matlab transformer分类

kbhit()) // 检测键盘输入
{
char ch = _getch();
switch (ch)
{
case 'a':
if (checkShape(shape, -1, 0, 0))
{
drawShape(shape, true在MATLAB中使用Transformer进行分类，可以遵循以下步骤：

1.准备数据集：收集或创建适当);
shape.x--;
drawShape(shape);
}
break;
case 'd':
if (checkShape(shape, 的数据集，将其分为训练集、验证集和测试集。

2.数据预处理：对数据进行必要1, 0, 0))
{
drawShape(shape, true);
shape.x++;
drawShape(shape);
}
的预处理，如图像缩放、归一化、裁剪、旋转等。

3.构建Transformer模型： break;
case 's':
if (checkShape(shape, 0, 1, 0))
{
drawShape(shape, true);
shape.y++;
drawShape(shape);
}
break;
case 'w':
if (check使用MATLAB Deep Learning Toolbox中的transformerLayer函数构建Transformer模型。

4.训练模型：使用训练集Shape(shape, 0, 0, 1))
{
drawShape(shape, true);
shape.angle = (shape.angle对模型进行训练，并使用验证集对模型进行验证和优化。

5.测试模型：使用测试集对 + 1) % 4;
drawShape(shape);
}
break;
case ' ':
while (checkShape模型进行测试，并计算模型的准确率、精度、召回率等性能指标。

以下是一个(shape, 0, 1, 0))
{
drawShape(shape, true);
shape.y++;
drawShape(shape简单的MATLAB代码示例，展示如何使用Transformer进行图像分类：

% 加载和划分数据);
                }
                break;
            }
        }
        if (checkShape(shape, 0, 1, 0))
        {
集
imds = imageDatastore('path/to/images', 'LabelSource', 'foldernames');
[imdsTrain,imdsValidation            drawShape(shape, true);
            shape.y++;
            drawShape(shape);
        }
        else
        {
            fixShape(shape);
] = splitEachLabel(imds,0.7);

% 创建Transformer网络层
dModel = 64;
numHeads =             clearLines();
            shape = generateShape();
            drawShape(shape);
            if (!checkShape(shape, 0, 0,4;
dff = 256;
dropoutRate = 0.1;
transformerLayer = transformerLayer(dModel,numHeads,dff, 0))
            {
                cout << "Game Over!" << endl;
                break;
            }
        }
        DWORD end = GetTickCountdropoutRate);

% 构建分类器
numClasses = numel(categories(imdsTrain.Labels));
inputSize = [224 224 3();
        if (end - start < speed)
            Sleep(speed - (end - start));
    }
    return 0;
}

];
outputLayer = fullyConnectedLayer(numClasses,'Name','fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);

%注意：本代码仅供学习参考，不保证完全正确和可靠，存在一些局限性和不 创建分类模型
layers = [
imageInputLayer(inputSize,'Name','input','Normalization','rescale-symmetric')
transformerLayer足，欢迎各位大佬指正和改进！