TensorFlow uses a **dataflow graph** to represent your computation in terms of the dependencies between individual operations. A graph defines the computation. It doesn't compute anything, it doesn't hold any values, it just defines the operations that you specified in your code.

We define a graph with a variable and three operations: `variable` returns the current value of our variable. `initialize` assigns the initial value of 31 to that variable. `assign` assigns the new value of 12 to that variable.

with<Graph>(tf.Graph().as_default(), graph =>

{

var variable = tf.Variable(31, name: "tree");

tf.global_variables_initializer();

variable.assign(12);

});

TF.NET simulate a `with` syntax to manage the Graph lifecycle which will be disposed when the graph instance is no long need. The graph is also what the sessions in the next chapter use when not manually specifying a graph because use invoked the `as_default()`.

A typical graph is looks like below:


TensorFlow **session** runs parts of the graph across a set of local and remote devices. A session allows to execute graphs or part of graphs. It allocates resources (on one or more machines) for that and holds the actual values of intermediate results and variables.

Let's complete the example in last chapter.

with<Graph>(tf.Graph(), graph =>

{

var variable = tf.Variable(31, name: "tree");

var init = tf.global_variables_initializer();

var sess = tf.Session(graph);

sess.run(init);

var result = sess.run(variable); // 31

var assign = variable.assign(12);

result = sess.run(assign); // 12

});

Constant

Variable

Placeholder

Graph

Session

Operation

Attribute

NameScope

Here are some pre-built TensorFlow binaries you can use for each platform:

- Linux

- CPU-only: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.13.1.tar.gz

- GPU-enabled: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.13.1.tar.gz

- Mac: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.13.1.tar.gz

- Windows: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-1.13.1.zip

pacman -S git patch unzip

}

[TestMethod]

public void Assign1()

{

with<Graph>(tf.Graph().as_default(), graph =>

{

var variable = tf.Variable(31, name: "tree");

var init = tf.global_variables_initializer();

var sess = tf.Session(graph);

sess.run(init);

var result = sess.run(variable);

Assert.IsTrue((int)result == 31);

var assign = variable.assign(12);

result = sess.run(assign);

Assert.IsTrue((int)result == 12);

});

}

[TestMethod]

public void Assign2()

{

var v1 = tf.Variable(10.0f, name: "v1"); //tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);

var inc_v1 = v1.assign(v1 + 1.0f);