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--- cs501r_f2017:lab04 [2017/09/16 22:13]
humphrey [Understand OOP in Tensorflow(Extra credit)]
+++ cs501r_f2017:lab04 [2021/06/30 23:42] (current)
@@ Line 26: / Line 26: @@
 ===Key concepts:===
-Computation graphs: A computation graph is essentially an electric circuit, or you can think of it as a dynamical system if you are a math student. Given that, there are three things we want to do with it: first, feed it with some inputs; second, measure the readings of its output nodes; third, trigger some operations on occasions for more control on the graph/circulatory/system.
+Computation graphs: A computation graph is essentially an electric circuit, or you can think of it as a dynamical system if you are a math student. Given that, there are three things we would like to do with it: first, feed it with some inputs; second, measure the readings of its output nodes; third, trigger some operations on occasions for more control on the system.
-Sessions: It provide a framework to send and read signals to/from a graph, and it has very similarly syntax as a file stream.
+Sessions: It provides a framework to send and read signals to or from a graph, and it has very similarly syntax as a file stream.
 Placeholders: They are the input ports of a graph. Each time we run a computation graph with the goal of triggering an operation or measuring a set of nodes, it’s required to send in the request with an input dictionary, specifying what input values are used to generate the outputs.

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