resnet34加入注意力机制keras

可以使用Keras中的`Attention`层来为ResNet34加入注意力机制，以下是一个示例代码：

from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense, Add, GlobalAveragePooling2D, Multiply
from tensorflow.keras.layers import Reshape, Permute, multiply, LSTM, Lambda, TimeDistributed, concatenate
from tensorflow.keras import Model

def attention_block(inputs, time_steps):
    a = Permute((2,1))(inputs) # 将时间步和特征维度进行交换，方便后续计算
    a = Dense(time_steps, activation='softmax')(a) # 经过全连接层后进行softmax操作，得到每个时间步的权重
    a = Permute((2,1))(a) # 再将时间步和特征维度进行交换，还原原始维度
    a = multiply([inputs, a]) # 将输入和注意力权重相乘得到加权输入
    return a

def resnet_block(inputs, filters, kernel_size, strides):
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    shortcut = Conv2D(filters=filters, kernel_size=1, strides=strides, padding='same')(inputs)
    shortcut = BatchNormalization()(shortcut)
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def ResNet34_attention(input_shape, time_steps):
    inputs = Input(shape=input_shape)

    x = Conv2D(filters=64, kernel_size=7, strides=2, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    for i in range(3):
        x = resnet_block(x, filters=64, kernel_size=3, strides=1)

    x = attention_block(x, time_steps=time_steps)

    for i in range(4):
        if i==0:
            x = resnet_block(x, filters=128, kernel_size=3, strides=2)
        else:
            x = resnet_block(x, filters=128, kernel_size=3, strides=1)

    x = attention_block(x, time_steps=time_steps)

    for i in range(6):
        if i==0:
            x = resnet_block(x, filters=256, kernel_size=3, strides=2)
        else:
            x = resnet_block(x, filters=256, kernel_size=3, strides=1)

    x = attention_block(x, time_steps=time_steps)

    for i in range(3):
        if i==0:
            x = resnet_block(x, filters=512, kernel_size=3, strides=2)
        else:
            x = resnet_block(x, filters=512, kernel_size=3, strides=1)

    x = GlobalAveragePooling2D()(x)
    x = Dense(1024, activation='relu')(x)
    x = Dense(512, activation='relu')(x)
    outputs = Dense(1, activation='sigmoid')(x)

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

    return model

在这个示例代码中，我们定义了一个`attention_block`函数，用于为ResNet34的每个`resnet_block`加入注意力机制。在`ResNet34_attention`函数中，我们先构建了ResNet34的主体结构，然后在第1、2、3、4个阶段的最后一个`resnet_block`后都加入了一个`attention_block`。最后，我们再添加全局平均池化层和全连接层进行分类。

需要注意的是，在`attention_block`函数中，我们使用了`Permute`层将时间步和特征维度进行交换，这是因为我们需要对每个时间步进行softmax计算。计算完成后，我们再将时间步和特征维度交换回来，得到加权输入。