AI-TensorFlow-Libtensorflow

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lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod  view on Meta::CPAN

      TF_Graph* graph, const TF_Buffer* graph_def,
      const TF_ImportGraphDefOptions* options, TF_Status* status);

=head2 TF_GraphCopyFunction

=over 2

  Adds a copy of function `func` and optionally its gradient function `grad`
  to `g`. Once `func`/`grad` is added to `g`, it can be called by creating
  an operation using the function's name.
  Any changes to `func`/`grad` (including deleting it) done after this method
  returns, won't affect the copy of `func`/`grad` in `g`.
  If `func` or `grad` are already in `g`, TF_GraphCopyFunction has no
  effect on them, but can establish the function->gradient relationship
  between them if `func` does not already have a gradient. If `func` already
  has a gradient different from `grad`, an error is returned.
  
  `func` must not be null.
  If `grad` is null and `func` is not in `g`, `func` is added without a
  gradient.
  If `grad` is null and `func` is in `g`, TF_GraphCopyFunction is a noop.
  `grad` must have appropriate signature as described in the doc of
  GradientDef in tensorflow/core/framework/function.proto.
  
  If successful, status is set to OK and `func` and `grad` are added to `g`.
  Otherwise, status is set to the encountered error and `g` is unmodified.

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern void TF_GraphCopyFunction(TF_Graph* g,
                                                  const TF_Function* func,
                                                  const TF_Function* grad,
                                                  TF_Status* status);

=head2 TF_GraphNumFunctions

=over 2

  Returns the number of TF_Functions registered in `g`.

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern int TF_GraphNumFunctions(TF_Graph* g);

=head2 TF_GraphGetFunctions

=over 2

  Fills in `funcs` with the TF_Function* registered in `g`.
  `funcs` must point to an array of TF_Function* of length at least
  `max_func`. In usual usage, max_func should be set to the result of
  TF_GraphNumFunctions(g). In this case, all the functions registered in
  `g` will be returned. Else, an unspecified subset.
  
  If successful, returns the number of TF_Function* successfully set in
  `funcs` and sets status to OK. The caller takes ownership of
  all the returned TF_Functions. They must be deleted with TF_DeleteFunction.
  On error, returns 0, sets status to the encountered error, and the contents
  of funcs will be undefined.

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern int TF_GraphGetFunctions(TF_Graph* g, TF_Function** funcs,
                                                 int max_func, TF_Status* status);

=head2 TF_OperationToNodeDef

=over 2

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern void TF_OperationToNodeDef(TF_Operation* oper,
                                                   TF_Buffer* output_node_def,
                                                   TF_Status* status);

=head2 TF_NewWhile

=over 2

  Creates a TF_WhileParams for creating a while loop in `g`. `inputs` are
  outputs that already exist in `g` used as initial values for the loop
  variables.
  
  The returned TF_WhileParams will have all fields initialized except
  `cond_output`, `body_outputs`, and `name`. The `body_outputs` buffer will be
  allocated to size `ninputs`. The caller should build `cond_graph` and
  `body_graph` starting from the inputs, and store the final outputs in
  `cond_output` and `body_outputs`.
  
  If `status` is OK, the caller must call either TF_FinishWhile or
  TF_AbortWhile on the returned TF_WhileParams. If `status` isn't OK, the
  returned TF_WhileParams is not valid, and the caller should not call
  TF_FinishWhile() or TF_AbortWhile().
  
  Missing functionality (TODO):
  - Gradients
  - Reference-type inputs
  - Directly referencing external tensors from the cond/body graphs (this is
    possible in the Python API)

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs,
                                                   int ninputs,
                                                   TF_Status* status);

=head2 TF_FinishWhile

=over 2

  Builds the while loop specified by `params` and returns the output tensors of
  the while loop in `outputs`. `outputs` should be allocated to size
  `params.ninputs`.
  
  `params` is no longer valid once this returns.
  

lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod  view on Meta::CPAN

=head2 TF_RegisterLogListener

=over 2

  Register a listener method that processes printed messages.
  
  If any listeners are registered, the print operator will call all listeners
  with the printed messages and immediately return without writing to the
  logs.

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern void TF_RegisterLogListener(
      void (*listener)(const char*));

=head2 TF_RegisterFilesystemPlugin

=over 2

  Register a FileSystem plugin from filename `plugin_filename`.
  
