模型导出

tf.train.Saver
用于保存和恢复Variable。它可以非常方便的保存当前模型的变量或者倒入之前训练好的变量。一个最简单的运用：
saver - tf.train.Saver()

Save the variables to disk.
saver.save(sess, “/tmp/test.ckpt”)

Restore variables from disk.
saver.restore(sess, “/tmp/test.ckpt”)

tf.contrib.session_bundle.exporter.Exporter
导出模型还需要这个 Exporter 的协助。
Exporter 的基本使用方式是
1.传入 saver 构造一个实例
2.调用 init 定义模型的 graph 和 input/output
3.使用 export 导出为文件

import numpy as np
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
model_path = 'snapshots'  # 'experiments/train_experiment/checkpoints'
# Tensorflow specific configuration
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
latest_checkpoint = tf.train.latest_checkpoint(model_path)
if latest_checkpoint:
        print("Loading model checkpoint  ...\\n".format(latest_checkpoint))
        metagraph_file = latest_checkpoint + '.meta'
        saver = tf.train.import_meta_graph(metagraph_file)
        saver.restore(sess, latest_checkpoint)
        print("Checkpoint loaded\\n\\n")
else:
        print("No checkpoints available!\\n\\n")
image_data = sess.graph.get_tensor_by_name('input/Placeholder:0')
softmax_tensor     = sess.graph.get_tensor_by_name('output/Softmax:0')
model_exporter = exporter.Exporter(saver)
model_exporter.init(sess.graph.as_graph_def(),
                        named_graph_signatures=
                        'inputs': exporter.generic_signature('x': image_data),
                        'outputs': exporter.generic_signature('y': softmax_tensor)
    )
model_exporter.export('model', tf.constant(1), sess)

https://github.com/triagemd/model-serving

https://github.com/songbinliu/inceptionServer

https://github.com/vishh/tf-serving-base

https://github.com/tanveer941/Object-detection-sever

https://github.com/peppaseven/object_detector_server

https://github.com/arixlin/server_object_detection

https://github.com/BlueLens/bl-detect

https://github.com/jsonw99/object_detect_serving

https://github.com/movchan74/tensorflow_serving_examples

https://github.com/TensorLab/tensorfx

https://github.com/aaxwaz/Serving-TensorFlow-Model

https://github.com/hetaoaoao/tensorflow_web_deploy