如何将上面的代码在tensorflow中引入到CNN模型中并进行训练

将上述的空间注意力机制引入到CNN模型中可以通过以下代码实现：

import tensorflow as tf

# 定义CNN模型
def cnn_model(input_feature):
    conv1 = tf.layers.conv2d(inputs=input_feature, filters=32, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # 在第一层卷积之后引入空间注意力机制
    attention1 = spatial_attention(pool1)

    conv2 = tf.layers.conv2d(inputs=attention1, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # 在第二层卷积之后引入空间注意力机制
    attention2 = spatial_attention(pool2)

    pool2_flat = tf.reshape(attention2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(inputs=dense, rate=0.4)

    logits = tf.layers.dense(inputs=dropout, units=10)
    return logits

# 定义训练过程
def train_model():
    # 加载数据集
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

    x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.
    x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.

    y_train = tf.keras.utils.to_categorical(y_train, 10)
    y_test = tf.keras.utils.to_categorical(y_test, 10)

    # 定义输入和输出占位符
    input_feature = tf.placeholder(tf.float32, shape=[None, 28, 28, 1], name='input_feature')
    output_label = tf.placeholder(tf.float32, shape=[None, 10], name='output_label')

    # 构建模型
    logits = cnn_model(input_feature)

    # 定义损失函数和优化器
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=output_label, logits=logits))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    # 定义评估指标
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(output_label, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # 开始训练模型
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for i in range(10000):
            batch = mnist.train.next_batch(50)
            if i % 100 == 0:
                train_accuracy = accuracy.eval(feed_dict={input_feature: batch[0], output_label: batch[1]})
                print('step %d, training accuracy %g' % (i, train_accuracy))
            train_step.run(feed_dict={input_feature: batch[0], output_label: batch[1]})

        test_accuracy = accuracy.eval(feed_dict={input_feature: mnist.test.images, output_label: mnist.test.labels})
        print('test accuracy %g' % test_accuracy)

在训练模型的过程中，我们首先加载MNIST数据集，并对输入的特征图进行归一化处理。然后，我们定义输入和输出的占位符，构建模型，并定义损失函数和优化器。最后，我们在训练过程中循环执行训练步骤，并在每100个步骤之后输出训练准确率。在训练完成后，我们计算测试准确率并输出它。