Tensorflow保存神经网络参数有妙招（Saver和Restore）

幽映每白日，清辉照衣裳。这篇文章主要讲述Tensorflow保存神经网络参数有妙招：Saver和Restore相关的知识，希望能为你提供帮助。
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 Jan2 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)

“Save to path:my_net/save_net.ckpt”保存成功如下图所示：

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打开内容如下图所示：

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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 Jan2 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))

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。

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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]]。

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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 = https://www.songbingjia.com/android/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))

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))

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。

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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: 0.905

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