在MNIST模型上测试图像，Python TensorFlow

我最近开始同时学习python和tensorflow，我目前正在研究MNIST，这里是关于MNIST数据集的代码，模型训练和测试，我的下一个任务是从计算机中取出一个图像，在我的程序中导入它并测试我训练过的模特上的那张照片 . 所以我有2个问题

• 如何保存我的模型，以便我不必一次又一次地运行它？

• 如何在此模型上导入和测试图像，以便模型可以预测哪个数字

import tensorflow as tf`
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10
batch_size = 100
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')

def neural_network_model(data):
    hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])),
                      'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                  'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                  'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}

output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                'biases': tf.Variable(tf.random_normal([n_classes])), }

l1 = tf.add(
    tf.matmul(
        data,
        hidden_1_layer['weights']),
    hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)

l2 = tf.add(
    tf.matmul(
        l1,
        hidden_2_layer['weights']),
    hidden_2_layer['biases'])
l2 = tf.nn.relu(l2)

l3 = tf.add(
    tf.matmul(
        l2,
        hidden_3_layer['weights']),
    hidden_3_layer['biases'])
l3 = tf.nn.relu(l3)

output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']

return output


def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(
            logits=prediction, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(hm_epochs):
        epoch_loss = 0
        for _ in range(int(mnist.train.num_examples / batch_size)):
            epoch_x, epoch_y = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={
                            x: epoch_x, y: epoch_y})
            epoch_loss += c

        print(
            'Epoch',
            epoch,
            'completed out of',
            hm_epochs,
            'loss:',
            epoch_loss)

    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
    print('Accuracy:', accuracy.eval(
        {x: mnist.test.images, y: mnist.test.labels}))

train_neural_network(x)