key points
To use Tensorflow you need to understand how tensorflow:
- Represents computations as graphs
- Executes graphs in the context of Sessions.
- Represents data as tensors
- Maintains state with Variables.
- Uses feeds and fetches to get data into and out of arbitrary operations.
Overview
TensorFlow is a programming system in which you represent computations as graphs. Nodes in the graph are called ops (short for operations). An op takes zero or more Tensors, performs some computation, and produces zero or more Tensors. In TensorFlow terminology, a Tensor is a typed multi-dimensional array. For example, you can represent a mini-batch of images as a 4-D array of floating point numbers with dimensions [batch, height, width, channels].
A TensorFlow graph is a description of computations. To compute anything, a graph must be launched in a Session. A Session places the graph ops onto Devices, such as CPUs or GPUs, and provides methods to execute them. These methods return tensors produced by ops as numpy ndarray objects in Python, and as tensorflow::Tensor instances in C and C++.