matlab self attention layer
时间: 2023-06-21 18:05:42 浏览: 243
在 MATLAB 中实现 Self-Attention Layer,可以按照以下步骤进行:
1. 定义一个自注意力层的类,该类应该继承自 matlab.mixin.CustomDisplay 类,以便在命令窗口中显示自定义信息。
2. 在类的构造函数中设置自注意力层的参数,包括输入向量的维度、自注意力头的数量、隐藏层的大小等。
3. 实现自注意力层的前向传播函数,该函数应该接受输入向量并返回自注意力层的输出。在前向传播函数中,需要实现自注意力机制,包括计算注意力权重、加权求和等操作。
4. 实现自注意力层的反向传播函数,该函数应该接收反向传播的梯度并返回输入向量的梯度。在反向传播函数中,需要计算注意力权重的导数、加权求和的导数等。
下面是一个简单的 MATLAB 自注意力层的示例代码:
```matlab
classdef SelfAttentionLayer < matlab.mixin.CustomDisplay
properties
input_dim
num_heads
hidden_dim
dropout_rate
query_weights
key_weights
value_weights
end
methods
function obj = SelfAttentionLayer(input_dim, num_heads, hidden_dim, dropout_rate)
obj.input_dim = input_dim;
obj.num_heads = num_heads;
obj.hidden_dim = hidden_dim;
obj.dropout_rate = dropout_rate;
obj.query_weights = randn(hidden_dim, input_dim);
obj.key_weights = randn(hidden_dim, input_dim);
obj.value_weights = randn(hidden_dim, input_dim);
end
function output = forward(obj, input)
batch_size = size(input, 1);
query = input * obj.query_weights';
key = input * obj.key_weights';
value = input * obj.value_weights';
query = reshape(query, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
key = reshape(key, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
value = reshape(value, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
attention_weights = softmax(query * permute(key, [1, 3, 2]) / sqrt(obj.hidden_dim/obj.num_heads), 3);
attention_weights = dropout(attention_weights, obj.dropout_rate);
output = reshape(attention_weights * value, [batch_size, obj.hidden_dim]);
end
function input_gradient = backward(obj, output_gradient, input)
batch_size = size(input, 1);
query = input * obj.query_weights';
key = input * obj.key_weights';
value = input * obj.value_weights';
query = reshape(query, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
key = reshape(key, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
value = reshape(value, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
attention_weights = softmax(query * permute(key, [1, 3, 2]) / sqrt(obj.hidden_dim/obj.num_heads), 3);
attention_weights = dropout(attention_weights, obj.dropout_rate);
output_gradient = reshape(output_gradient, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
value_gradient = attention_weights' * output_gradient;
attention_weights_gradient = output_gradient * permute(value, [1, 3, 2]);
attention_weights_gradient = attention_weights_gradient .* (attention_weights .* (1-attention_weights));
attention_weights_gradient = dropout(attention_weights_gradient, obj.dropout_rate);
query_gradient = attention_weights_gradient * key;
key_gradient = permute(query, [1, 3, 2]) * attention_weights_gradient;
input_gradient = (query_gradient + key_gradient + value_gradient) * obj.query_weights;
end
function displayScalarObject(obj)
disp(['SelfAttentionLayer with input_dim = ', num2str(obj.input_dim), ', num_heads = ', num2str(obj.num_heads), ', hidden_dim = ', num2str(obj.hidden_dim), ', dropout_rate = ', num2str(obj.dropout_rate)]);
end
end
end
```
该代码使用了随机初始化的权重矩阵,实现了自注意力层的前向传播和反向传播函数,并添加了自定义信息的显示功能。