基于tensorflow2.0注意力机制进行人脸表情识别的代码

以下是基于 TensorFlow 2.0 和注意力机制的人脸表情识别代码：

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models

# 定义注意力机制函数
def attention_block(inputs, skip):
    # 获取输入和跳跃层的形状
    input_shape = tf.keras.backend.int_shape(inputs)
    skip_shape = tf.keras.backend.int_shape(skip)
    # 进行卷积操作
    x = layers.Conv2D(input_shape[-1], (1, 1), padding='same')(inputs)
    skip = layers.Conv2D(skip_shape[-1], (1, 1), padding='same')(skip)
    # 将输入和跳跃层的形状进行变换
    x = layers.Reshape((input_shape[1]*input_shape[2], input_shape[-1]))(x)
    skip = layers.Reshape((skip_shape[1]*skip_shape[2], skip_shape[-1]))(skip)
    # 计算注意力分数
    attention_scores = layers.Dot(axes=[2, 2])([x, skip])
    attention_scores = layers.Softmax()(attention_scores)
    # 进行加权平均
    x = layers.Dot(axes=[2, 1])([attention_scores, skip])
    x = layers.Reshape((input_shape[1], input_shape[2], input_shape[-1]))(x)
    # 合并输入和加权平均后的结果
    x = layers.Add()([x, inputs])
    return x

# 定义网络模型
def build_model():
    input_shape = (48, 48, 1)
    inputs = layers.Input(shape=input_shape)
    x = layers.Conv2D(64, (3, 3), padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(64, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    # 添加注意力机制
    x = attention_block(x, inputs)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = layers.Conv2D(128, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(128, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    # 添加注意力机制
    x = attention_block(x, inputs)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = layers.Conv2D(256, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(256, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    # 添加注意力机制
    x = attention_block(x, inputs)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = layers.Flatten()(x)
    x = layers.Dense(128)(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Dropout(0.5)(x)

    outputs = layers.Dense(7, activation='softmax')(x)

    model = models.Model(inputs, outputs)

    return model

在这个代码中，我们首先定义了一个 `attention_block` 函数，该函数实现了注意力机制的计算。然后，我们定义了一个 `build_model` 函数，该函数定义了一个具有注意力机制的卷积神经网络模型。在这个模型中，我们添加了三个注意力机制块，分别在第一、第二和第三个卷积层之后。最后，我们将模型的输出连接到一个具有 7 个类的全连接层，使用 softmax 激活函数进行分类。