tensorflow模型加载与保存 tensorflow保存模型方法

Tensorflow训练的模型，如何保存与载入？

目的：学习tensorflow框架的DNN，掌握如何将tensorflow训练得到的模型保存并载入，做预测？

内容：

1、tensorflow模型保存与载入的两种方法

2、实例分析1——线性回归

3、实例分析2——mnist分类

一、tensorflow模型保存与载入的两种方法

参考网址：，tensorflow模型保存与载入的两种方法：

方法一：

保存模型（定义变量 + 使用saver.save()方法保存）

import tensorflow as tf
import numpy as np

W = tf.Variable([[1,1,1],[2,2,2]],dtype = tf.float32,name='w')
b = tf.Variable([[0,1,2]],dtype = tf.float32,name='b')

init = tf.initialize_all_variables()
saver = tf.train.Saver()
with tf.Session() as sess:
        sess.run(init)
        save_path = saver.save(sess,"save/model.ckpt")

载入模型（定义变量 + 使用saver.restore()方法载入）

import tensorflow as tf
import numpy as np

W = tf.Variable(tf.truncated_normal(shape=(2,3)),dtype = tf.float32,name='w')
b = tf.Variable(tf.truncated_normal(shape=(1,3)),dtype = tf.float32,name='b')

saver = tf.train.Saver()
with tf.Session() as sess:
        saver.restore(sess,"save/model.ckpt")

该方法的缺点：在使用模型时，必须把模型的结构重新定义一次，然后载入对应名字的变量的值。但是很多时候我们都更希望能够读取一个文件然后就直接使用模型，而不是还要把模型重新定义一遍。所以就需要使用另一种方法。

方法二：不重新定义网络结构的方法

具体地址见点击打开链接

二、实例分析1——线性回归

python代码如下：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
    if len(a) < w: 
        return a[:]    
    return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]

#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x，但是加入了噪声
#显示模拟数据点
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")

# 前向结构
z = tf.multiply(X, W)+ b
tf.summary.histogram("z",z)

#反向优化
cost =tf.reduce_mean( tf.square(Y - z))
tf.summary.scalar('loss_function',cost)

learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent

# 初始化变量
init = tf.global_variables_initializer()
# 训练参数
training_epochs = 20
display_step = 2

# 启动session
with tf.Session() as sess:
    sess.run(init)

    merged_summary_op = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter('log/mnist_with_summaries',sess.graph)

    # Fit all training data
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={X: x, Y: y})

        #生成summary
        summary_str = sess.run(merged_summary_op,feed_dict={X:x,Y:y})
        summary_writer.add_summary(summary_str,epoch) #将summary写入文件
        #显示训练中的详细信息
        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
            if not (loss == "NA" ):
                plotdata["batchsize"].append(epoch)
                plotdata["loss"].append(loss)

    print (" Finished!")
    print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b))
    #print ("cost:",cost.eval（{X: train_X, Y: train_Y}))

    #图形显示
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()
    
    plotdata["avgloss"] = moving_average(plotdata["loss"])
    plt.figure(1)
    plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
    plt.xlabel('Minibatch number')
    plt.ylabel('Loss')
    plt.title('Minibatch run vs. Training loss')
    plt.show()
    print ("x=0.2，z=", sess.run(z, feed_dict={X: 0.2}))

运行结果：

三、实例分析2——mnist分类

python代码如下：

import tensorflow as tf #导入tensorflow库
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import pylab 

tf.reset_default_graph()
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 数字=> 10 classes

# Set model weights
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.zeros([10]))

# 构建模型
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax分类

# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

#参数设置
learning_rate = 0.01
# 使用梯度下降优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

training_epochs = 25
batch_size = 100
display_step = 1
saver = tf.train.Saver()
model_path = "log/521model.ckpt"

# 启动session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())# Initializing OP

    # 启动循环开始训练
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # 遍历全部数据集
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
                                                          y: batch_ys})
            # Compute average loss
            avg_cost += c / total_batch
        # 显示训练中的详细信息
        if (epoch+1) % display_step == 0:
            print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

    print( " Finished!")

