01 ckpt文件保存方法

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts05.09.py # ckpt文件保存方法

import tensorflow as tf

# 1. 保存计算两个变量和的模型
v1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "v1")
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "v2")
result = v1 + v2

init_op = tf.global_variables_initializer()
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init_op)
    # 需要在本python脚本文件下存在Saved_model目录
    # 否则提示错误 ValueError: Parent directory of Saved_model/model.ckpt doesn't exist, can't save.
    saver.save(sess, "Saved_model/model.ckpt")

# 2. 加载保存了两个变量和的模型
with tf.Session() as sess:
    saver.restore(sess, "Saved_model/model.ckpt")
    print(sess.run(result)) # [3.]

# 3. 直接加载持久化的图
saver = tf.train.import_meta_graph("Saved_model/model.ckpt.meta")
with tf.Session() as sess:
    saver.restore(sess, "Saved_model/model.ckpt")
    print(sess.run(tf.get_default_graph().get_tensor_by_name("add:0"))) # [3.]

# 4. 变量重命名
v1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "other-v1")
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "other-v2")
saver = tf.train.Saver({"v1": v1, "v2": v2})

02 滑动平均类的保存

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts05.10.py # 滑动平均类的保存

import tensorflow as tf

# 1. 使用滑动平均
v = tf.Variable(0, dtype=tf.float32, name="v")
for variables in tf.global_variables():
    print(variables.name)
'''
v:0
'''
ema = tf.train.ExponentialMovingAverage(0.99)
maintain_averages_op = ema.apply(tf.global_variables())
for variables in tf.global_variables():
    print(variables.name)
'''
v:0
v/ExponentialMovingAverage:0
'''
# 2. 保存滑动平均模型
saver = tf.train.Saver()
with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    sess.run(tf.assign(v, 10))
    sess.run(maintain_averages_op)
    # 保存的时候会将v:0  v/ExponentialMovingAverage:0这两个变量都存下来。
    saver.save(sess, "Saved_model/model2.ckpt")
    print(sess.run([v, ema.average(v)]))
'''
[10.0, 0.099999905]
'''
# 3. 加载滑动平均模型
v = tf.Variable(0, dtype=tf.float32, name="v")

# 通过变量重命名将原来变量v的滑动平均值直接赋值给v。
saver = tf.train.Saver({"v/ExponentialMovingAverage": v})
with tf.Session() as sess:
    saver.restore(sess, "Saved_model/model2.ckpt")
    print(sess.run(v))
'''
0.0999999
'''

03 variables_to_restore函数的使用样例

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts05.11.py # variables_to_restore函数的使用样例

import tensorflow as tf
v = tf.Variable(0, dtype=tf.float32, name="v")
ema = tf.train.ExponentialMovingAverage(0.99)
print(ema.variables_to_restore())
'''
{'v/ExponentialMovingAverage': <tensorflow.python.ops.variables.Variable object at 0x000001F0CFB260F0>}
'''
saver = tf.train.Saver({"v/ExponentialMovingAverage": v})
with tf.Session() as sess:
    saver.restore(sess, "Saved_model/model2.ckpt")
    print(sess.run(v))
'''
0.0999999
'''

04 pb文件保存方法

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts05.12.py # pb文件保存方法

import tensorflow as tf

# 1. pb文件的保存方法
import tensorflow as tf
from tensorflow.python.framework import graph_util

v1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "v1")
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "v2")
result = v1 + v2

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    graph_def = tf.get_default_graph().as_graph_def()
    output_graph_def = graph_util.convert_variables_to_constants(sess, graph_def, ['add'])
    with tf.gfile.GFile("Saved_model/combined_model.pb", "wb") as f:
           f.write(output_graph_def.SerializeToString())

# 2. 加载pb文件
from tensorflow.python.platform import gfile

with tf.Session() as sess:
    model_filename = "Saved_model/combined_model.pb"

    with gfile.FastGFile(model_filename, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    result = tf.import_graph_def(graph_def, return_elements=["add:0"])
    print(sess.run(result)) # [array([ 3.], dtype=float32)]