卷积神经网络3完全解析

#coding:utf-8

import time

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

import mnist_lenet5_forward

import mnist_lenet5_backward

import numpy as np

TEST_INTERVAL_SECS = 5

def test(mnist):

    with tf.Graph().as_default() as g:

        x = tf.placeholder(tf.float32,[

            mnist.test.num_examples,

            mnist_lenet5_forward.IMAGE_SIZE,

            mnist_lenet5_forward.IMAGE_SIZE,

            mnist_lenet5_forward.NUM_CHANNELS])

        y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE])

        y = mnist_lenet5_forward.forward(x,False,None)

        ema = tf.train.ExponentialMovingAverage(mnist_lenet5_backward.MOVING_AVERAGE_DECAY)

        ema_restore = ema.variables_to_restore()

        saver = tf.train.Saver(ema_restore)

        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))#预测值y和实际值y_是否一致

        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))#求平均得到预测准确率

        while True:

            with tf.Session() as sess:

                ckpt = tf.train.get_checkpoint_state(mnist_lenet5_backward.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]

                    reshaped_x = np.reshape(mnist.test.images,(

                    mnist.test.num_examples,

                mnist_lenet5_forward.IMAGE_SIZE,

                mnist_lenet5_forward.IMAGE_SIZE,

                mnist_lenet5_forward.NUM_CHANNELS))

                    accuracy_score = sess.run(accuracy, feed_dict={x:reshaped_x,y_:mnist.test.labels})

                    print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))

                else:

                    print('No checkpoint file found')

                    return

            time.sleep(TEST_INTERVAL_SECS)

def main():

    mnist = input_data.read_data_sets("./data/", one_hot=True)

    test(mnist)

if __name__ == '__main__':

    main()