import time import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow.examples.tutorials.mnist import input_data import mnist_inference import mnist_train tf.compat.v1.reset_default_graph() EVAL_INTERVAL_SECS = 10 def evaluate(mnist): with tf.Graph().as_default() as g: #定义输入与输出的格式 x = tf.compat.v1.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input') y_ = tf.compat.v1.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input') validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels} #直接调用封装好的函数来计算前向传播的结果 y = mnist_inference.inference(x, None) #计算正确率 correcgt_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correcgt_prediction, tf.float32)) #通过变量重命名的方式加载模型 variable_averages = tf.train.ExponentialMovingAverage(0.99) variable_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variable_to_restore) #每隔10秒调用一次计算正确率的过程以检测训练过程中正确率的变化 while True: with tf.compat.v1.Session() as sess: ckpt = tf.train.get_checkpoint_state(minist_train.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: #load the model saver.restore(sess, ckpt.model_checkpoint_path) global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] accuracy_score = sess.run(accuracy, feed_dict=validate_feed) print("After %s training steps, validation accuracy = %g" % (global_step, accuracy_score)) else: print('No checkpoint file found') return time.sleep(EVAL_INTERVAL_SECS) def main(argv=None): mnist = input_data.read_data_sets(r"D:\Anaconda123\Lib\site-packages\tensorboard\mnist", one_hot=True) evaluate(mnist) if __name__ == '__main__': tf.compat.v1.app.run()对代码进行改进

以下是对代码的改进建议：

1. 在代码开头添加注释，简要说明代码功能和使用方法。

2. 将导入模块的语句放在代码开头。

3. 将模型保存路径和评估时间间隔定义为常量，并使用有意义的变量名。

4. 将计算正确率和加载模型的过程封装为函数。

5. 在主函数中调用评估函数。

改进后的代码如下：

# 该代码实现了使用已训练好的模型对 MNIST 数据集进行评估

import time
import tensorflow.compat.v1 as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
import mnist_train

# 定义常量
MODEL_SAVE_PATH = 'model/'
EVAL_INTERVAL_SECS = 10

def evaluate(mnist):
    """
    计算模型在验证集上的正确率
    """
    with tf.Graph().as_default() as g:
        # 定义输入和输出格式
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')

        # 直接调用封装好的函数计算前向传播结果
        y = mnist_inference.inference(x, None)

        # 计算正确率
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        # 加载模型
        variable_averages = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)

        # 在验证集上计算正确率
        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                accuracy_score = sess.run(accuracy, feed_dict={x: mnist.validation.images, y_: mnist.validation.labels})
                print("After %s training steps, validation accuracy = %g" % (global_step, accuracy_score))
            else:
                print('No checkpoint file found')

def main(argv=None):
    # 读取数据集
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

    # 每隔一定时间评估模型在验证集上的正确率
    while True:
        evaluate(mnist)
        time.sleep(EVAL_INTERVAL_SECS)

if __name__ == '__main__':
    tf.app.run()