摘要：这篇文章将讲解TensorFlow如何保存变量和神经网络参数，通过Saver保存神经网络，再通过Restore调用训练好的神经网络。
本文分享自华为云社区 《[Python人工智能] 十一.Tensorflow如何保存神经网络参数 丨【百变AI秀】》，作者：eastmount。
一、保存变量
通过tf.Variable()定义权重和偏置变量，然后调用tf.train.Saver()存储变量，将数据保存至本地“my_net/save_net.ckpt”文件中。
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 2 20:04:57 2020
@author: xiuzhang Eastmount CSDN
"""
import tensorflow as tf
import numpy as np
#---------------------------------------保存文件---------------------------------------
W = tf.Variable([[1,2,3], [3,4,5]], dtype=tf.float32, name='weights') #2行3列的数据
b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases')
# 初始化
init = tf.initialize_all_variables()
# 定义saver 存储各种变量
saver = tf.train.Saver()
# 使用Session运行初始化
with tf.Session() as sess:
sess.run(init)
# 保存 官方保存格式为ckpt
save_path = saver.save(sess, "my_net/save_net.ckpt")
print("Save to path:", save_path)1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.“Save to path:my_net/save_net.ckpt”保存成功如下图所示：
打开内容如下图所示：
接着定义标记变量train，通过Restore操作使用我们保存好的变量。注意，在Restore时需要定义相同的dtype和shape，不需要再定义init。最后直接通过 saver.restore(sess, “my_net/save_net.ckpt”) 提取保存的变量并输出即可。
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 2 20:04:57 2020
@author: xiuzhang Eastmount CSDN
"""
import tensorflow as tf
import numpy as np
# 标记变量
train = False
#---------------------------------------保存文件---------------------------------------
# Save
if train==True:
# 定义变量
W = tf.Variable([[1,2,3], [3,4,5]], dtype=tf.float32, name='weights') #2行3列的数据
b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases')
# 初始化
init = tf.global_variables_initializer()
# 定义saver 存储各种变量
saver = tf.train.Saver()
# 使用Session运行初始化
with tf.Session() as sess:
sess.run(init)
# 保存 官方保存格式为ckpt
save_path = saver.save(sess, "my_net/save_net.ckpt")
print("Save to path:", save_path)
#---------------------------------------Restore变量-------------------------------------
# Restore
if train==False:
# 记住在Restore时定义相同的dtype和shape
# redefine the same shape and same type for your variables
W = tf.Variable(np.arange(6).reshape((2,3)), dtype=tf.float32, name='weights') #空变量
b = tf.Variable(np.arange(3).reshape((1,3)), dtype=tf.float32, name='biases') #空变量
# Restore不需要定义init
saver = tf.train.Saver()
with tf.Session() as sess:
# 提取保存的变量
saver.restore(sess, "my_net/save_net.ckpt")
# 寻找相同名字和标识的变量并存储在W和b中
print("weights", sess.run(W))
print("biases", sess.run(b))1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.运行代码，如果报错“NotFoundError: Restoring from checkpoint failed. This ismost likely due to a Variable name or other graph key that is missing from thecheckpoint. Please ensure that you have not altered the graph expected based onthe checkpoint. ”，则需要重置Spyder即可。
最后输出之前所保存的变量，weights为 [[1,2,3],[3,4,5]]，偏置为 [[1,2,3]]。
二、保存神经网络
那么，TensorFlow如何保存我们的神经网络框架呢？我们需要把整个网络训练好再进行保存，其方法和上面类似，完整代码如下：
"""
Created on Sun Dec 29 19:21:08 2019
@author: xiuzhang Eastmount CSDN
"""
import os
import glob
import cv2
import numpy as np
import tensorflow as tf
# 定义图片路径
path = 'photo/'
#---------------------------------第一步 读取图像-----------------------------------
def read_img(path):
cate = [path + x for x in os.listdir(path) if os.path.isdir(path + x)]
imgs = []
labels = []
fpath = []
for idx, folder in enumerate(cate):
# 遍历整个目录判断每个文件是不是符合
for im in glob.glob(folder + '/*.jpg'):
#print('reading the images:%s' % (im))
img = cv2.imread(im) #调用opencv库读取像素点
img = cv2.resize(img, (32, 32)) #图像像素大小一致
imgs.append(img) #图像数据
labels.append(idx) #图像类标
fpath.append(path+im) #图像路径名
#print(path+im, idx)
return np.asarray(fpath, np.string_), np.asarray(imgs, np.float32), np.asarray(labels, np.int32)
# 读取图像
fpaths, data, label = read_img(path)
print(data.shape) # (1000, 256, 256, 3)
# 计算有多少类图片
num_classes = len(set(label))
print(num_classes)
# 生成等差数列随机调整图像顺序
num_example = data.shape[0]
arr = np.arange(num_example)
np.random.shuffle(arr)
data = data[arr]
label = label[arr]
fpaths = fpaths[arr]
# 拆分训练集和测试集 80%训练集 20%测试集
ratio = 0.8
s = np.int(num_example * ratio)
x_train = data[:s]
y_train = label[:s]
fpaths_train = fpaths[:s]
x_val = data[s:]
y_val = label[s:]
fpaths_test = fpaths[s:]
print(len(x_train),len(y_train),len(x_val),len(y_val)) #800 800 200 200
print(y_val)
#---------------------------------第二步 建立神经网络-----------------------------------
# 定义Placeholder
xs = tf.placeholder(tf.float32, [None, 32, 32, 3]) #每张图片32*32*3个点
ys = tf.placeholder(tf.int32, [None]) #每个样本有1个输出
