#include <client_session.h>
A ClientSession
object lets the caller drive the evaluation of the TensorFlow graph constructed with the C++ API.
Example:
Scope root = Scope::NewRootScope(); auto a = Placeholder(root, DT_INT32); auto c = Add(root, a, {41}); ClientSession session(root); std::vector outputs; Status s = session.Run({ {a, {1}} }, {c}, &outputs); if (!s.ok()) { ... }
Constructors and Destructors | |
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ClientSession(const Scope & scope, const string & target) Create a new session to evaluate the graph contained in scope by connecting to the TensorFlow runtime specified by target . | |
ClientSession(const Scope & scope) Same as above, but use the empty string ("") as the target specification. | |
ClientSession(const Scope & scope, const SessionOptions & session_options) Create a new session, configuring it with session_options . | |
~ClientSession() |
Public types | |
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FeedType | typedefstd::unordered_map< Output, Input::Initializer, OutputHash > A data type to represent feeds to a Run call.
|
Public functions | |
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Run(const std::vector< Output > & fetch_outputs, std::vector< Tensor > *outputs) const | Evaluate the tensors in fetch_outputs . |
Run(const FeedType & inputs, const std::vector< Output > & fetch_outputs, std::vector< Tensor > *outputs) const | Same as above, but use the mapping in inputs as feeds. |
Run(const FeedType & inputs, const std::vector< Output > & fetch_outputs, const std::vector< Operation > & run_outputs, std::vector< Tensor > *outputs) const | Same as above. Additionally runs the operations ins run_outputs . |
Run(const RunOptions & run_options, const FeedType & inputs, const std::vector< Output > & fetch_outputs, const std::vector< Operation > & run_outputs, std::vector< Tensor > *outputs, RunMetadata *run_metadata) const | Use run_options to turn on performance profiling. |
std::unordered_map< Output, Input::Initializer, OutputHash > FeedType
A data type to represent feeds to a Run call.
This is a map of Output
objects returned by op-constructors to the value to feed them with. See Input::Initializer
for details on what can be used as feed values.
ClientSession( const Scope & scope, const string & target )
Create a new session to evaluate the graph contained in scope
by connecting to the TensorFlow runtime specified by target
.
ClientSession( const Scope & scope )
Same as above, but use the empty string ("") as the target specification.
ClientSession( const Scope & scope, const SessionOptions & session_options )
Create a new session, configuring it with session_options
.
Status Run( const std::vector< Output > & fetch_outputs, std::vector< Tensor > *outputs ) const
Evaluate the tensors in fetch_outputs
.
The values are returned as Tensor
objects in outputs
. The number and order of outputs
will match fetch_outputs
.
Status Run( const FeedType & inputs, const std::vector< Output > & fetch_outputs, std::vector< Tensor > *outputs ) const
Same as above, but use the mapping in inputs
as feeds.
Status Run( const FeedType & inputs, const std::vector< Output > & fetch_outputs, const std::vector< Operation > & run_outputs, std::vector< Tensor > *outputs ) const
Same as above. Additionally runs the operations ins run_outputs
.
Status Run( const RunOptions & run_options, const FeedType & inputs, const std::vector< Output > & fetch_outputs, const std::vector< Operation > & run_outputs, std::vector< Tensor > *outputs, RunMetadata *run_metadata ) const
Use run_options
to turn on performance profiling.
run_metadata
, if not null, is filled in with the profiling results.
~ClientSession()
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/cc/class/tensorflow/client-session.html