Tensorflow如何加载变量名和变量值

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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 tffrom tensorflow.python import pywrap_tensorflowreader = 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 tffrom tensorflow.examples.tutorials.mnist import input_datafrom tensorflow.python.framework import graph_utillog_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_sizex = 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}#FC1w3 = 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reshapeinput3 = 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}#FC2w4 = 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solvertrain = 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opmerged = tf.summary.merge_all()init = tf.global_variables_initializer()saver = tf.train.Saver()#训练NNwith 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 == 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 osfrom tensorflow.python import pywrap_tensorflowcheckpoint_path = os.path.join('modelckpt', "fc_nn_model")# Read data from checkpoint filereader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)var_to_shape_map = reader.get_variable_to_shape_map()# Print tensor name and valuesfor 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。

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')

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