# 存放DropOut参数的容器
drop = tf.placeholder(tf.float32) #训练时为0.25 测试时为0
# 定义卷积层 conv0
conv0 = tf.layers.conv2d(xs, 20, 5, activation=tf.nn.relu) #20个卷积核 卷积核大小为5 Relu激活
# 定义max-pooling层 pool0
pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2]) #pooling窗口为2x2 步长为2x2
print("Layer0：\n", conv0, pool0)
# 定义卷积层 conv1
conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu) #40个卷积核 卷积核大小为4 Relu激活
# 定义max-pooling层 pool1
pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2]) #pooling窗口为2x2 步长为2x2
print("Layer1：\n", conv1, pool1)
# 将3维特征转换为1维向量
flatten = tf.layers.flatten(pool1)
# 全连接层 转换为长度为400的特征向量
fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu)
print("Layer2：\n", fc)
# 加上DropOut防止过拟合
dropout_fc = tf.layers.dropout(fc, drop)
# 未激活的输出层
logits = tf.layers.dense(dropout_fc, num_classes)
print("Output：\n", logits)
# 定义输出结果
predicted_labels = tf.arg_max(logits, 1)
#---------------------------------第三步 定义损失函数和优化器---------------------------------
# 利用交叉熵定义损失
losses = tf.nn.softmax_cross_entropy_with_logits(
labels = tf.one_hot(ys, num_classes), #将input转化为one-hot类型数据输出
logits = logits)
# 平均损失
mean_loss = tf.reduce_mean(losses)
# 定义优化器 学习效率设置为0.0001
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(losses)
#------------------------------------第四步 模型训练和预测-----------------------------------
# 用于保存和载入模型
saver = tf.train.Saver()
# 训练或预测
train = False
# 模型文件路径
model_path = "model/image_model"
with tf.Session() as sess:
if train:
print("训练模式")
# 训练初始化参数
sess.run(tf.global_variables_initializer())
# 定义输入和Label以填充容器 训练时dropout为0.25
train_feed_dict = {
xs: x_train,
ys: y_train,
drop: 0.25
}
# 训练学习1000次
for step in range(1000):
_, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict)
if step % 50 == 0: #每隔50次输出一次结果
print("step = {}\t mean loss = {}".format(step, mean_loss_val))
# 保存模型
saver.save(sess, model_path)
print("训练结束，保存模型到{}".format(model_path))
else:
print("测试模式")
# 测试载入参数
saver.restore(sess, model_path)
print("从{}载入模型".format(model_path))
# label和名称的对照关系
label_name_dict = {
0: "人类",
1: "沙滩",
2: "建筑",
3: "公交",
4: "恐龙",
5: "大象",
6: "花朵",
7: "野马",
8: "雪山",
9: "美食"
}
# 定义输入和Label以填充容器 测试时dropout为0
test_feed_dict = {
xs: x_val,
ys: y_val,
drop: 0
}
# 真实label与模型预测label
predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict)
for fpath, real_label, predicted_label in zip(fpaths_test, y_val, predicted_labels_val):
# 将label id转换为label名
real_label_name = label_name_dict[real_label]
predicted_label_name = label_name_dict[predicted_label]
print("{}\t{} => {}".format(fpath, real_label_name, predicted_label_name))
# 评价结果
print("正确预测个数:", sum(y_val==predicted_labels_val))
print("准确度为:", 1.0*sum(y_val==predicted_labels_val) / len(y_val))1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99.100.101.102.103.104.105.106.107.108.109.110.111.112.113.114.115.116.117.118.119.120.121.122.123.124.125.126.127.128.129.130.131.132.133.134.135.136.137.138.139.140.141.142.143.144.145.146.147.148.149.150.151.152.153.154.155.156.157.158.159.160.161.162.163.164.165.166.167.核心步骤为：
saver = tf.train.Saver()
model_path = "model/image_model"
with tf.Session() as sess:
if train:
#保存神经网络
sess.run(tf.global_variables_initializer())
for step in range(1000):
_, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict)
if step % 50 == 0:
print("step = {}\t mean loss = {}".format(step, mean_loss_val))
saver.save(sess, model_path)
else:
#载入神经网络
saver.restore(sess, model_path)
predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict)
for fpath, real_label, predicted_label in zip(fpaths_test, y_val, predicted_labels_val):
real_label_name = label_name_dict[real_label]
predicted_label_name = label_name_dict[predicted_label]
print("{}\t{} => {}".format(fpath, real_label_name, predicted_label_name)) 1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.预测输出结果如下图所示，最终预测正确181张图片，准确度为0.905。相比之前机器学习KNN的0.500有非常高的提升。
测试模式 INFO:tensorflow:Restoring parameters from model/image_model 从model/image_model载入模型 b'photo/photo/3\\335.jpg' 公交 => 公交 b'photo/photo/1\\129.jpg' 沙滩 => 沙滩 b'photo/photo/7\\740.jpg' 野马 => 野马 b'photo/photo/5\\564.jpg' 大象 => 大象 ... b'photo/photo/9\\974.jpg' 美食 => 美食 b'photo/photo/2\\220.jpg' 建筑 => 公交 b'photo/photo/9\\912.jpg' 美食 => 美食 b'photo/photo/4\\459.jpg' 恐龙 => 恐龙 b'photo/photo/5\\525.jpg' 大象 => 大象 b'photo/photo/0\\44.jpg' 人类 => 人类 正确预测个数: 181 准确度为: 0.9051.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.
