一维cbam注意力机制代码适用于keras

以下是一维CBAM注意力机制的Keras代码示例：

from keras.layers import Input, Dense, Conv1D, Activation, Multiply, Add, Lambda
from keras.models import Model
from keras import backend as K

def cbam_block(cbam_feature, ratio=8):
    # 通道注意力机制
    cbam_feature_shape = cbam_feature.shape
    cbam_channel_avg = Lambda(lambda x: K.mean(x, axis=1, keepdims=True))(cbam_feature)
    cbam_channel_max = Lambda(lambda x: K.max(x, axis=1, keepdims=True))(cbam_feature)
    cbam_channel_concat = Concatenate(axis=1)([cbam_channel_avg, cbam_channel_max])
    cbam_channel_dense_1 = Dense(units=cbam_feature_shape[-1]//ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')(cbam_channel_concat)
    cbam_channel_dense_2 = Dense(units=cbam_feature_shape[-1], kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')(cbam_channel_dense_1)
    cbam_channel_attention = Activation('sigmoid')(cbam_channel_dense_2)
    cbam_channel_attention = Reshape((1, cbam_feature_shape[-1]))(cbam_channel_attention)
    cbam_feature = Multiply()([cbam_feature, cbam_channel_attention])

    # 空间注意力机制
    cbam_channel_maxpool = Lambda(lambda x: K.max(x, axis=-1, keepdims=True))(cbam_feature)
    cbam_channel_avgpool = Lambda(lambda x: K.mean(x, axis=-1, keepdims=True))(cbam_feature)
    cbam_channel_pool = Concatenate(axis=-1)([cbam_channel_maxpool, cbam_channel_avgpool])
    cbam_channel_conv = Conv1D(filters=1, kernel_size=7, padding='same', activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(cbam_channel_pool)
    cbam_feature = Multiply()([cbam_feature, cbam_channel_conv])

    return cbam_feature

input_tensor = Input(shape=(input_shape))
x = Conv1D(filters=64, kernel_size=7, strides=1, padding='same', kernel_initializer='he_normal', use_bias=False)(input_tensor)
x = cbam_block(x)
x = Conv1D(filters=64, kernel_size=7, strides=1, padding='same', kernel_initializer='he_normal', use_bias=False)(x)
x = cbam_block(x)
x = Conv1D(filters=64, kernel_size=7, strides=1, padding='same', kernel_initializer='he_normal', use_bias=False)(x)
x = cbam_block(x)
output_tensor = Conv1D(filters=num_classes, kernel_size=1, strides=1, padding='same', kernel_initializer='he_normal', use_bias=False)(x)

model = Model(inputs=input_tensor, outputs=output_tensor)

在这个示例中，我们使用了Keras的Lambda层来实现一些自定义的功能，如平均池化和最大池化。我们还使用了Keras的Reshape层来重新调整张量的形状。同时，我们使用了Keras的Concatenate层来连接不同的张量。另外，我们还使用了Keras的Multiply层来实现元素级别的乘法。

这个示例中，我们定义了一个名为cbam_block的函数，该函数实现了一维CBAM注意力机制。在这个函数中，我们首先实现了通道注意力机制，然后实现了空间注意力机制。最后，我们将两种注意力机制的结果相乘，得到最终的特征映射。

在这个示例中，我们将cbam_block函数应用于几个卷积层中。最后，我们使用一个具有num_classes个过滤器的1x1卷积层来输出最终的分类结果。

请注意，这只是一个示例，可以根据您的具体应用程序进行修改。