% 加载数据 load('data.mat'); X_train = train_data; % 训练数据 Y_train = train_labels; % 训练标签 X_test = test_data; % 测试数据 Y_test = test_labels; % 测试标签 % 构建神经网络 layers = [ imageInputLayer([28 28 1]) convolution2dLayer(3, 32, 'Padding', 'same') batchNormalizationLayer reluLayer maxPooling2dLayer(2, 'Stride', 2) convolution2dLayer(3, 64, 'Padding', 'same') batchNormalizationLayer reluLayer maxPooling2dLayer(2, 'Stride', 2) convolution2dLayer(3, 128, 'Padding', 'same') batchNormalizationLayer reluLayer fullyConnectedLayer(10) softmaxLayer classificationLayer ]; % 设置训练选项 options = trainingOptions('adam', ... 'MaxEpochs', 10, ... 'MiniBatchSize', 128, ... 'ValidationData', {X_test, Y_test}, ... 'Plots', 'training-progress'); % 训练网络 net = trainNetwork(X_train, Y_train, layers, options); % 测试网络 YPred = classify(net, X_test); % 计算准确率 accuracy = sum(YPred == Y_test) / numel(Y_test); fprintf('Accuracy: %.2f%%\n', accuracy * 100); 这段代码不能运行，如何解决？

在这段代码中，您需要确保已经定义 train_data、train_labels、test_data 和 test_labels。如果您没有定义这些变量，那么您需要将您的数据加载进来，例如：

load('mnist.mat');
X_train = mnist.train_images;
Y_train = categorical(mnist.train_labels);
X_test = mnist.test_images;
Y_test = categorical(mnist.test_labels);

另外，您需要确保您的 MATLAB 版本支持深度学习工具箱。如果您的版本不支持，您需要更新 MATLAB 或者安装深度学习工具箱。

相关问题

function untitled() load('D:\mat格式的MNIST数据\test_labels.mat') load('D:\mat格式的MNIST数据\train_images.mat') load('D:\mat格式的MNIST数据\train_labels.mat') load('D:\mat格式的MNIST数据\test_images.mat') train_num = 600; test_num = 200; %训练数据,图像转向量 data_train = mat2vector(train_images(:,:,1:train_num),train_num); data_test = mat2vector(test_images(:,:,1:test_num),test_num); % 处理训练数据,防止后验概率为0 [data_train,position] = fun(data_train,train_labels1(1:train_num)'); % 处理测试数据 for rows = 1:10 data_test(:,position{1,rows})=[]; end %模型部分 Mdl = fitcnb(data_train,train_labels1(1:train_num)); %测试结果 result = predict(Mdl,data_test); result = result.'; xlabel=[0,1,2,3,4,5,6,7,8,9]; resultbar = [0,0,0,0,0,0,0,0,0,0]; testbar = [0,0,0,0,0,0,0,0,0,0]; for i = 1:test_num temp1=result(i); temp1=temp1+1; resultbar(temp1)=resultbar(temp1)+1; temp2=test_labels1(i); temp2=temp2+1; testbar(temp2)=testbar(temp2)+1; end bar(xlabel, [resultbar' testbar']); % 整体正确率 acc = 0.; for i = 1:test_num if result(i)==test_labels1(i) acc = acc+1; end end title('精确度为：',(acc/test_num)*100) end function [output,position] = fun(data,label) position = cell(1,10); %创建cell存储每类中删除的列标 for i = 0:9 temp = []; pos = []; for rows = 1:size(data,1) if label(rows)==i temp = [temp;data(rows,:)]; end end for cols = 1:size(temp,2) var_data = var(temp(:,cols)); if var_data==0 pos = [pos,cols]; end end position{i+1} = pos; data(:,pos)=[]; end output = data; end function [data_]= mat2vector(data,num) [row,col,~] = size(data); data_ = zeros(num,row*col); for page = 1:num for rows = 1:row for cols = 1:col data_(page,((rows-1)*col+cols)) = im2double(data(rows,cols,page)); end end end end 将画图部分重写，完成相同功能

好的，以下是将画图部分重写的代码：

% 统计预测结果和真实结果的数量
result_count = histcounts(result, 0:10);
test_count = histcounts(test_labels1, 0:10);

% 绘制柱状图
figure;
bar(0:9, [result_count', test_count']);
xlabel('数字');
ylabel('数量');
legend('预测结果', '真实结果');

% 计算并输出整体正确率
accuracy = sum(result == test_labels1) / test_num;
title(['整体正确率为：', num2str(accuracy * 100), '%']);

改动后，完整代码如下：

function untitled()
    load('D:\mat格式的MNIST数据\test_labels.mat')
    load('D:\mat格式的MNIST数据\train_images.mat')
    load('D:\mat格式的MNIST数据\train_labels.mat')
    load('D:\mat格式的MNIST数据\test_images.mat')
    
    train_num = 600;
    test_num = 200;
    
