def randInitializeWeights(L_in, L_out): #初始化权重矩阵 """ randomly initializes the weights of a layer with L_in incoming connections and L_out outgoing connections. """ epi = (6**1/2) / (L_in + L_out)**1/2 W = np.random.rand(L_out,L_in +1) *(2*epi) -epi return W

时间: 2023-06-18 10:06:47 浏览: 160
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Python库 | pytest_lazy_fixture-0.5.1-py3-none-any.whl

这段代码实现了一个权重矩阵的随机初始化函数，其中L_in表示输入层的神经元数量，L_out表示输出层的神经元数量。初始化权重矩阵可以使神经网络更好地学习输入数据的特征。 具体来说，该函数首先计算了一个被称为"epsilon"的值，它是一个常量，用于控制权重矩阵中元素的范围。然后，使用Numpy库的random.rand函数生成一个大小为(L_out, L_in+1)的随机矩阵，每个元素的值都在[-epsilon, epsilon]之间。最后返回该权重矩阵W。值得注意的是，由于偏置项也需要被训练，因此在输入层上增加了一个偏置单元，因此矩阵的列数为L_in+1。
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def monty_choice(car, contestant): # this method simulates Monty opening an door behind which there is a goat # Parameters # car, which is either 1, 2 or 3, represents the door behind which is a car # contestant, which is either 1, 2 or 3, represents the door that contestant chooses # Returns # an integer, which is 1,2 or 3, represents the door that host chooses to open # write your code below this line

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x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.1, random_state = 999) # Reshape image in 3 dimensions (height = 100, width = 125 , canal = 3) x_train = x_train.reshape(x_train.shape[0], *(100, 125, 3)) x_test = x_test.reshape(x_test.shape[0], *(100, 125, 3)) x_validate = x_validate.reshape(x_validate.shape[0], *(100, 125, 3)) # With data augmentation to prevent overfitting datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180) zoom_range = 0.1, # Randomly zoom image width_shift_range=0.12, # randomly shift images horizontally (fraction of total width) height_shift_range=0.12, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=True) # randomly flip images datagen.fit(x_train)代码段一开始没有问题，但是多次执行后出现报错MemoryError: Unable to allocate 1.52 GiB for an array with shape (5423, 100, 125, 3) and data type float64

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I am an AI language model and cannot create images directly. However, I can describe the structure of the DeepNeuralNet class in a text format, which you can use as a guide to drawing the network structure. The structure looks like this: 1. Input Layer: This is where the network receives user and item inputs. Each input goes through an embedding layer, with n_users and n_items as the number of embeddings, and n_factors as the size of the embeddings. 2. Concatenation Layer: The output of the user and item embedding layers is concatenated, resulting in a tensor of shape (batch_size, n_factors*2). 3. Fully Connected Hidden Layers: The concatenated tensor is then passed through a series of fully connected layers. In your case, you have two hidden layers of sizes 64 and 32. Each layer is defined as a Linear layer with a specified number of input and output features, and these layers are stored in a ModuleList (fc_layers). 4. Dropout Layer: After passing through the hidden layers, the network goes through a dropout layer with probability 0.2. This randomly sets some elements to zero during training to prevent overfitting. 5. Output Layer: After the dropout layer, the network passes through another Linear layer, which reduces the tensor's dimension to 1. 6. Sigmoid Activation: Finally, the output goes through a sigmoid activation function, which squashes the output value between 0 and 1. The sigmoid activation is applied to make the output ready for predicting ratings or binary outcomes such as preferences. To draw the structure, you can use rectangles to represent the Linear layers and circles for activation functions. Label the rectangles with the number of input and output features, and label the circles with the activation function's name. Connect the rectangles with lines to visualize the information flow.用图展示这个网络层·

抱歉，我是一名语言模型，无法直接创建图像。不过，我可以为您提供一个文字版的 DeepNeuralNet 类的结构描述，您可以根据这个描述来绘制网络结构图。结构如下： ...使用线条连接矩形以可视化信息流。

We have seen the cars2018.csv data in the 'Example: Fuel Consumption' Lecture Task 1: Install {keras} (and Tenserflow) on your computer. Make sure its linkage with Python libary is also established. (It is one of the key steps for this assignment. It may take some time if you have not get the right library installed. Here is a reference link. https://keras.rstudio.com/reference/install_keras.html) Task 2: Divide the data randomly into 80% training and 20% test, fit a neural network model on fuel consumption (mpg) with one hidden dense layer. Choose the number of hidden units, and select the right activation function and modelling parameters. Task 3: Fit another neural network with 2 hidden dense layers, i.e. stack another hidden layer on top of the pervious neural net. Choose the number of the hidden units for the second layer. Compare the results with output from Task 2. Task 4: Compare the model performance with the model discussed in the lecture, i.e. the lasso, rpart, random forest, and xgboost models. Task 5: Change the response variable from mpg to Liters/KM (i.e. 235.15/mpg), and re-fit a neural network model. Compare performance as in Task 4.请你提供这个题目的task4的代码

# Task 2 - Fit a neural network model with one hidden layer set.seed(123) train_index (cars2018$mpg, p = 0.8, list = FALSE) train_data [train_index, ] test_data [-train_index, ] model1 <- keras_model...

