如何在C语⾔中使⽤tensorflow（四）实例
通过TF_SessionRun的⽅式输出字符串内容：
#include<stdlib.h>
#include<stdio.h>
#include<string.h>
#include<tensorflow/c/c_api.h>
int main( int argc, char ** argv )
{
TF_Graph * graph = TF_NewGraph();
TF_SessionOptions * options = TF_NewSessionOptions();
TF_Status * status = TF_NewStatus();
TF_Session * session = TF_NewSession( graph, options, status );
char hello[] = "Hello TensorFlow!";
TF_Tensor * tensor = TF_AllocateTensor( TF_STRING, 0, 0, 8 + TF_StringEncodedSize( strlen( hello ) ) );
TF_Tensor * tensorOutput;
TF_OperationDescription * operationDescription = TF_NewOperation( graph, "Const", "hello" );
TF_Operation * operation;
struct TF_Output output;
TF_StringEncode( hello, strlen( hello ), 8 + ( char * ) TF_TensorData( tensor ), TF_StringEncodedSize( strlen( hello ) ), status );
memset( TF_TensorData( tensor ), 0, 8 );
TF_SetAttrTensor( operationDescription, "value", tensor, status );
TF_SetAttrType( operationDescription, "dtype", TF_TensorType( tensor ) );
operation = TF_FinishOperation( operationDescription, status );
output.oper = operation;
output.index = 0;
TF_SessionRun( session, 0,
0, 0, 0,  // Inputs
&output, &tensorOutput, 1,  // Outputs
c++中string的用法&operation, 1,  // Operations
0, status );
printf( "status code: %i\n", TF_GetCode( status ) );
printf( "%s\n", ( ( char * ) TF_TensorData( tensorOutput ) ) + 9 );
TF_CloseSession( session, status );
TF_DeleteSession( session, status );
TF_DeleteStatus( status );
TF_DeleteSessionOptions( options );
return 0;
}
将代码替换到test.c当中，编译⽣成可执⾏⽂件test。
./test
2020-11-16 19:58:37.850140: I tensorflow/core/platform/cpu_:142] Your CPU supports instructions that this TensorFlow binary was not compiled 2020-11-16 19:58:37.874392: I tensorflow/core/platform/profile_utils/:94] CPU Frequency: 3000000000 Hz
2020-11-16 19:58:37.874637: I tensorflow/compiler/xla/:168] XLA service 0x55902
1926b60 initialized for platform Host (this does not guarantee 2020-11-16 19:58:37.874654: I tensorflow/compiler/xla/:176]  StreamExecutor device (0): Host, Default Version
status code: 0
Hello TensorFlow!
从可执⾏⽂件的输出结果发现，输出包含LOG信息，LOG信息通过宏确定，具体取值及意义如下表所⽰。
0默认值，输出所有信息
1屏蔽通知信息
2屏蔽通知信息和警告信息
3屏蔽通知信息、警告信息和报错信息因此可以根据要求将设置为合理值。
$ export TF_CPP_MIN_LOG_LEVEL='2'
$ ./test
status code: 0
Hello TensorFlow!
参考资料：

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