基于TensorFlow2.0，在vgg19中增加注意力机制进行人脸表情识别的代码

以下是基于TensorFlow2.0，在vgg19中增加注意力机制进行人脸表情识别的代码：

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout, GlobalAveragePooling2D, Reshape, multiply

def vgg19_attention_model(input_shape, num_classes):
    # 构建VGG19模型
    input_tensor = Input(shape=input_shape)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(input_tensor)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    # 添加注意力机制
    attention = Dense(1, activation='tanh')(x)
    attention = Flatten()(attention)
    attention = Dense(512, activation='softmax')(attention)
    attention = Reshape((1, 1, 512))(attention)
    x = multiply([x, attention])

    # 添加全局平均池化层和全连接层
    x = GlobalAveragePooling2D()(x)
    x = Dropout(0.5)(x)
    x = Dense(num_classes, activation='softmax')(x)

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

    return model

在该模型中，注意力机制被添加到VGG19的最后一个卷积层之后，用于增强特定区域的特征。在注意力机制中，通过添加一个全连接层和一个softmax函数，计算每个特征图的权重，然后将这些权重与对应的特征图相乘，以增强需要注意的区域。最后，通过全局平均池化层和全连接层，将特征映射到表情分类的结果。