mnist_eval.py

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

# 每五秒加载一次最新的模型，并在测试数据上测试最新模型的准确率
EVAL_INTERVAL_SECS = 5


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)
        predict = tf.argmax(y, 1)[0:10]
        # 真实值
        real = tf.argmax(y_, 1)[0:10]

        # 使用前向传播的结果计算正确率。如果需要对未知的样例进行分类，那么使用tf.argmax(y, 1)就可以得到输入样例的预测类别
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        # 通过变量重命名的方式来加载模型
        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)

        # 每隔EVAL_INTERVAL_SECS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.Session() as sess:
                # 函数通过checkpoint文件自动找到目录中最新模型的文件名
                ckpt = tf.train.get_checkpoint_state(mnist_train.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    # 加载模型
                    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 step(s), validation accuracy = %g" % (global_step, accuracy_score))
                    prediction = sess.run(predict, feed_dict=validate_feed)
                    real_value = sess.run(real, feed_dict=validate_feed)
                    # 输出前十个预测值和真实值
                    print('after %s training step(s), validation prediction is ' % (global_step), prediction)
                    print('after %s training step(s), real value is            ' % (global_step), real_value)


                else:
                    print('No checkpoint file found')
                    return
            time.sleep(EVAL_INTERVAL_SECS)


def main(argv=None):
    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
    evaluate(mnist)


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