以下是一个使用 TensorFlow 实现带自注意力机制的卷积神经网络（Convolutional Neural Network, CNN）进行训练和预测的示例代码：

import tensorflow as tf# 定义模型的超参数
learning_rate = 0.001
epochs = 10
batch_size = 32# 构建带自注意力机制的卷积神经网络模型
def self_attention_cnn(input_shape, num_classes):inputs = tf.keras.Input(shape=input_shape)# 卷积层x = tf.keras.layers.Conv1D(32, 3, activation='relu')(inputs)x = tf.keras.layers.MaxPooling1D(2)(x)x = tf.keras.layers.Conv1D(64, 3, activation='relu')(x)x = tf.keras.layers.MaxPooling1D(2)(x)x = tf.keras.layers.Conv1D(128, 3, activation='relu')(x)x = tf.keras.layers.MaxPooling1D(2)(x)# 自注意力机制attention_weights = tf.keras.layers.Dense(1, activation='softmax')(x)attention_weights = tf.keras.layers.Flatten()(attention_weights)attention_weights = tf.keras.layers.Softmax()(attention_weights)attention_weights = tf.keras.layers.Reshape((1, -1))(attention_weights)x = tf.keras.layers.Dot(axes=[2, 1])([attention_weights, x])# 全连接层x = tf.keras.layers.Dense(128, activation='relu')(x)x = tf.keras.layers.Dropout(0.5)(x)x = tf.keras.layers.Dense(num_classes, activation='softmax')(x)model = tf.keras.Model(inputs=inputs, outputs=x)return model# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.your_dataset.load_data()# 预处理数据
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train)
x_test = tf.keras.preprocessing.sequence.pad_sequences(x_test)# 构建模型
input_shape = x_train.shape[1:]
num_classes = len(set(y_train))
model = self_attention_cnn(input_shape, num_classes)# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),loss=tf.keras.losses.SparseCategoricalCrossentropy(),metrics=['accuracy'])# 训练模型
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))# 预测数据
predictions = model.predict(x_test)

在上述代码中，你需要根据你的具体数据集，自定义加载数据集的部分（tf.keras.datasets.your_dataset.load_data()）。其余代码构建了一个具有自注意力机制的卷积神经网络模型，并使用 Adam 优化器进行训练和预测。