tensorflow模型save和restore_tensorflow导入meta 恢复图预测-优快云博客

本文链接：https://blog.youkuaiyun.com/wang2008start/article/details/78125076

本文介绍了如何在TensorFlow中保存训练过程中的模型，并在后续使用中进行加载。通过使用saver.save()方法来保存模型，并利用saver.restore()方法进行恢复。文中详细展示了设置参数、保存模型以及从文件中加载模型的具体步骤。

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把tensorflow每个阶段训练的模型进行保存，供后面预测或者进一步训练时使用。保存的时候使用saver.save()方法，恢复的时候使用saver.restore方法。详细如下：

初始信息

flags.DEFINE_integer('epochs', 100, '') #FLAGS.epochs
flags.DEFINE_integer('display_steps', 10, 'Number of steps to run trainer.') #FLAGS.batch_size
flags.DEFINE_integer('batch_size', 256, 'Batch size.Must divide evenly into the dataset sizes.')  #FLAGS.batch_size
flags.DEFINE_string('train_dir', '/data1/Image/image_75', 'Directory to put the training data.')

模型save部分

for epoch in range(FLAGS.epochs):
           for batch_i in range(9):
                for batch_features, batch_labels in  img_util_getBatch(batch_i, FLAGS.batch_size):
                   train_neural_network(sess, optimizer, FLAGS.keep_probability, batch_features, batch_labels)
                print 'Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i)
                print_stats(sess, batch_features, batch_labels, cost)#, accuracy)
           if epoch%FLAGS.display_steps == 0:
             save_path = saver.save(sess, FLAGS.base_dir+'tf/', global_step=epoch, write_meta_graph=True)
             print("Model saved in file: %s" % save_path)
        saver.export_meta_graph(FLAGS.base_dir+'tf/'+'.meta')
        tf.train.write_graph(sess.graph_def, FLAGS.base_dir+'tf/', 'soft.ph',False)

需要注意的地方:
1. global_step的设置意义，在于给文件名字编号，如：

saver.save(sess,'my-model',global_step=10) --> filename:'my-model-10'
tf.train.Saver.__init__(varlist=None,reshape=False,shared=False,max_to_keep=5,keep_checkpoint_every_n_hour=100,name=None)

模型restore部分

    image = cv2.imread(path)
    x = tf.placeholder(tf.float32, shape=[None, 227, 225, 3], name='x')
    y = conv_net(x, 1)
    saver = tf.train.Saver()
    saver.restore(sess,tf.train.latest_checkpoint('/data1/wzy/deepImage/0919/tf2/'))
    feed_dict = {x: [image]}
    score = sess.run(y, feed_dict)

图片预测

    image = cv2.imread(image_path)
    x = tf.placeholder(tf.float32, shape=[None, 227, 225, 3], name='x')
    y = conv_net(x, 1)
    sess.run(tf.global_variables_initializer())
    saver = tf.train.import_meta_graph('/data1/Image/tf/20.meta')
    saver.restore(sess,tf.train.latest_checkpoint('/data1/Image/tf/'))
    feed_dict = {x: [image]}
    score = sess.run(y, feed_dict)