DSAN的keras代码

以下是一个简单的 DSAN 模型的 Keras 代码实现：

from keras import layers
from keras.models import Model
from keras.optimizers import Adam

def DSAN(input_shape, num_classes):
    inputs = layers.Input(shape=input_shape)

    # Spatial Attention Module
    x1 = layers.Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding="same", activation="relu")(inputs)
    x2 = layers.Conv2D(filters=64, kernel_size=(3,3), strides=(2,2), padding="same", activation="relu")(x1)
    x3 = layers.Conv2D(filters=64, kernel_size=(3,3), strides=(2,2), padding="same", activation="relu")(x2)
    x4 = layers.Conv2D(filters=64, kernel_size=(3,3), strides=(2,2), padding="same", activation="relu")(x3)

    # Channel Attention Module
    avg_pool = layers.GlobalAveragePooling2D()(x4)
    max_pool = layers.GlobalMaxPooling2D()(x4)
    a = layers.Dense(units=64, activation='relu')(layers.concatenate([avg_pool, max_pool]))
    a = layers.Dense(units=64, activation='sigmoid')(a)
    a = layers.Reshape((1,1,64))(a)
    x5 = layers.Multiply()([x4, a])

    # Classification
    x6 = layers.Flatten()(x5)
    x6 = layers.Dense(units=256, activation='relu')(x6)
    outputs = layers.Dense(units=num_classes, activation='softmax')(x6)

    model = Model(inputs, outputs)
    model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

    return model

以上代码实现了 DSAN 模型的网络结构，包括空间注意力模块和通道注意力模块。其中，空间注意力模块包括四个卷积层，每个卷积层的步长为2，用于逐渐减少特征图的大小。通道注意力模块包括两个全连接层和一个 sigmoid 激活函数，用于计算特征图中每个通道的权重。最终，将空间注意力模块和通道注意力模块结合起来，进行分类任务。