假设你已经经过上千次的迭代，并且得到了以下模型：
则从这些checkpoint文件中加载变量名和变量值代码如下：
model_dir = './ckpt-182802'
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
reader = pywrap_tensorflow.NewCheckpointReader(model_dir)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in var_to_shape_map:
print("tensor_name: ", key)
print(reader.get_tensor(key)) # Remove this is you want to print only variable names
Mnist
下面将给出一个基于卷积神经网络的手写数字识别样例：
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.framework import graph_util
log_dir = './tensorboard'
mnist = input_data.read_data_sets(train_dir="./mnist_data",one_hot=True)
if tf.gfile.Exists(log_dir):
tf.gfile.DeleteRecursively(log_dir)
tf.gfile.MakeDirs(log_dir)
#定义输入数据mnist图片大小28*28*1=784,None表示batch_size
x = tf.placeholder(dtype=tf.float32,shape=[None,28*28],name="input")
#定义标签数据,mnist共10类
y_ = tf.placeholder(dtype=tf.float32,shape=[None,10],name="y_")
#将数据调整为二维数据，w*H*c---> 28*28*1,-1表示N张
image = tf.reshape(x,shape=[-1,28,28,1])
#第一层，卷积核={5*5*1*32}，池化核={2*2*1,1*2*2*1}
w1 = tf.Variable(initial_value=tf.random_normal(shape=[5,5,1,32],stddev=0.1,dtype=tf.float32,name="w1"))
b1= tf.Variable(initial_value=tf.zeros(shape=[32]))
conv1 = tf.nn.conv2d(input=image,filter=w1,strides=[1,1,1,1],padding="SAME",name="conv1")
relu1 = tf.nn.relu(tf.nn.bias_add(conv1,b1),name="relu1")
pool1 = tf.nn.max_pool(value=relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME")
#shape={None，14,14,32}
#第二层，卷积核={5*5*32*64}，池化核={2*2*1,1*2*2*1}
w2 = tf.Variable(initial_value=tf.random_normal(shape=[5,5,32,64],stddev=0.1,dtype=tf.float32,name="w2"))
b2 = tf.Variable(initial_value=tf.zeros(shape=[64]))
conv2 = tf.nn.conv2d(input=pool1,filter=w2,strides=[1,1,1,1],padding="SAME")
relu2 = tf.nn.relu(tf.nn.bias_add(conv2,b2),name="relu2")
pool2 = tf.nn.max_pool(value=relu2,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME",name="pool2")
#shape={None，7,7,64}
#FC1
w3 = tf.Variable(initial_value=tf.random_normal(shape=[7*7*64,1024],stddev=0.1,dtype=tf.float32,name="w3"))
b3 = tf.Variable(initial_value=tf.zeros(shape=[1024]))
#关键，进行reshape
input3 = tf.reshape(pool2,shape=[-1,7*7*64],name="input3")
fc1 = tf.nn.relu(tf.nn.bias_add(value=tf.matmul(input3,w3),bias=b3),name="fc1")
#shape={None，1024}
#FC2
w4 = tf.Variable(initial_value=tf.random_normal(shape=[1024,10],stddev=0.1,dtype=tf.float32,name="w4"))
b4 = tf.Variable(initial_value=tf.zeros(shape=[10]))
fc2 = tf.nn.bias_add(value=tf.matmul(fc1,w4),bias=b4,name="logit")
#shape={None，10}
#定义交叉熵损失
# 使用softmax将NN计算输出值表示为概率
y = tf.nn.softmax(fc2,name="out")
# 定义交叉熵损失函数
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=fc2,labels=y_)
loss = tf.reduce_mean(cross_entropy)
tf.summary.scalar('Cross_Entropy',loss)
#定义solver
train = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss=loss)
for var in tf.trainable_variables():
print var
#train = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss=loss)
#定义正确值,判断二者下标index是否相等
correct_predict = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
#定义如何计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_predict,dtype=tf.float32),name="accuracy")
tf.summary.scalar('Training_ACC',accuracy)
#定义初始化op
merged = tf.summary.merge_all()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
#训练NN
with tf.Session() as session:
session.run(fetches=init)
writer = tf.summary.FileWriter(log_dir,session.graph) #定义记录日志的位置
for i in range(0,500):
xs, ys = mnist.train.next_batch(100)
session.run(fetches=train,feed_dict={x:xs,y_:ys})
if i%10 == 0:
train_accuracy,summary = session.run(fetches=[accuracy,merged],feed_dict={x:xs,y_:ys})
writer.add_summary(summary,i)
print(i,"accuracy=",train_accuracy)
'''
#训练完成后，将网络中的权值转化为常量，形成常量graph，注意：需要x与label
constant_graph = graph_util.convert_variables_to_constants(sess=session,
input_graph_def=session.graph_def,
output_node_names=['out','y_','input'])
#将带权值的graph序列化，写成pb文件存储起来
with tf.gfile.FastGFile("lenet.pb", mode='wb') as f:
f.write(constant_graph.SerializeToString())
'''
saver.save(session,'./ckpt')
补充：查看tensorflow产生的checkpoint文件内容的方法
tensorflow在保存权重模型时多使用tf.train.Saver().save 函数进行权重保存，保存的ckpt文件无法直接打开，但tensorflow提供了相关函数 tf.train.NewCheckpointReader 可以对ckpt文件进行权重查看。
import os
from tensorflow.python import pywrap_tensorflow
checkpoint_path = os.path.join('modelckpt', "fc_nn_model")
# Read data from checkpoint file
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map = reader.get_variable_to_shape_map()
# Print tensor name and values
for key in var_to_shape_map:
print("tensor_name: ", key)
print(reader.get_tensor(key))
其中‘modelckpt'是存放.ckpt文件的文件夹，"fc_nn_model"是文件名，如下图所示。
var_to_shape_map是一个字典，其中的键值是变量名，对应的值是该变量的形状，如{‘LSTM_input/bias_LSTM/Adam_1': [128]}。
想要查看某变量值时，需要调用get_tensor函数，即输入以下代码：
reader.get_tensor('LSTM_input/bias_LSTM/Adam_1')
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。