请写出cnn-attention模型的示例代码

以下是一个简单的CNN-Attention模型的示例代码：

import tensorflow as tf

# Define the input shape
input_shape = (None, 28, 28, 1)  # Batch size x Height x Width x Channels

# Define the CNN layers
cnn_model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=input_shape[1:]),
    tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
    tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
    tf.keras.layers.Flatten(),
])

# Define the attention mechanism
attention_layer = tf.keras.layers.Attention()

# Define the fully connected layers
fc_model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Combine the CNN and attention layers
input_tensor = tf.keras.layers.Input(shape=input_shape)
cnn_output = cnn_model(input_tensor)
attention_output = attention_layer([cnn_output, cnn_output])
fc_input = tf.keras.layers.GlobalAveragePooling2D()(attention_output)
fc_output = fc_model(fc_input)
model = tf.keras.models.Model(inputs=input_tensor, outputs=fc_output)

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Print the model summary
model.summary()

在这个示例代码中，我们首先定义了一个简单的CNN模型，然后定义了一个Attention层。接着，我们定义了一个全连接层，最后将CNN和Attention层和全连接层组合在一起。最后，我们编译了这个模型，并打印了模型的摘要信息。