tensorfow模型保存并导入返回预测结果

首先新建一个文件夹：‘Model’

模型保存：

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

mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)

x = tf.placeholder(tf.float32, [None, 784], name='x')
y_ = tf.placeholder(tf.int32, [None, ], name='y')

dense1 = tf.layers.dense(inputs=x,
                         units=1024,
                         activation=tf.nn.relu,
                         kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                         kernel_regularizer=tf.nn.l2_loss)
dense2 = tf.layers.dense(inputs=dense1,
                         units=512,
                         activation=tf.nn.relu,
                         kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                         kernel_regularizer=tf.nn.l2_loss)
logits = tf.layers.dense(inputs=dense2,
                         units=10,
                         activation=None,
                         kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                         kernel_regularizer=tf.nn.l2_loss, name='logit')

loss = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

saver = tf.train.Saver()
# saver = tf.train.Saver(max_to_keep=1)
max_acc = 0
for i in range(100):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_op, feed_dict={x: batch_xs, y_: batch_ys})
    val_loss, val_acc = sess.run([loss, acc], feed_dict={x: mnist.test.images, y_: mnist.test.labels})
    print('epoch:%d, val_loss:%f, val_acc:%f' % (i, val_loss, val_acc))
    if val_acc > max_acc:
        max_acc = val_acc
        saver.save(sess, 'Model/mnist.ckpt', global_step=i + 1)

导入模型并输出预测结果：

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
saver = tf.train.import_meta_graph('Model/mnist.ckpt-100.meta')
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint("Model/"))
    graph = tf.get_default_graph()
    logits = graph.get_tensor_by_name("logit/BiasAdd:0")
    print(logits)
    for i in range(100):
        batch_xs, batch_ys = mnist.train.next_batch(50)
        prediction = sess.run(logits, feed_dict={'x:0': batch_xs, 'y:0': batch_ys})
        prediction = np.array(prediction)
        print(prediction.argmax(axis=1))

输出结果

参考博客：非常抱歉忘记了，实现了很长时间，然后才想起来写博客。