  On success, place OK in status.
  On failure, place an error status in status.

=back

  /* From <tensorflow/c/c_api.h> */
  TF_CAPI_EXPORT extern void TF_RegisterFilesystemPlugin(
      const char* plugin_filename, TF_Status* status);

=head2 TF_NewShape

=over 2

  Return a new, unknown rank shape object. The caller is responsible for
  calling TF_DeleteShape to deallocate and destroy the returned shape.

=back

  /* From <tensorflow/c/tf_shape.h> */
  TF_CAPI_EXPORT extern TF_Shape* TF_NewShape();

=head2 TF_ShapeDims

=over 2

  Returns the rank of `shape`. If `shape` has unknown rank, returns -1.

=back

  /* From <tensorflow/c/tf_shape.h> */
  TF_CAPI_EXPORT extern int TF_ShapeDims(const TF_Shape* shape);

=head2 TF_ShapeDimSize

=over 2

  Returns the `d`th dimension of `shape`. If `shape` has unknown rank,
  invoking this function is undefined behavior. Returns -1 if dimension is
  unknown.

=back

  /* From <tensorflow/c/tf_shape.h> */
  TF_CAPI_EXPORT extern int64_t TF_ShapeDimSize(const TF_Shape* shape, int d);

=head2 TF_DeleteShape

=over 2

  Deletes `shape`.

=back

  /* From <tensorflow/c/tf_shape.h> */
  TF_CAPI_EXPORT extern void TF_DeleteShape(TF_Shape* shape);

=head2 TF_NewTensor

=over 2

  Return a new tensor that holds the bytes data[0,len-1].
  
  The data will be deallocated by a subsequent call to TF_DeleteTensor via:
       (*deallocator)(data, len, deallocator_arg)
  Clients must provide a custom deallocator function so they can pass in
  memory managed by something like numpy.
  
  May return NULL (and invoke the deallocator) if the provided data buffer
  (data, len) is inconsistent with a tensor of the given TF_DataType
  and the shape specified by (dima, num_dims).

=back

  /* From <tensorflow/c/tf_tensor.h> */
  TF_CAPI_EXPORT extern TF_Tensor* TF_NewTensor(
      TF_DataType, const int64_t* dims, int num_dims, void* data, size_t len,
      void (*deallocator)(void* data, size_t len, void* arg),
      void* deallocator_arg);

=head2 TF_AllocateTensor

=over 2

  Allocate and return a new Tensor.
  
  This function is an alternative to TF_NewTensor and should be used when
  memory is allocated to pass the Tensor to the C API. The allocated memory
  satisfies TensorFlow's memory alignment preferences and should be preferred
  over calling malloc and free.
  
  The caller must set the Tensor values by writing them to the pointer returned
  by TF_TensorData with length TF_TensorByteSize.

=back

  /* From <tensorflow/c/tf_tensor.h> */
  TF_CAPI_EXPORT extern TF_Tensor* TF_AllocateTensor(TF_DataType,
                                                     const int64_t* dims,

lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod  view on Meta::CPAN

=over 2

  Returns the name of the OpKernel.
  
  The returned TF_StringView's underlying string is owned by the OpKernel and
  has the same lifetime as the OpKernel.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern TF_StringView TF_GetOpKernelName(TF_OpKernelContext* ctx);

=head2 TF_GetResourceMgrDefaultContainerName

=over 2

  Returns the default container of the resource manager in OpKernelContext.
  
  The returned TF_StringView's underlying string is owned by the OpKernel and
  has the same lifetime as the OpKernel.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern TF_StringView TF_GetResourceMgrDefaultContainerName(
      TF_OpKernelContext* ctx);

=head2 TF_GetOpKernelRequestedInput

=over 2

  Returns the name of the requested input at `index` from the OpKernel.
  
