import tensorflow as tf
#保存计算两个变量和的模型
v1 = tf.Variable(tf.random_normal([1], stddev=1, seed=1))
v2 = tf.Variable(tf.random_normal([1], stddev=1, seed=1))
result = v1 + v2init_op = tf.global_variables_initializer()
saver = tf.train.Saver()with tf.Session() as sess:sess.run(init_op)saver.save(sess, "Saved_model/model.ckpt")
#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)INFO:tensorflow:Restoring parameters from Saved_model/model.ckpt
[-1.6226364]
#直接加载持久化的图。因为之前没有导出v3，所以这里会报错
saver = tf.train.import_meta_graph("Saved_model/model.ckpt.meta")
v3 = tf.Variable(tf.random_normal([1], stddev=1, seed=1))with tf.Session() as sess:saver.restore(sess, "Saved_model/model.ckpt")print sess.run(v1) print sess.run(v2) print sess.run(v3)
INFO:tensorflow:Restoring parameters from Saved_model/model.ckpt
[-0.81131822]
[-0.81131822]# 变量重命名，这样可以通过字典将模型保存时的变量名和需要加载的变量联系起来
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})

View Code

import tensorflow as tf
#使用滑动平均
v = tf.Variable(0, dtype=tf.float32, name="v")
for variables in tf.global_variables(): print variables.nameema = 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:0
v/ExponentialMovingAverage:0#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]#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)
INFO:tensorflow:Restoring parameters from Saved_model/model2.ckpt
0.0999999

View Code

import tensorflow as tf
v = tf.Variable(0, dtype=tf.float32, name="v")
ema = tf.train.ExponentialMovingAverage(0.99)
print ema.variables_to_restore()#等同于saver = tf.train.Saver(ema.variables_to_restore())
saver = tf.train.Saver({"v/ExponentialMovingAverage": v})
with tf.Session() as sess:saver.restore(sess, "Saved_model/model2.ckpt")print sess.run(v)
{u'v/ExponentialMovingAverage': <tf.Variable 'v:0' shape=() dtype=float32_ref>}

#pb文件的保存方法
import tensorflow as tf
from tensorflow.python.framework import graph_utilv1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "v1")
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "v2")
result = v1 + v2init_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())INFO:tensorflow:Froze 2 variables.
Converted 2 variables to const ops.
------------------------------------------------------------------------
#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)]

import tensorflow as tf
#1. 定义神经网络结构相关的参数
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500
#2. 通过tf.get_variable函数来获取变量
def get_weight_variable(shape, regularizer):weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(weights))return weights
#3. 定义神经网络的前向传播过程
def inference(input_tensor, regularizer):with tf.variable_scope('layer1'):weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)with tf.variable_scope('layer2'):weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))layer2 = tf.matmul(layer1, weights) + biasesreturn layer2

mnist_train
#1. 定义神经网络结构相关的参数
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "MNIST_model/"
MODEL_NAME = "mnist_model"
#2. 定义训练过程
def train(mnist):# 定义输入输出placeholder。x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)y = mnist_inference.inference(x, regularizer)global_step = tf.Variable(0, trainable=False)# 定义损失函数、学习率、滑动平均操作以及训练过程。variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)variables_averages_op = variable_averages.apply(tf.trainable_variables())cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))cross_entropy_mean = tf.reduce_mean(cross_entropy)loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,staircase=True)train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)with tf.control_dependencies([train_step, variables_averages_op]):train_op = tf.no_op(name='train')# 初始化TensorFlow持久化类。saver = tf.train.Saver()with tf.Session() as sess:tf.global_variables_initializer().run()for i in range(TRAINING_STEPS):xs, ys = mnist.train.next_batch(BATCH_SIZE)_, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})if i % 1000 == 0:print("After %d training step(s), loss on training batch is %g." % (step, loss_value))saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)
#3. 主程序入口
def main(argv=None):mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)train(mnist)if __name__ == '__main__':main()
-------------------------------------------------------------------------
Extracting ../../../datasets/MNIST_data/train-images-idx3-ubyte.gz
Extracting ../../../datasets/MNIST_data/train-labels-idx1-ubyte.gz
Extracting ../../../datasets/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting ../../../datasets/MNIST_data/t10k-labels-idx1-ubyte.gz
After 1 training step(s), loss on training batch is 3.05851.
After 1001 training step(s), loss on training batch is 0.207949.
After 2001 training step(s), loss on training batch is 0.214515.
After 3001 training step(s), loss on training batch is 0.237391.
After 4001 training step(s), loss on training batch is 0.115064.
After 5001 training step(s), loss on training batch is 0.103093.
After 6001 training step(s), loss on training batch is 0.133556.
....

import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
import mnist_train
#1. 每10秒加载一次最新的模型
# 加载的时间间隔。
EVAL_INTERVAL_SECS = 10def evaluate(mnist):with tf.Graph().as_default() as g:x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}y = mnist_inference.inference(x, None)correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))variable_averages = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)variables_to_restore = variable_averages.variables_to_restore()saver = tf.train.Saver(variables_to_restore)while True:with tf.Session() as sess:ckpt = tf.train.get_checkpoint_state(mnist_train.MODEL_SAVE_PATH)if ckpt and ckpt.model_checkpoint_path:saver.restore(sess, ckpt.model_checkpoint_path)global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]accuracy_score = sess.run(accuracy, feed_dict=validate_feed)print("After %s training step(s), validation accuracy = %g" % (global_step, accuracy_score))else:print('No checkpoint file found')returntime.sleep(EVAL_INTERVAL_SECS)
#2主程序
def main(argv=None):mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)evaluate(mnist)if __name__ == '__main__':main()
------------------------------------------------------------------------
Extracting ../../../datasets/MNIST_data/train-images-idx3-ubyte.gz
Extracting ../../../datasets/MNIST_data/train-labels-idx1-ubyte.gz
Extracting ../../../datasets/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting ../../../datasets/MNIST_data/t10k-labels-idx1-ubyte.gz
INFO:tensorflow:Restoring parameters from MNIST_model/mnist_model-4001
After 4001 training step(s), validation accuracy = 0.9826
INFO:tensorflow:Restoring parameters from MNIST_model/mnist_model-5001
After 5001 training step(s), validation accuracy = 0.983
INFO:tensorflow:Restoring parameters from MNIST_model/mnist_model-6001
After 6001 training step(s), validation accuracy = 0.9832
INFO:tensorflow:Restoring parameters from MNIST_model/mnist_model-7001
After 7001 training step(s), validation accuracy = 0.9834...