在手写KMeans # 构建K-Means++类 class K_Means_plus(): def __init__(self,k): self.k = k self.max_iter = max_iter s基础上，补充它的参数，使X,Y = make_moons(n_samples=400,shuffle=True,noise=0.1,random_state=136)数据集的准确率高于百分之九十，这个数据集的标签为0或1.写出代码

好的，我理解了你的要求。...2. fit()：实现KMeans++聚类算法的核心方法，包括初始化质心、分配簇和更新质心等步骤。 3. predict()：根据训练好的模型，预测新样本的类别。 4. 使用KMeans函数计算准确率。

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Sure, here is a possible implementation of the kmeans_color function for image segmentation using the RGB color information: python import numpy as np def kmeans_color(features, k, num_iters=...

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random_door to randomly select a door for the car or the contestant, monty_choice to choose the door Monty opens based on the contestant's choice and the location of the car, and win_car to ...

用到以下类实现The division of responsibility between the different classes is as follows. - Die: Responsible for handling randomly generated integer values between 1 and 6. - DiceCup: Handles five objects (dice) of class Die. Has the ability to bank and release dice individually. Can also roll dice that are not banked. - ShipOfFoolsGame: Responsible for the game logic and has the ability to play a round of the game resulting in a score. Also has a property that tells what accumulated score results in a winning state, for example 21. - Player: Responsible for the score of the individual player. Has the ability, given a game logic, play a round of a game. The gained score is accumulated in the attribute score. - PlayRoom: Responsible for handling a number of players and a game. Every round the room lets each player play, and afterwards check if any player has reached the winning score. 3. Implementation

Sure, here's a possible implementation of the game using the classes described: python import random class Die: """Class representing a 6-sided die.""" def __init__(self): self.value = None ...

%% % For more details: % A. Elkelesh, M. Ebada, S. Cammerer, L. Schmalen and S. ten Brink, "Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design," in IEEE Access, 2019. % DOI: 10.1109/ACCESS.2019.2942999 % IEEE URL: https://ieeexplore.ieee.org/document/8846017 % arXiv URL: https://arxiv.org/abs/1903.03128 % elkelesh@inue.uni-stuttgart.de %% % See Fig. 2 in the paper clear all; close all; clc; rng('shuffle'); parpool(24); tic; N = 128; % number of VNs m = 64; % number of CNs R = 0.5; % code rate name = 'population1.mat'; load(name); % To get started, our initial population (i.e., population 1) contains a set of randomly constructed regular (3,6) LDPC codes pop_index=1; while true %%%%% Population N_pop tic; pop_index = pop_index + 1; name = ['population' num2str(pop_index) '.mat']; all_Hs = population_update(all_Hs,BLERs,R); S=size(all_Hs,1); save('H_matrices.mat'); BLERs = nan(1,S); for H_count = 1:S BLERs(H_count) = compute_BLER( squeeze(all_Hs(H_count,:,:)) , R ); save('status_BLER_Done.mat','H_count'); end timeNeeded = toc; save(name); end

这段代码实现了论文 "Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design" 中的基因优化算法来设计低密度奇偶校验码。代码中的注释提供了论文和相关的细节信息。下面是这段代码的主要步骤： 1. ...

根据以下要求编写一个python程序1. Description Ship of Fools is a simple classic dice game. It is played with five standard 6-faced dice by two or more players. - The goal of the game is to gather a 6, a 5 and a 4 (ship, captain and crew) in the mentioned order. - The sum of the two remaining dice (cargo) is preferred as high as possible. The player with the highest cargo score wins the round. Example: - In the first round turn, if a player rolls 6 4 3 3 1 (note we five dice at the beginning), the player can bank the 6 (ship), but the rest needs to be re-rolled since there is no 5. - In the second round turn, if the player rolls 6 5 4 4 (four dice, since the 6 from last turn is banked), the player can bank the 5 (captain) and the 4 (crew). The player has three choices for the remaining 6 and 4. The player can bank both and score 10 points, or re-roll one or two of the dice and hope for a higher score. - In the second round turn, if the player instead rolled 4 4 3 1, all dice needs to be re-rolled since there is no 5.程序需要包含一下几个类.The division of responsibility between the different classes is as follows. - Die: Responsible for handling randomly generated integer values between 1 and 6. - DiceCup: Handles five objects (dice) of class Die. Has the ability to bank and release dice individually. Can also roll dice that are not banked. - ShipOfFoolsGame: Responsible for the game logic and has the ability to play a round of the game resulting in a score. Also has a property that tells what accumulated score results in a winning state, for example 21. - Player: Responsible for the score of the individual player. Has the ability, given a game logic, play a round of a game. The gained score is accumulated in the attribute score. - PlayRoom: Responsible for handling a number of players and a game. Every round the room lets each player play, and afterwards check if any player has reached the winning score.

