keras实现多头注意力机制代码

以下是一个简单的 Keras 实现多头注意力机制的代码：

from keras.layers import *
from keras.models import Model

def multihead_attention(x, n_heads):
    d_model = x.shape[-1]
    assert d_model % n_heads == 0
    d_head = d_model // n_heads

    def reshape_to_heads(x):
        return K.reshape(x, (-1, K.shape(x)[1], n_heads, d_head))

    def transpose_to_standard(x):
        return K.permute_dimensions(x, (0, 2, 1, 3))

    def reshape_from_heads(x):
        return K.reshape(x, (-1, K.shape(x)[1], n_heads * d_head))

    # Compute Q, K, V
    q = Dense(d_model)(x)
    k = Dense(d_model)(x)
    v = Dense(d_model)(x)

    # Reshape Q, K, V to heads
    q = reshape_to_heads(q)
    k = reshape_to_heads(k)
    v = reshape_to_heads(v)

    # Compute dot product attention
    attention = Dot(axes=[-1, -1])([q, k])
    attention = Lambda(lambda x: x / np.sqrt(d_head))(attention)
    attention = Activation('softmax')(attention)

    # Apply attention to V
    output = Dot(axes=[3, 2])([attention, v])
    output = transpose_to_standard(output)
    output = reshape_from_heads(output)

    return output

# Example usage
inputs = Input(shape=(100, 300))
attention_output = multihead_attention(inputs, n_heads=4)
model = Model(inputs, attention_output)

这个函数接受一个形状为 `(batch_size, sequence_length, input_dim)` 的张量 `x`，并返回一个形状相同的张量，它已经被多头注意力机制处理过了。其中 `n_heads` 是你要使用的注意力头的数量。这个函数中的实现是基于 Transformers 中的多头注意力机制。