    # 测试 model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print ("Accuracy:", accuracy.eval（{x: mnist.test.images, y: mnist.test.labels}))

    # Save model weights to disk
    save_path = saver.save(sess, model_path)
    print("Model saved in file: %s" % save_path)



#读取模型
print("Starting 2nd session...")
with tf.Session() as sess:
    # Initialize variables
    sess.run(tf.global_variables_initializer())
    # Restore model weights from previously saved model
    saver.restore(sess, model_path)
    
     # 测试 model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # 计算准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print ("Accuracy:", accuracy.eval（{x: mnist.test.images, y: mnist.test.labels}))
    
    output = tf.argmax(pred, 1)
    batch_xs, batch_ys = mnist.train.next_batch(2)


    outputval,predv = sess.run([output,pred], feed_dict={x: batch_xs})
    print(outputval,predv,batch_ys)

    im = batch_xs[0]
    im = im.reshape(-1,28)
    pylab.imshow(im)
    pylab.show()
    
    im = batch_xs[1]
    im = im.reshape(-1,28)
    pylab.imshow(im)
    pylab.show()

运行结果：

Epoch: 0001 cost= 7.752772305
Epoch: 0002 cost= 4.151113472
Epoch: 0003 cost= 2.902867300
Epoch: 0004 cost= 2.292819615
Epoch: 0005 cost= 1.945046414
Epoch: 0006 cost= 1.721682055
Epoch: 0007 cost= 1.565224952
Epoch: 0008 cost= 1.448184885
Epoch: 0009 cost= 1.357409785
Epoch: 0010 cost= 1.283956942
Epoch: 0011 cost= 1.223152844
Epoch: 0012 cost= 1.171679115
Epoch: 0013 cost= 1.127339950
Epoch: 0014 cost= 1.089194359
Epoch: 0015 cost= 1.055257367
Epoch: 0016 cost= 1.025059551
Epoch: 0017 cost= 0.997867818
Epoch: 0018 cost= 0.973305143
Epoch: 0019 cost= 0.951017423
Epoch: 0020 cost= 0.930552574
Epoch: 0021 cost= 0.911731513
Epoch: 0022 cost= 0.894192883
Epoch: 0023 cost= 0.878128686
Epoch: 0024 cost= 0.862873784
Epoch: 0025 cost= 0.848758641
 Finished!
Accuracy: 0.8355
Model saved in file: log/521model.ckpt
Starting 2nd session...
Accuracy: 0.8355
[2 6] [[  4.44788748e-05   5.84178214e-13   9.99922991e-01   1.38609546e-09
    4.55205260e-08   5.39752136e-06   2.67073501e-05   2.82684276e-16
    4.42017324e-07   2.21145666e-14]
 [  1.80836395e-08   5.05934682e-18   5.30333818e-05   5.56881845e-14
    2.38929709e-10   7.70143487e-08   9.99946833e-01   5.28569544e-09
    9.02450684e-11   3.62926039e-14]] [[ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]]

import tensorflow as tf #导入tensorflow库
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import pylab

########################################################################
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 若不添加，中文无法在图中显示
# import matplotlib
# matplotlib.rcParams['axes.unicode_minus']=False # 若不添加，无法在图中显示负号
###########################################################################

tf.reset_default_graph()
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 数字=> 10 classes

# Set model weights
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.zeros([10]))

# 构建模型
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax分类

# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

#参数设置
learning_rate = 0.01
# 使用梯度下降优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

saver = tf.train.Saver()
model_path = "log/521model.ckpt"
###############################################################################
# 读取模型
print("Starting 2nd session...")
with tf.Session() as sess:
    # Initialize variables
    sess.run(tf.global_variables_initializer())
    # Restore model weights from previously saved model
    saver.restore(sess, model_path)

    # # 测试 model
    # correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # # 计算准确率
    # accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    # print("Accuracy:", accuracy.eval（{x: mnist.test.images, y: mnist.test.labels}))

    output = tf.argmax(pred, 1)
    batch_xs, batch_ys = mnist.train.next_batch(2)

    outputval, predv = sess.run([output, pred], feed_dict={x: batch_xs})
    # print(outputval, predv, batch_ys)
    #######################################################################
    print(outputval)
    pylab.subplot(121)
    im = batch_xs[0]
    im = im.reshape(-1, 28)
    pylab.title('该图片中的数字为:'+ str(outputval[0]))
    pylab.imshow(im)

    pylab.subplot(122)
    im = batch_xs[1]
    im = im.reshape(-1, 28)
    pylab.title('该图片中的数字为:' + str(outputval[1]))
    pylab.imshow(im)
    pylab.show()

运行结果：

Starting 2nd session...
[6 1]