def __init__(self): self.value = random.randint(1, 6) def roll(self): self.value = random.randint(1, 6) class DiceCup: def __init__(self): self.dice = [Die() for i in range(5)] self.banked =...

Write pytorch-based Python code to implement a neural network that solves binary classification problem with an output layer of a positively weighted sub-network plus a negatively weighted sub-network，where the negative weight is random and take the opposite of the absolute value of the standard normal distribution, the exact value of the two weights are obtained from the objective function by Back propagation. The activation function is set to tanh.

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下面这个代码报错了，应该怎么改： %%Matlab Genetic Algorithm for Sin Prediction clear; clc; %population size Npop=50; %create the population Pop=rand(Npop,1)*2*pi; %define fitness fit=@(x) sin(x); %fitness score score=fit(Pop); %maximum number of generations maxgen=100; %weights w=0.7; %probability p_crossover=0.9; p_mutation=0.2; %loop for number of generations for gen=1:maxgen %ranking %rank the population in descending order [~,rank]=sort(score); %rank the population in ascending order rank=flipud(rank); %normalised rank NormalisedRank=rank/sum(rank); %selection %cumulative sum of the normalised rank cumulativeSum=cumsum(NormalisedRank); %randomly select the two parents %from the populations based on their %normalised rank randnum=rand; parent1=find(cumulativeSum>randnum,1); randnum=rand; parent2=find(cumulativeSum>randnum,1); %crossover %randomly select the crossover point pc=randi([1 Npop-1]); %create the offsprings offspring1=[Pop(parent1,1:pc) Pop(parent2,pc+1:end)]; offspring2=[Pop(parent2,1:pc) Pop(parent1,pc+1:end)]; %perform crossover with a probability if(rand<p_crossover) Pop=[Pop; offspring1; offspring2]; end %mutation %randomly select the point of mutation pm=randi([1 Npop]); %mutate the value under the chosen point Pop(pm)=rand*2*pi; %perform mutation with a probability if (rand<p_mutation) Pop(pm)=rand*2*pi; end %evaluate new population score=fit(Pop); %elitism %sort the population in ascending order %of their fitness score [score,rank]=sort(score); elite=Pop(rank(1),:); Pop(rank(Npop),:)=elite; %replace old population Pop=Pop(1:Npop,:); end %print the best solution disp('Best Solution: '); disp(elite);

I'm sorry, I don't understand your question. Can you please provide more information or clarify what you're asking?

The x and y instance variables indicate the position of the center of the shape, and the color instance variable indicates the color of the shape. The color of the shape is computed randomly in the constructor of the shape class and never changes after that, like this: color = new Color((float)Math.random(), (float)Math.random(), (float)Math.random()); The isVisible method is abstract, and indicates whether the shape is currently visible or not inside a window of width w and of height h. The isIn method is abstract, and indicates whether the point at coordinates (x, y) is currently inside the shape or not. The draw method simply changes the color of the graphics object g to be the correct color for the shape. Also add to your program a Start class to test your Shape class.帮我写出以上Java代码

注意，上面的代码中 Shape 是一个抽象类，不能被直接实例化。你需要创建特定的子类来实现 isVisible 和 isIn 方法。例如，下面是一个可能的 Circle 类的实现： public class Circle extends Shape { private ...

Create the Producer class. It must implement the Runnable interface. Write a constructor that initializes all four instance variables sizeOfJobs,numberOfJobs,delayBetweenJobs,producerName. The run method must generate a certain number of print jobs, based on the numberOfJobs attribute. Give each of these jobs a name based on the producerName and the number of the job. The number of pages in the print job is specified by the sizeOfJobs attribute. Add the print job to the printer manager via the addJob method. This method might throw an FullQueueException, write code to handle this occurrance. Print out a message that the producer is adding a job to the print queue. Between print jobs the producer must sleep based on the delayBetweenJobs attribute

The number of pages in each job is randomly generated between 1 and sizeOfJobs. The job is added to the printer manager via the addJob method, which might throw a FullQueueException. If this ...
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