model的存儲與讀取

# 本文件程序爲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，最好每段重啓控制器一次，輸出結果更準確
 
 
# Part1: 通過tf.train.Saver類實現保存和載入神經網絡模型
 
# 執行本段程序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: 加載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") # 注意此處路徑前添加"./"
    print(sess.run(result)) # [ 3.]
 
 
# Part3: 若不希望重複定義計算圖上的運算，可直接加載已經持久化的圖
 
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類也支持在保存和加載時給變量重命名
 
import tensorflow as tf
 
# 聲明的變量名稱name與已保存的模型中的變量名稱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類對象，會報錯變量找不到
# 使用一個字典dict重命名變量即可，{"已保存的變量的名稱name": 重命名變量名}
# 原來名稱name爲v1的變量現在加載到變量u1（名稱name爲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: 保存滑動平均模型
 
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: 通過變量重命名直接讀取變量的滑動平均值
 
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()函數獲取變量重命名字典
 
import tensorflow as tf
 
v = tf.Variable(0, dtype=tf.float32, name="v")
# 注意此處的變量名稱name一定要與已保存的變量名稱一致
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取自上面變量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函數將計算圖中的變量及其取值通過常量的方式保存於一個文件中
 
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())
    # 導出當前計算圖的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: 載入包含變量及其取值的模型
 
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)]