{c++} typedef float T; Tensor my_mat(...built with Shape{rows: 3, cols: 5}...); auto mat = my_mat.matrix<T>(); // 2D Eigen::Tensor, 3 x 5. auto mat = my_mat.tensor<T,2>(); // 2D Eigen::Tensor, 3 x 5. auto vec = my_mat.vec<T>(); // CHECK fails as my_mat is 2D. auto vec = my_mat.tensor<T,3>(); // CHECK fails as my_mat is 2D. auto mat = my_mat.matrix<int32>();// CHECK fails as type mismatch.
@@ -220,22 +204,7 @@ These methods allow you to access the data with the dimensions and sizes of your
Example:
```c++ typedef float T;
Tensor my_ten(...built with Shape{planes: 4, rows: 3, cols: 5}...);
// 1D Eigen::Tensor, size 60:
auto flat = my_ten.flat<T>();
// 2D Eigen::Tensor 12 x 5:
auto inner = my_ten.flat_inner_dims<T>();
// 2D Eigen::Tensor 4 x 15:
auto outer = my_ten.shaped<T, 2>({4, 15});
// CHECK fails, bad num elements:
auto outer = my_ten.shaped<T, 2>({4, 8});
// 3D Eigen::Tensor 6 x 5 x 2:
auto weird = my_ten.shaped<T, 3>({6, 5, 2});
// CHECK fails, type mismatch:
auto bad = my_ten.flat<int32>();
```
{c++} typedef float T; Tensor my_ten(...built with Shape{planes: 4, rows: 3, cols: 5}...); // 1D Eigen::Tensor, size 60: auto flat = my_ten.flat<T>(); // 2D Eigen::Tensor 12 x 5: auto inner = my_ten.flat_inner_dims<T>(); // 2D Eigen::Tensor 4 x 15: auto outer = my_ten.shaped<T,2>({4, 15}); // CHECK fails, bad num elements: auto outer = my_ten.shaped<T,2>({4, 8}); // 3D Eigen::Tensor 6 x 5 x 2: auto weird = my_ten.shaped<T,3>({6, 5, 2}); // CHECK fails, type mismatch: auto bad = my_ten.flat<int32>();
