AI - TensorFlow - 示例05：保存和恢复模型
官网示例：https://www.tensorflow.org/tutorials/keras/save_and_restore_models
在训练期间保存检查点
在训练期间或训练结束时自动保存检查点。
权重存储在检查点格式的文件集合中，这些文件仅包含经过训练的权重（采用二进制格式）。
可以使用经过训练的模型，而无需重新训练该模型，或从上次暂停的地方继续训练，以防训练过程中断
- 检查点回调用法：创建检查点回调，训练模型并将ModelCheckpoint回调传递给该模型，得到检查点文件集合，用于分享权重
- 检查点回调选项：该回调提供了多个选项，用于为生成的检查点提供独一无二的名称，以及调整检查点创建频率。
手动保存权重
使用 Model.save_weights 方法即可手动保存权重
保存整个模型
整个模型可以保存到一个文件中，其中包含权重值、模型配置（架构）、优化器配置。
可以为模型设置检查点，并稍后从完全相同的状态继续训练，而无需访问原始代码。
Keras通过检查架构来保存模型，使用HDF5标准提供基本的保存格式。
特别注意：
- 目前无法保存TensorFlow优化器（来自tf.train）。
- 使用此类优化器时，需要在加载模型后对其进行重新编译，使优化器的状态变松散。
MNIST数据集
MNIST（Mixed National Institute of Standards and Technology database）是一个计算机视觉数据集
- 官方下载地址：http://yann.lecun.com/exdb/mnist/
- 包含70000张手写数字的灰度图片，其中60000张为训练图像和10000张为测试图像
- 每一张图片都是28*28个像素点大小的灰度图像
- https://keras.io/datasets/#mnist-database-of-handwritten-digits
- TensorFlow：https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist
示例
脚本内容
GitHub：https://github.com/anliven/Hello-AI/blob/master/Google-Learn-and-use-ML/5_save_and_restore_models.py
1 # coding=utf-8
2 import tensorflow as tf
3 from tensorflow import keras
4 import numpy as np
5 import pathlib
6 import os
7
8 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
9 print("# TensorFlow version: {} - tf.keras version: {}".format(tf.VERSION, tf.keras.__version__)) # 查看版本
10
11 # ### 获取示例数据集
12
13 ds_path = str(pathlib.Path.cwd()) + "\\datasets\\mnist\\" # 数据集路径
14 np_data = np.load(ds_path + "mnist.npz") # 加载numpy格式数据
15 print("# np_data keys: ", list(np_data.keys())) # 查看所有的键
16
17 # 加载mnist数据集
18 (train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data(path=ds_path + "mnist.npz")
19 train_labels = train_labels[:1000]
20 test_labels = test_labels[:1000]
21 train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
22 test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0
23
24
25 # ### 定义模型
26 def create_model():
27 model = tf.keras.models.Sequential([
28 keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),
29 keras.layers.Dropout(0.2),
30 keras.layers.Dense(10, activation=tf.nn.softmax)
31 ]) # 构建一个简单的模型
32 model.compile(optimizer=tf.keras.optimizers.Adam(),
33 loss=tf.keras.losses.sparse_categorical_crossentropy,
34 metrics=['accuracy'])
35 return model
36
37
38 mod = create_model()
39 mod.summary()
40
41 # ### 在训练期间保存检查点
42
43 # 检查点回调用法
44 checkpoint_path = "training_1/cp.ckpt"
45 checkpoint_dir = os.path.dirname(checkpoint_path) # 检查点存放目录
46 cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
47 save_weights_only=True,
48 verbose=2) # 创建检查点回调
49 model1 = create_model()
50 model1.fit(train_images, train_labels,
51 epochs=10,
52 validation_data=(test_images, test_labels),
53 verbose=0,
54 callbacks=[cp_callback] # 将ModelCheckpoint回调传递给该模型
55 ) # 训练模型，将创建一个TensorFlow检查点文件集合，这些文件在每个周期结束时更新
56
57 model2 = create_model() # 创建一个未经训练的全新模型（与原始模型架构相同，才能分享权重）
58 loss, acc = model2.evaluate(test_images, test_labels) # 使用测试集进行评估
59 print("# Untrained model2, accuracy: {:5.2f}%".format(100 * acc)) # 未训练模型的表现（准确率约为10%）
60
61 model2.load_weights(checkpoint_path) # 从检查点加载权重
