TensorFlow 1.*基础

计算图：TODO

Session

Where we launch the graph. A Session object is the part of the TensorFlow API that communicates between Python objects and data on our end, and the actual computational system where memory is allocated for the objects we define, intermediate variables are stored, and finally results are fetched for us. The execution itself is then done with the .run() method of the Session object. When called, this method completes one set of computations in our graph in the following manner: it starts at the requested output(s) and then works backward, computing nodes that must be executed according to the set of dependencies.

Variables:

The optimization process serves to tune the parameters of some given model. For that purpose, TensorFlow uses special objects called Variables.

Placeholders：

Placeholders can be thought of as empty Variables that will be filled with data later on. We use them by first constructing our graph and only when it is executed feeding them with the input data.

MSE：mean squared error

Cross entropy（交叉熵，源于香农信息论）：交叉熵刻画的是实际输出（概率）与期望输出（概率）的距离，也就是交叉熵的值越小，两个概率分布就越接近。假设概率分布p为期望输出，概率分布q为实际输出，H(p,q)为交叉熵，则有

SoftMax：