Update documentation for 0.11.0rc1

tensorflow/g3doc/api_docs/cc/ClassEnv.md

@@ -90,6 +90,24 @@ Stores in *result the names of the children of the specified directory. The name
 
 Original contents of *results are dropped.
 
+#### `virtual bool tensorflow::Env::MatchPath(const string &path, const string &pattern)=0` {#virtual_bool_tensorflow_Env_MatchPath}
+
+Returns true if the path matches the given pattern. The wildcards allowed in pattern are described below (GetMatchingPaths).
+
+
+
+#### `Status tensorflow::Env::GetMatchingPaths(const string &pattern, std::vector< string > *results)` {#Status_tensorflow_Env_GetMatchingPaths}
+
+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: &apos;*&apos;: matches any sequence of non-&apos;/&apos; characters &apos;?&apos;: matches a single non-&apos;/&apos; character &apos;[&apos; [ &apos;^&apos; ] { match-list } &apos;]&apos;: matches any single character (not) on the list c: matches character c (c != &apos;*&apos;, &apos;?&apos;, &apos;\&apos;, &apos;[&apos;) &apos;\&apos; c: matches character c character-range: c: matches character c (c != &apos;\&apos;, &apos;-&apos;, &apos;]&apos;) &apos;\&apos; c: matches character c lo &apos;-&apos; 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.
+
 #### `Status tensorflow::Env::DeleteFile(const string &fname)` {#Status_tensorflow_Env_DeleteFile}
 
 Deletes the named file.
@@ -110,11 +128,27 @@ PERMISSION_DENIED - dirname or some descendant is not writable
 
 UNIMPLEMENTED - Some underlying functions (like Delete) are not implemented
 
+#### `Status tensorflow::Env::RecursivelyCreateDir(const string &dirname)` {#Status_tensorflow_Env_RecursivelyCreateDir}
+
+Creates the specified directory and all the necessary subdirectories. Typical return codes.
+
+
+
+OK - successfully created the directory and sub directories, even if they were already created.
+
+PERMISSION_DENIED - dirname or some subdirectory is not writable.
+
 #### `Status tensorflow::Env::CreateDir(const string &dirname)` {#Status_tensorflow_Env_CreateDir}
 
-Creates the specified directory.
+Creates the specified directory. Typical return codes.
+
+
+
+OK - successfully created the directory.
 
+ALREADY_EXISTS - directory already exists.
 
+PERMISSION_DENIED - dirname is not writable.
 
 #### `Status tensorflow::Env::DeleteDir(const string &dirname)` {#Status_tensorflow_Env_DeleteDir}
 

tensorflow/g3doc/api_docs/cc/ClassEnvWrapper.md

@@ -42,6 +42,12 @@ Returns the file system schemes registered for this Env .
 
 
 
+#### `bool tensorflow::EnvWrapper::MatchPath(const string &path, const string &pattern) override` {#bool_tensorflow_EnvWrapper_MatchPath}
+
+Returns true if the path matches the given pattern. The wildcards allowed in pattern are described below (GetMatchingPaths).
+
+
+
 #### `uint64 tensorflow::EnvWrapper::NowMicros() override` {#uint64_tensorflow_EnvWrapper_NowMicros}
 
 Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

tensorflow/g3doc/api_docs/cc/ClassRandomAccessFile.md

@@ -18,7 +18,7 @@ A file abstraction for randomly reading the contents of a file.
 
 
 
-#### `virtual Status tensorflow::RandomAccessFile::Read(uint64 offset, size_t n, StringPiece *result, char *scratch) const =0` {#virtual_Status_tensorflow_RandomAccessFile_Read}
+#### `virtual Status tensorflow::RandomAccessFile::Read(uint64 offset, size_t n, StringPiece *result, char *scratch) const  =0` {#virtual_Status_tensorflow_RandomAccessFile_Read}
 
 Reads up to `n` bytes from the file starting at `offset`.
 

tensorflow/g3doc/api_docs/cc/ClassSession.md

@@ -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.
 

tensorflow/g3doc/api_docs/cc/ClassTensor.md

@@ -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:
 
-```c++ Tensor(DT_FLOAT, TensorShape({}))
-
-```
+{c++}   Tensor(DT_FLOAT, TensorShape({}))
 
 #### `tensorflow::Tensor::Tensor(DataType type, const TensorShape &shape)` {#tensorflow_Tensor_Tensor}
 
@@ -100,12 +98,6 @@ Convenience accessor for the tensor shape.
 
 
 
-#### `size_t tensorflow::Tensor::BufferHash() const` {#size_t_tensorflow_Tensor_BufferHash}
-
-
-
-
-
 #### `bool tensorflow::Tensor::IsInitialized() const` {#bool_tensorflow_Tensor_IsInitialized}
 
 If necessary, has this Tensor been initialized?
@@ -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.
 
 #### `TTypes<T>::Matrix tensorflow::Tensor::matrix()` {#TTypes_T_Matrix_tensorflow_Tensor_matrix}
 
@@ -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>();
 
 #### `TTypes<T>::UnalignedFlat tensorflow::Tensor::unaligned_flat()` {#TTypes_T_UnalignedFlat_tensorflow_Tensor_unaligned_flat}
 
@@ -379,7 +348,7 @@ NOTE: The underlying tensor buffer is refcounted, so the lifetime of the content
 
 REQUIRES: `DataTypeCanUseMemcpy(dtype())`.
 
-#### `void tensorflow::Tensor::UnsafeCopyFromInternal(const Tensor &, const TensorShape &)` {#void_tensorflow_Tensor_UnsafeCopyFromInternal}
+#### `void tensorflow::Tensor::UnsafeCopyFromInternal(const Tensor &, DataType dtype, const TensorShape &)` {#void_tensorflow_Tensor_UnsafeCopyFromInternal}
 
 
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Saver.md

@@ -72,7 +72,7 @@ protocol buffer file in the call to `save()`.
 
 - - -
 
-#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False)` {#Saver.__init__}
+#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False, write_version=1)` {#Saver.__init__}
 
 Creates a `Saver`.
 
@@ -140,6 +140,11 @@ checkpoints per device.
 *  <b>`allow_empty`</b>: If `False` (default) raise an error if there are no
     variables in the graph. Otherwise, construct the saver anyway and make
     it a no-op.
+*  <b>`write_version`</b>: controls what format to use when saving checkpoints.  It
+    also affects certain filepath matching logic.  Defaults to V1
+    currently, and will be switched to the more memory-efficient V2 format
+    in the future.  If set to V2, the Saver is still able to restore from
+    old V1 checkpoints.
 
 ##### Raises:
 

tensorflow/g3doc/api_docs/python/state_ops.md

@@ -1522,7 +1522,7 @@ protocol buffer file in the call to `save()`.
 
 - - -
 
-#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False)` {#Saver.__init__}
+#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False, write_version=1)` {#Saver.__init__}
 
 Creates a `Saver`.
 
@@ -1590,6 +1590,11 @@ checkpoints per device.
 *  <b>`allow_empty`</b>: If `False` (default) raise an error if there are no
     variables in the graph. Otherwise, construct the saver anyway and make
     it a no-op.
+*  <b>`write_version`</b>: controls what format to use when saving checkpoints.  It
+    also affects certain filepath matching logic.  Defaults to V1
+    currently, and will be switched to the more memory-efficient V2 format
+    in the future.  If set to V2, the Saver is still able to restore from
+    old V1 checkpoints.
 
 ##### Raises: