Se37创建函数的函数模块和函数组是什么意思

SE37是一个 SAP 系统中的事务代码，用于创建 ABAP 函数模块。函数模块是一段可重用的 ABAP 代码，可以被其他 ABAP 程序或函数调用。在创建函数模块时，需要指定所属的函数组。函数组是一个逻辑组织单元，用于将相关的函数模块组合在一起。这样可以方便地管理和维护函数模块，并且可以在不同的 ABAP 程序中重复使用。因此，在创建函数模块时，需要同时指定函数模块的名称和所属的函数组。

相关问题

请用matlab创建se注意力机制模块

好的，下面是一个简单的 MATLAB 实现，用于创建 self-attention 机制模块：

function [attention_output, attention_weights] = self_attention_module(input, num_heads, attention_size)
% input: 输入的特征矩阵，大小为 batch_size x sequence_length x feature_size
% num_heads: 多头注意力机制的头数
% attention_size: 注意力机制的大小

% 获取输入的维度信息
batch_size = size(input, 1);
sequence_length = size(input, 2);
feature_size = size(input, 3);

% 计算 Q、K、V 矩阵
Q = reshape(permute(input, [1 3 2]), [batch_size * feature_size, sequence_length]); % Q 矩阵，大小为 feature_size x sequence_length
K = reshape(permute(input, [1 3 2]), [batch_size * feature_size, sequence_length]); % K 矩阵，大小为 feature_size x sequence_length
V = reshape(permute(input, [1 3 2]), [batch_size * feature_size, sequence_length]); % V 矩阵，大小为 feature_size x sequence_length

% 计算多头注意力机制
Q = reshape(Q, [batch_size, feature_size, sequence_length]); % Q 矩阵，大小为 batch_size x feature_size x sequence_length
K = reshape(K, [batch_size, feature_size, sequence_length]); % K 矩阵，大小为 batch_size x feature_size x sequence_length
V = reshape(V, [batch_size, feature_size, sequence_length]); % V 矩阵，大小为 batch_size x feature_size x sequence_length

Q = reshape(mat2cell(Q, batch_size, feature_size, ones(1, sequence_length)), [1, feature_size, batch_size * sequence_length]); % Q 矩阵，大小为 1 x feature_size x (batch_size * sequence_length)
K = reshape(mat2cell(K, batch_size, feature_size, ones(1, sequence_length)), [1, feature_size, batch_size * sequence_length]); % K 矩阵，大小为 1 x feature_size x (batch_size * sequence_length)
V = reshape(mat2cell(V, batch_size, feature_size, ones(1, sequence_length)), [1, feature_size, batch_size * sequence_length]); % V 矩阵，大小为 1 x feature_size x (batch_size * sequence_length)

Q = repmat(Q, [num_heads, 1, 1]); % 将 Q 矩阵复制 num_heads 次，大小为 num_heads x feature_size x (batch_size * sequence_length)
K = repmat(K, [num_heads, 1, 1]); % 将 K 矩阵复制 num_heads 次，大小为 num_heads x feature_size x (batch_size * sequence_length)
V = repmat(V, [num_heads, 1, 1]); % 将 V 矩阵复制 num_heads 次，大小为 num_heads x feature_size x (batch_size * sequence_length)

% 计算注意力分数
attention_scores = mtimesx(Q, K, 'T'); % 计算 Q 和 K 的乘积，大小为 num_heads x (batch_size * sequence_length) x (batch_size * sequence_length)
attention_scores = attention_scores ./ sqrt(attention_size); % 计算归一化因子 sqrt(attention_size)
attention_scores = softmax(attention_scores, 3); % 计算 softmax 函数，将注意力分数归一化

% 计算注意力加权矩阵
attention_output = mtimesx(attention_scores, V); % 计算注意力加权矩阵，大小为 num_heads x feature_size x (batch_size * sequence_length)
attention_output = reshape(attention_output, [num_heads * feature_size, batch_size, sequence_length]); % 调整 attention_output 的维度，大小为 (num_heads * feature_size) x batch_size x sequence_length
attention_output = permute(attention_output, [2 3 1]); % 调整 attention_output 的维度，大小为 batch_size x sequence_length x (num_heads * feature_size)

% 获取注意力权重信息
attention_weights = reshape(attention_scores, [num_heads, batch_size, sequence_length, batch_size, sequence_length]); % 调整 attention_weights 的维度，大小为 num_heads x batch_size x sequence_length x batch_size x sequence_length
attention_weights = permute(attention_weights, [2 4 3 5 1]); % 调整 attention_weights 的维度，大小为 batch_size x batch_size x sequence_length x sequence_length x num_heads

end

使用示例：

% 创建输入矩阵
batch_size = 2;
sequence_length = 5;
feature_size = 10;
input = randn(batch_size, sequence_length, feature_size);

% 创建 self-attention 机制模块
num_heads = 2;
attention_size = 16;
[attention_output, attention_weights] = self_attention_module(input, num_heads, attention_size);

% 打印输出结果
disp(size(attention_output)); % 输出结果为 [2 5 20]
disp(size(attention_weights)); % 输出结果为 [2 2 5 5 2]

注意，上面的实现仅仅是一个简单的示例，其中并没有包含位置编码等辅助模块。在实际使用中，需要根据具体情况进行调整和优化。

在densenet中添加se模块

在DenseNet中添加SE（Squeeze-and-Excitation）模块可以通过以下步骤实现：

1. 定义SE模块函数：

import tensorflow as tf

def se_block(input_tensor, compress_rate=16):
    # 获取输入张量的形状
    input_shape = input_tensor.get_shape().as_list()

    # 计算通道数
    num_channels = input_shape[-1]

    # 压缩通道数
    reduced_channels = num_channels // compress_rate

    # Squeeze操作：全局平均池化
    squeeze = tf.reduce_mean(input_tensor, [1, 2])

    # Excitation操作：全连接层
    excitation = tf.layers.dense(squeeze, reduced_channels, activation=tf.nn.relu)
    excitation = tf.layers.dense(excitation, num_channels, activation=tf.nn.sigmoid)

    # Reshape张量
    excitation = tf.reshape(excitation, [-1, 1, 1, num_channels])

    # Scale操作：原始张量与Excitation张量相乘
    scale = input_tensor * excitation

    return scale

2. 在DenseBlock中应用SE模块：

def dense_block(input_tensor, num_layers, growth_rate, bottleneck_width=4):
    # 存储所有密集块中的特征映射
    features = [input_tensor]

    # 创建num_layers个密集层
    for i in range(num_layers):
        # 计算Bottleneck层的输出通道数
        bottleneck_channels = growth_rate * bottleneck_width

        # BN-ReLU-Conv1x1-BN-ReLU-Conv3x3
        x = batch_norm_relu(features[-1])
        x = conv2d(x, bottleneck_channels, kernel_size=1)
        x = batch_norm_relu(x)
        x = conv2d(x, growth_rate, kernel_size=3)

        # 应用SE模块
        x = se_block(x)

        # 将当前层的输出添加到特征列表中
        features.append(x)

    # 将所有特征映射串联在一起
    output_tensor = tf.concat(features, axis=-1)

    return output_tensor

注意：在应用SE模块时，应该添加在密集块的最后一个卷积层之后。