62 loss, acc = model2.evaluate(test_images, test_labels) # 使用测试集，重新进行评估
63 print("# Restored model2, accuracy: {:5.2f}%".format(100 * acc)) # 模型表现得到大幅提升
64
65 # 检查点回调选项
66 checkpoint_path2 = "training_2/cp-{epoch:04d}.ckpt" # 使用“str.format”方式为每个检查点设置唯一名称
67 checkpoint_dir2 = os.path.dirname(checkpoint_path)
68 cp_callback2 = tf.keras.callbacks.ModelCheckpoint(checkpoint_path2,
69 verbose=1,
70 save_weights_only=True,
71 period=5 # 每隔5个周期保存一次检查点
72 ) # 创建检查点回调
73 model3 = create_model()
74 model3.fit(train_images, train_labels,
75 epochs=50,
76 callbacks=[cp_callback2], # 将ModelCheckpoint回调传递给该模型
77 validation_data=(test_images, test_labels),
78 verbose=0) # 训练一个新模型，每隔5个周期保存一次检查点并设置唯一名称
79 latest = tf.train.latest_checkpoint(checkpoint_dir2)
80 print("# latest checkpoint: {}".format(latest)) # 查看最新的检查点
81
82 model4 = create_model() # 重新创建一个全新的模型
83 loss, acc = model2.evaluate(test_images, test_labels) # 使用测试集进行评估
84 print("# Untrained model4, accuracy: {:5.2f}%".format(100 * acc)) # 未训练模型的表现（准确率约为10%）
85
86 model4.load_weights(latest) # 加载最新的检查点
87 loss, acc = model4.evaluate(test_images, test_labels) #
88 print("# Restored model4, accuracy: {:5.2f}%".format(100 * acc)) # 模型表现得到大幅提升
89
90 # ### 手动保存权重
91 model5 = create_model()
92 model5.fit(train_images, train_labels,
93 epochs=10,
94 validation_data=(test_images, test_labels),
95 verbose=0) # 训练模型
96 model5.save_weights('./training_3/my_checkpoint') # 手动保存权重
97
98 model6 = create_model()
99 loss, acc = model6.evaluate(test_images, test_labels)
100 print("# Restored model6, accuracy: {:5.2f}%".format(100 * acc))
101 model6.load_weights('./training_3/my_checkpoint')
102 loss, acc = model6.evaluate(test_images, test_labels)
103 print("# Restored model6, accuracy: {:5.2f}%".format(100 * acc))
104
105 # ### 保存整个模型
106 model7 = create_model()
107 model7.fit(train_images, train_labels, epochs=5)
108 model7.save('my_model.h5') # 保存整个模型到HDF5文件
109
110 model8 = keras.models.load_model('my_model.h5') # 重建完全一样的模型，包括权重和优化器
111 model8.summary()
112 loss, acc = model8.evaluate(test_images, test_labels)
113 print("Restored model8, accuracy: {:5.2f}%".format(100 * acc))运行结果
C:\Users\anliven\AppData\Local\conda\conda\envs\mlcc\python.exe D:/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML/5_save_and_restore_models.py # TensorFlow version: 1.12.0 - tf.keras version: 2.1.6-tf # np_data keys: ['x_test', 'x_train', 'y_train', 'y_test'] _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 512) 401920 _________________________________________________________________ dropout (Dropout) (None, 512) 0 _________________________________________________________________ dense_1 (Dense) (None, 10) 5130 ================================================================= Total params: 407,050 Trainable params: 407,050 Non-trainable params: 0 _________________________________________________________________ Epoch 00001: saving model to training_1/cp.ckpt Epoch 00002: saving model to training_1/cp.ckpt Epoch 00003: saving model to training_1/cp.ckpt Epoch 00004: saving model to training_1/cp.ckpt