  The returned TF_StringView's underlying string is owned by the OpKernel and
  has the same lifetime as the OpKernel.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern TF_StringView TF_GetOpKernelRequestedInput(
      TF_OpKernelContext* ctx, size_t index);

=head2 TF_OpKernelConstruction_GetAttrSize

=over 2

  Get the list_size and total_size of the attribute `attr_name` of `oper`.
  list_size - the length of the list.
  total_size - total size of the list.
    (1) If attr_type == TF_ATTR_STRING
        then total_size is the cumulative byte size
        of all the strings in the list.
    (3) If attr_type == TF_ATTR_SHAPE
        then total_size is the number of dimensions
        of the shape valued attribute, or -1
        if its rank is unknown.
    (4) If attr_type == TF_ATTR_SHAPE
        then total_size is the cumulative number
        of dimensions of all shapes in the list.
    (5) Otherwise, total_size is undefined.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern void TF_OpKernelConstruction_GetAttrSize(
      TF_OpKernelConstruction* ctx, const char* attr_name, int32_t* list_size,
      int32_t* total_size, TF_Status* status);

=head2 TF_OpKernelConstruction_GetAttrType

=over 2

  Interprets the named kernel construction attribute as a TF_DataType and
  places it into *val. *status is set to TF_OK.
  
  If the attribute could not be found or could not be interpreted as
  TF_DataType, *status is populated with an error.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern void TF_OpKernelConstruction_GetAttrType(
      TF_OpKernelConstruction* ctx, const char* attr_name, TF_DataType* val,
      TF_Status* status);

=head2 TF_OpKernelConstruction_GetAttrInt32

=over 2

  Interprets the named kernel construction attribute as int32_t and
  places it into *val. *status is set to TF_OK.
  
  If the attribute could not be found or could not be interpreted as
  int32, *status is populated with an error.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern void TF_OpKernelConstruction_GetAttrInt32(
      TF_OpKernelConstruction* ctx, const char* attr_name, int32_t* val,
      TF_Status* status);

=head2 TF_OpKernelConstruction_GetAttrInt64

=over 2

  Interprets the named kernel construction attribute as int64_t and
  places it into *val. *status is set to TF_OK.
  
  If the attribute could not be found or could not be interpreted as
  int64, *status is populated with an error.

=back

  /* From <tensorflow/c/kernels.h> */
  TF_CAPI_EXPORT extern void TF_OpKernelConstruction_GetAttrInt64(
      TF_OpKernelConstruction* ctx, const char* attr_name, int64_t* val,
      TF_Status* status);

=head2 TF_OpKernelConstruction_GetAttrFloat

lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod  view on Meta::CPAN

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern void TF_DeleteAttrBuilder(TF_AttrBuilder* builder);

=head2 TF_AttrBuilderSetType

=over 2

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern void TF_AttrBuilderSetType(TF_AttrBuilder* builder,
                                                   const char* attr_name,
                                                   TF_DataType value);

=head2 TF_AttrBuilderSetTypeList

=over 2

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder,
                                                       const char* attr_name,
                                                       const TF_DataType* values,
                                                       int num_values);

=head2 TF_AttrBuilderCheckCanRunOnDevice

=over 2

  Checks the tensorflow::NodeDef built via the methods above to see if it can
  run on device_type.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern void TF_AttrBuilderCheckCanRunOnDevice(
      TF_AttrBuilder* builder, const char* device_type, TF_Status* status);

=head2 TF_GetNumberAttrForOpListInput

=over 2

  For argument number input_index, fetch the corresponding number_attr that
  needs to be updated with the argument length of the input list.
  Returns nullptr if there is any problem like op_name is not found, or the
  argument does not support this attribute type.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern const char* TF_GetNumberAttrForOpListInput(
      const char* op_name, int input_index, TF_Status* status);

=head2 TF_OpIsStateful

=over 2

  Returns 1 if the op is stateful, 0 otherwise. The return value is undefined
  if the status is not ok.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern int TF_OpIsStateful(const char* op_type,
                                            TF_Status* status);

=head2 TF_InitMain

=over 2

  Platform specific initialization routine. Very few platforms actually require
  this to be called.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT void TF_InitMain(const char* usage, int* argc, char*** argv);

=head2 TF_PickUnusedPortOrDie

=over 2

  Platform-specific implementation to return an unused port. (This should used
  in tests only.)

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT int TF_PickUnusedPortOrDie(void);

=head2 TFE_NewTensorHandleFromScalar

=over 2

  Fast path method that makes constructing a single scalar tensor require less
  overhead and copies.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromScalar(
      TF_DataType data_type, void* data, size_t len, TF_Status* status);

=head2 TFE_EnableCollectiveOps

=over 2

  Specify the server_def that enables collective ops.
  This is different to the above function in that it doesn't create remote
  contexts, and remotely executing ops is not possible. It just enables
  communication for collective ops.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,
                                                     const void* proto,
                                                     size_t proto_len,