Tensorflow is a high performance numerical computation software library, it is mostly known for its strong support for machine learning and deep learning.

### How to Install

If you have ‘pip’, everything is simple`pip install tensorflow`

### Basic Concepts

Graph

Graph is a fundamental concept in Tensorflow. Take ReLU computation as an example, the function of ReLU is

*h=ReLU(Wx+b)*

In the view of Tensorflow, the function looks like this

Nodes

Variables such as *W* and *b* , placeholders such as x, are operations such as *MatMul*, *Add* are all nodes in the graph.

Edges

The edges between nodes indicate the data which flow between nodes, in tensorflow data is represented as “tensor” .

“tensor” + “flow” = “tensorflow”

### Codes

1 | import tensorflow as tf |

Here are the steps described in the above codes.

- Create a graph using Variables and placeholders.
- Start a tensorflow session and deploy the graph into the session.
- Run the session, let the tensors flow.
- Write processing logs using tools such as tf.summary.

Session is the so called execution environment. It needs two parameters which are “Fetches” and “Feeds”.

`sess.run(fetches, feeds)`

Fetches: List of graph nodes

Feeds: Dictionary mapping from graph nodes to concrete values.

In the ReLU example, Fetches = h = tf.nn.relu(tf.matmul(x, W) + b) , Feeds = {x: np.random.random((10, 20))}

It would be interesting to see what happened during the running time. We can try TensorBoard.

## TensorBoard Result

The Main Graph clearly shows how the tensor flows through the graph. In a complex machine learning program, the printed diagram is a convenient tool to increase the confidence of the result.