如何使用tensorflow2代码实现SE注意力模块

您好，可以使用以下代码实现SE注意力模块：

from tensorflow.keras.layers import Layer, Dense, Reshape, Multiply, Add
import tensorflow.keras.backend as K

class SE(Layer):
    def __init__(self, units, activation='relu', **kwargs):
        super(SE, self).__init__(**kwargs)
        self.units = units
        self.activation = activation

    def build(self, input_shape):
        self.fc1 = Dense(self.units, activation=self.activation, kernel_initializer='he_normal')
        self.fc2 = Dense(input_shape[-1], activation='sigmoid', kernel_initializer='glorot_normal')

    def call(self, inputs):
        x = K.mean(inputs, axis=1, keepdims=True)
        x = self.fc1(x)
        x = self.fc2(x)
        x = Multiply()([inputs, x])
        return Add()([inputs, x])

    def compute_output_shape(self, input_shape):
        return input_shape

使用方法：

from tensorflow.keras.layers import Input, Embedding, LSTM, Bidirectional, Dense
from tensorflow.keras.models import Model

inputs = Input(shape=(10,))
x = Embedding(input_dim=1000, output_dim=256)(inputs)
x = Bidirectional(LSTM(64, return_sequences=True))(x)
x = SE(64)(x)
x = Dense(1, activation='sigmoid')(x)

model = Model(inputs, x)
model.summary()

其中SE(64)表示输出特征维度为64的SE注意力模块。感谢您的提问，如果还有其他问题可以继续咨询我。