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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`