mnist_eval.py

# -*- coding: UTF-8 -*-
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference
import mnist_train

# 每10秒加载一次最新的模型，并在测试数据上测试最新模型的正确率
EVAL_INITERVAL_SECS = 10


def evaluate(mnist):
    with tf.Graph().as_default() as g:
        # 定义输入输出的格式
        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')
        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}

        y = mnist_inference.inference(x, None)

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

        # 变量重命名方式加载模型，共用mnist_inference.py中的前向传播过程
        variable_average = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)
        variable_to_restore = variable_average.variables_to_restore()
        saver = tf.train.Saver(variable_to_restore)

        while True:
            with tf.Session() as sess:
                ckpt = tf.train.get_checkpoint_state(mnist_train.MODE_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    # 加载模型
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    # 通过文件名得到模型保存时迭代的轮数
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    accuracy_score = sess.run(accuracy, feed_dict=validate_feed)
                    print("After %s training step(s), validation "
                          "accuracy = %g" % (global_step, accuracy_score))
                else:
                    print('No checkpoint file found')
                    return
            time.sleep(EVAL_INITERVAL_SECS)


def main(argv=None):
    mnist = input_data.read_data_sets("/path/to/mnist_data", one_hot=True)
    evaluate(mnist)


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