    % 训练数据,图像转向量
    data_train = mat2vector(train_images(:,:,1:train_num),train_num);
    data_test = mat2vector(test_images(:,:,1:test_num),test_num);
    
    % 处理训练数据,防止后验概率为0
    [data_train, position] = fun(data_train,train_labels1(1:train_num)');
    
    % 处理测试数据
    for rows = 1:10
        data_test(:,position{1,rows})=[];
    end
    
    % 模型部分
    Mdl = fitcnb(data_train,train_labels1(1:train_num));
    
    % 测试结果
    result = predict(Mdl,data_test);
    result = result.';
    
    % 统计预测结果和真实结果的数量
    result_count = histcounts(result, 0:10);
    test_count = histcounts(test_labels1, 0:10);
    
    % 绘制柱状图
    figure;
    bar(0:9, [result_count', test_count']);
    xlabel('数字');
    ylabel('数量');
    legend('预测结果', '真实结果');
    
    % 计算并输出整体正确率
    accuracy = sum(result == test_labels1) / test_num;
    title(['整体正确率为：', num2str(accuracy * 100), '%']);
end

function [output,position] = fun(data,label)
    position = cell(1,10); % 创建cell存储每类中删除的列标
    for i = 0:9
        temp = [];
        pos = [];
        for rows = 1:size(data,1)
            if label(rows)==i
                temp = [temp;data(rows,:)];
            end
        end
        for cols = 1:size(temp,2)
            var_data = var(temp(:,cols));
            if var_data==0
                pos = [pos,cols];
            end
        end
        position{i+1} = pos;
        data(:,pos)=[];
    end
    output = data;
end

function [data_] = mat2vector(data,num)
    [row,col,~] = size(data);
    data_ = zeros(num,row*col);
    for page = 1:num
        for rows = 1:row
            for cols = 1:col
                data_(page,((rows-1)*col+cols)) = im2double(data(rows,cols,page));
            end
        end
    end
end

可以帮我把以下代码转成python语言吗：%data = randn(1000,16); %label = randi([0,1],1000,1); % data_trainnn_struct=load("data_trainn.mat"); label_trainnn_struct=load("label_trainn.mat"); data_trainnn=data_trainnn_struct.data; label_trainnn=label_trainnn_struct.label; % 数据预处理 data = zscore(data_trainnn); % 标准化数据 label = categorical(label_trainnn); % 标签划分为分类变量类型 % 进行数据集的划分 cv = cvpartition(length(label_trainnn), 'HoldOut', 0.3); idxTrain = training(cv); idxTest = test(cv); trainData = data(idxTrain,:); trainLabel = label(idxTrain); testData = data(idxTest,:); testLabel = label(idxTest); % 训练分类器 mdl = fitcecoc(trainData, trainLabel); % 预测测试集 predLabel = predict(mdl, testData); % 计算准确率 accuracy = sum(predLabel == testLabel)/numel(testLabel); disp(['Accuracy: ', num2str(accuracy)]); newData = data_filtered'; %代入滤波数据 % 对未知的样本进行数据预处理 newData = zscore(newData); % 训练完毕的分类器 predLabel = predict(mdl, newData); predLabels = double(predLabel); anss=0; %划分标准 avg = mean(predLabels); if abs(avg - 0.5) < 1 anss=1; elseif abs(avg) >= 1.5 anss=0; end

以下是转换后的 Python 代码：

import numpy as np
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC

# 加载数据和标签
data = np.random.randn(1000, 16)
label = np.random.randint(2, size=(1000,))

data_trainnn_struct = loadmat('data_trainn.mat')
label_trainnn_struct = loadmat('label_trainn.mat')

data_trainnn = data_trainnn_struct['data']
label_trainnn = label_trainnn_struct['label']

# 数据预处理
scaler = StandardScaler()
data = scaler.fit_transform(data_trainnn)
label = label_trainnn.astype('category')

# 进行数据集的划分
train_data, test_data, train_label, test_label = train_test_split(
    data, label, test_size=0.3, random_state=42)

# 训练分类器
clf = SVC(decision_function_shape='ovo')
clf.fit(train_data, train_label)

# 预测测试集
pred_label = clf.predict(test_data)

# 计算准确率
accuracy = accuracy_score(test_label, pred_label)
print('Accuracy:', accuracy)

# 对未知的样本进行数据预处理
new_data = data_filtered.T

# 数据预处理
new_data = scaler.transform(new_data)

# 预测未知样本
pred_label = clf.predict(new_data)
pred_labels = pred_label.astype(int)

# 划分标准
avg = pred_labels.mean()
if abs(avg - 0.5) < 1:
    anss = 1
elif abs(avg) >= 1.5:
    anss = 0