下面是一个使用TensorFlow框架的带有自注意力机制的卷积神经网络（Self-Attention Convolutional Neural Network）的示例代码，包括数据处理、模型定义和训练过程：

import tensorflow as tf
from tensorflow.keras.layers import Conv1D, Dense, GlobalMaxPooling1D, Concatenate# 数据处理
# 假设你的数据是一个形状为(样本数，特征维度)的numpy数组
x_train = ...
y_train = ...# 创建模型
class SelfAttentionConvNet(tf.keras.Model):def __init__(self, num_classes, attention_heads=8, hidden_units=128):super(SelfAttentionConvNet, self).__init__()self.attention_heads = attention_headsself.hidden_units = hidden_unitsself.conv1 = Conv1D(hidden_units, kernel_size=3, activation='relu')self.conv2 = Conv1D(hidden_units, kernel_size=3, activation='relu')self.attention_w = self.add_weight(shape=(hidden_units, attention_heads),initializer='glorot_uniform',trainable=True)self.attention_b = self.add_weight(shape=(attention_heads,),initializer='zeros',trainable=True)self.fc = Dense(num_classes, activation='softmax')def call(self, inputs):x = self.conv1(inputs)x = self.conv2(x)# 自注意力机制attention_logits = tf.matmul(x, self.attention_w) + self.attention_battention_weights = tf.nn.softmax(attention_logits, axis=1)x = tf.reduce_sum(x * tf.expand_dims(attention_weights, axis=-1), axis=1)x = self.fc(x)return x# 定义训练相关参数
learning_rate = 0.001
batch_size = 32
epochs = 10# 创建模型实例
model = SelfAttentionConvNet(num_classes=10)# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])# 训练模型
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)

请注意，这只是一个示例代码，具体的实现细节可能需要根据你的数据和任务进行调整。