cs501r_f2017:lab04
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cs501r_f2017:lab04 [2017/09/16 22:01] humphrey [Understand data importation in python and tensorflow] |
cs501r_f2017:lab04 [2021/06/30 23:42] (current) |
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| ===Key concepts:=== | ===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. | 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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| decorator pattern: A decorator function takes a function and its arguments then extend its behavior without changing the function's implementation. The @annotation in python does exactly that. | decorator pattern: A decorator function takes a function and its arguments then extend its behavior without changing the function's implementation. The @annotation in python does exactly that. | ||
| - | @property annotation: While all variables in an object are visible to its users, we might still want to implement getters and setters with special behaviors, for example, bound checking. The @property allows us to do exactly that. | + | @property annotation: While all variables in an object are visible to its users, we might still want to implement getters and setters with special behaviors, for example, bounds checking. The @property allows us to do exactly that. |
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| {{:cs501r_f2017:hint5.1.png?800|}} | {{:cs501r_f2017:hint5.1.png?800|}} | ||
| - | 2. The article by Danijar have shed great insight about this topic. Solution to the problem should become trivial after reading his article. | + | 2. The article by Danijar have shed great insight about this topic. Solutions to the problem should become trivial after reading his article. |
| 3. @functools.wraps(function) can be replaced by @six.wraps(function) for python 2 compatibility after installing and importing the python library "six" in your project environment. | 3. @functools.wraps(function) can be replaced by @six.wraps(function) for python 2 compatibility after installing and importing the python library "six" in your project environment. | ||
cs501r_f2017/lab04.1505599295.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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