TensorFlow模型儲存和提取方法

# 本檔案程式為配合教材及學習進度漸進進行，請按照註釋分段執行
# 執行時要注意IDE的當前工作過路徑，最好每段重啟控制器一次，輸出結果更準確


# Part1: 通過tf.train.Saver類實現儲存和載入神經網路模型

# 執行本段程式時注意當前的工作路徑
import tensorflow as tf

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

saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver.save(sess, "Model/model.ckpt")


# Part2: 載入TensorFlow模型的方法

import tensorflow as tf

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

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess, "./Model/model.ckpt") # 注意此處路徑前新增"./"
    print(sess.run(result)) # [ 3.]


# Part3: 若不希望重複定義計算圖上的運算，可直接載入已經持久化的圖

import tensorflow as tf

saver = tf.train.import_meta_graph("Model/model.ckpt.meta")

with tf.Session() as sess:
    saver.restore(sess, "./Model/model.ckpt") # 注意路徑寫法
    print(sess.run(tf.get_default_graph().get_tensor_by_name("add:0"))) # [ 3.]


# Part4： tf.train.Saver類也支援在儲存和載入時給變數重新命名

import tensorflow as tf

# 宣告的變數名稱name與已儲存的模型中的變數名稱name不一致
u1 = tf.Variable(tf.constant(1.0, shape=[1]), name="other-v1")
u2 = tf.Variable(tf.constant(2.0, shape=[1]), name="other-v2")
result = u1 + u2

# 若直接生命Saver類物件，會報錯變數找不到
# 使用一個字典dict重新命名變數即可，{"已儲存的變數的名稱name": 重新命名變數名}
# 原來名稱name為v1的變數現在載入到變數u1（名稱name為other-v1）中
saver = tf.train.Saver({"v1": u1, "v2": u2})

with tf.Session() as sess:
    saver.restore(sess, "./Model/model.ckpt")
    print(sess.run(result)) # [ 3.]


# Part5: 儲存滑動平均模型

import tensorflow as tf

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

saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(tf.assign(v, 10))
    sess.run(maintain_averages_op)
    saver.save(sess, "Model/model_ema.ckpt")
    print(sess.run([v, ema.average(v)])) # [10.0, 0.099999905]


# Part6: 通過變數重新命名直接讀取變數的滑動平均值

import tensorflow as tf

v = tf.Variable(0, dtype=tf.float32, name="v")
saver = tf.train.Saver({"v/ExponentialMovingAverage": v})

with tf.Session() as sess:
    saver.restore(sess, "./Model/model_ema.ckpt")
    print(sess.run(v)) # 0.0999999


# Part7: 通過tf.train.ExponentialMovingAverage的variables_to_restore()函式獲取變數重新命名字典

import tensorflow as tf

v = tf.Variable(0, dtype=tf.float32, name="v")
# 注意此處的變數名稱name一定要與已儲存的變數名稱一致
ema = tf.train.ExponentialMovingAverage(0.99)
print(ema.variables_to_restore())
# {'v/ExponentialMovingAverage': <tf.Variable 'v:0' shape=() dtype=float32_ref>}
# 此處的v取自上面變數v的名稱name="v"

saver = tf.train.Saver(ema.variables_to_restore())

with tf.Session() as sess:
    saver.restore(sess, "./Model/model_ema.ckpt")
    print(sess.run(v)) # 0.0999999


# Part8: 通過convert_variables_to_constants函式將計算圖中的變數及其取值通過常量的方式儲存於一個檔案中

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

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    # 匯出當前計算圖的GraphDef部分，即從輸入層到輸出層的計算過程部分
    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("Model/combined_model.pb", 'wb') as f:
        f.write(output_graph_def.SerializeToString())


# Part9: 載入包含變數及其取值的模型

import tensorflow as tf
from tensorflow.python.platform import gfile

with tf.Session() as sess:
    model_filename = "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)]