## 计算机代写|深度学习代写deep learning代考|FIT3181

2022年12月27日

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## 计算机代写|深度学习代写deep learning代考|Automating HPO with Random Search

We just looked at manual HPO of DL using a function approximation problem. During this scenario we provided a set of tools by which the practitioners could run the notebook consecutively using different hyperparameters to generate comparisons. As you likely discovered from working through that exercise manual HPO is time consuming and just downright boring.

Of course, there are now numerous tools for performing HPO automatically. These tools range from Python packaggés too full systêms incorpóratẻa intoo clouud téchnólogiēs as part of an AutọML solution. We could certainly use any of those tools to perform a baseline comparison against EC methods but for our purposes we want to understand the automation and search process deeper.

Random search HPO, as its name implies, is the process of sampling random values from a known set of hyperparameters within given ranges and then evaluating the effectiveness. The hope in random search is that you will eventually find the best or desired solution. An example of this is someone throwing darts blindfolded hoping to hit a bullseye. The blindfolded person likely won’t hit a bullseye in a couple throws but over many throws we may expect they might.

Notebook EDL_5_2_RS_HPO.ipynb is an upgraded version of our previous notebook that automates HPO using a simple random search algorithm. Open that notebook in Colab and run all the cells before jumping into the exercise.

1. Open the notebook EDL_5_2_RS_HPo.ipynb in Colab and run all the cells. Runtime $\rightarrow$ Run all from the menu. As a before comparison feel free to open EDL_5_1_Hyperparameter_Tuning.ipynb notebook.
2. We will start by exploring the problem function that we want our DL network to approximate. The first cell of code revisits our polynomial function from chapter 4 as shown in figure 5.1. Below is the code which generates the sample set of input and target data points we will use to train the network on.

## 计算机代写|深度学习代写deep learning代考|Using Grid Search for Automatic HPO

In our next exercise we are going to upgrade our earlier random search attempt to use a more sophisticated grid search technique. While this technique is more robust and efficient it is still bounded by the size of the grid. Using larger grid cells will often limit results to local minimums or maximums. Finer and smaller cells can locate global min and maximums but at the cost of increased search space.

The code in the next exercise notebook EDL_5_3_GS_HPo.ipynb is derived from our earlier random search example. As such much of the code is the same and as usual, we will focus on just the parts that make this sample unique.

1. Open the EDL_5_3_GS_HPO.ipynb in Colab and run all the cells, Runtime $\boldsymbol{>}$ Run all.
2. The primary difference in the code for this example is the hyperparameter object now needs to track a parameter grid. We will first look at the construction of a new class HyperparametersGrid class and the init function. Inside this function we extract the names of the input parameters into self. hparms and then test if the first input points to a generator. If it does then we generate a parameter grid with self.create_grid otherwise the instance will just be a child HP container.

Next we will take a look at how the parameter grid is constructed in the self.create_grid function. The function starts by creating an empty grid dictionary and then loops through the list of hyperparameters. It calls the hyperparameter generator using next to return in this case a value and the total number of values. Then we loop again through the generator to extract each unique value and append it to a row list. After which we append the row to the grid and then finish by injecting the grid into the Parametergrid class. ParameterGrid is a helper class from skLearn that takes as input a dictionary of inputs and list of values and then constructs a grid where each cell represents the various hyperparameter combinations. While we are only running this example with 2 hyperparameters over a 2-dimensional grid, Parametergrid can manage any number of dimensions.

## 计算机代写|深度学习代写deep learning代考|Automating HPO with Random Search

1. 在 Colab 中打开笔记本 EDL_5_2_RS_HPo.ipynb 并运行所有单元格。运行→从菜单运行所有。作为之前的比较，请随意打开 EDL_5_1_Hyperparameter_Tuning.ipynb 笔记本。
2. 我们将从探索我们希望 DL 网络近似的问题函数开始。代码的第一个单元格重新访问了第 4 章中的多项式函数，如图 5.1 所示。下面是生成我们将用来训练网络的输入和目标数据点样本集的代码。

## 计算机代写|深度学习代写deep learning代考|Using Grid Search for Automatic HPO

1. 在 Colab 中打开 EDL_5_3_GS_HPO.ipynb 并运行所有单元格，Runtime>全部运行。
2. 此示例代码的主要区别是超参数对象现在需要跟踪参数网格。我们将首先看一下新类HyperparametersGrid 类的构造和init 函数。在这个函数中，我们将输入参数的名称提取到 self. hparms 然后测试第一个输入是否指向生成器。如果是，那么我们使用 self.create_grid 生成一个参数网格，否则该实例将只是一个子 HP 容器。

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## MATLAB代写

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