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# TensorFlow
TensorFlow is a computational dataflow graph library.
## Getting started
### Python API example
The following is an example python code to do a simple matrix multiply
of two constants and get the result from a locally-running TensorFlow
process.
First, bring in the following dependency:
//third_party/tensorflow/core/public:tensorflow_py
to get the python TensorFlow API. If you intend to run TensorFlow within
the same process, link in the following to the same binary:
//third_party/tensorflow/core/public:tensorflow_std_ops
to get the standard set of op implementations. Then:
```python
import tensorflow as tf
with tf.Session("local"):
input1 = tf.Constant(1.0, shape=[1, 1], name="input1")
input2 = tf.Constant(2.0, shape=[1, 1], name="input2")
output = tf.MatMul(input1, input2)
# Run graph and fetch the output
result = output.eval()
print result
```
### C++ API Example
If you are running TensorFlow locally, link your binary with
//third_party/tensorflow/core/public:tensorflow_local
and link in the operation implementations you want to supported, e.g.,
//third_party/tensorflow/core/public:tensorflow_std_ops
An example program to take a GraphDef and run it using TensorFlow
using the C++ Session API:
```c++
#include <memory>
#include <string>
#include <vector>
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/public/tensor.h"
int main(int argc, char** argv) {
// Construct your graph.
tensorflow::GraphDef graph = ...;
// Create a Session running TensorFlow locally in process.
std::unique_ptr<tensorflow::Session> session(tensorflow::NewSession({}));
// Initialize the session with the graph.
tensorflow::Status s = session->Create(graph);
if (!s.ok()) { ... }
// Specify the 'feeds' of your network if needed.
std::vector<std::pair<string, tensorflow::Tensor>> inputs;
// Run the session, asking for the first output of "my_output".
std::vector<tensorflow::Tensor> outputs;
s = session->Run(inputs, {"my_output:0"}, {}, &outputs);
if (!s.ok()) { ... }
// Do something with your outputs
auto output_vector = outputs[0].vec<float>();
if (output_vector(0) > 0.5) { ... }
// Close the session.
session->Close();
return 0;
}
```
For a more fully-featured C++ example, see
`tensorflow/cc/tutorials/example_trainer.cc`
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