Epoch 00005: saving model to training_1/cp.ckpt Epoch 00006: saving model to training_1/cp.ckpt Epoch 00007: saving model to training_1/cp.ckpt Epoch 00008: saving model to training_1/cp.ckpt Epoch 00009: saving model to training_1/cp.ckpt Epoch 00010: saving model to training_1/cp.ckpt 32/1000 [..............................] - ETA: 3s 1000/1000 [==============================] - 0s 140us/step # Untrained model2, accuracy: 8.20% 32/1000 [..............................] - ETA: 0s 1000/1000 [==============================] - 0s 40us/step # Restored model2, accuracy: 86.40% Epoch 00005: saving model to training_2/cp-0005.ckpt Epoch 00010: saving model to training_2/cp-0010.ckpt Epoch 00015: saving model to training_2/cp-0015.ckpt Epoch 00020: saving model to training_2/cp-0020.ckpt Epoch 00025: saving model to training_2/cp-0025.ckpt Epoch 00030: saving model to training_2/cp-0030.ckpt Epoch 00035: saving model to training_2/cp-0035.ckpt Epoch 00040: saving model to training_2/cp-0040.ckpt Epoch 00045: saving model to training_2/cp-0045.ckpt Epoch 00050: saving model to training_2/cp-0050.ckpt # latest checkpoint: training_1\cp.ckpt 32/1000 [..............................] - ETA: 3s 1000/1000 [==============================] - 0s 140us/step # Untrained model4, accuracy: 86.40% 32/1000 [..............................] - ETA: 2s 1000/1000 [==============================] - 0s 110us/step # Restored model4, accuracy: 86.40% 32/1000 [..............................] - ETA: 5s 1000/1000 [==============================] - 0s 220us/step # Restored model6, accuracy: 18.20% 32/1000 [..............................] - ETA: 0s 1000/1000 [==============================] - 0s 40us/step # Restored model6, accuracy: 87.40% Epoch 1/5 32/1000 [..............................] - ETA: 9s - loss: 2.4141 - acc: 0.0625 320/1000 [========>.....................] - ETA: 0s - loss: 1.8229 - acc: 0.4469 576/1000 [================>.............] - ETA: 0s - loss: 1.4932 - acc: 0.5694 864/1000 [========================>.....] - ETA: 0s - loss: 1.2624 - acc: 0.6481 1000/1000 [==============================] - 1s 530us/step - loss: 1.1978 - acc: 0.6620 Epoch 2/5 32/1000 [..............................] - ETA: 0s - loss: 0.5490 - acc: 0.8750 320/1000 [========>.....................] - ETA: 0s - loss: 0.4832 - acc: 0.8594 576/1000 [================>.............] - ETA: 0s - loss: 0.4630 - acc: 0.8715 864/1000 [========================>.....] - ETA: 0s - loss: 0.4356 - acc: 0.8808 1000/1000 [==============================] - 0s 200us/step - loss: 0.4298 - acc: 0.8790 Epoch 3/5 32/1000 [..............................] - ETA: 0s - loss: 0.1681 - acc: 0.9688 320/1000 [========>.....................] - ETA: 0s - loss: 0.2826 - acc: 0.9437 576/1000 [================>.............] - ETA: 0s - loss: 0.2774 - acc: 0.9340 832/1000 [=======================>......] - ETA: 0s - loss: 0.2740 - acc: 0.9327 1000/1000 [==============================] - 0s 200us/step - loss: 0.2781 - acc: 0.9280 Epoch 4/5 32/1000 [..............................] - ETA: 0s - loss: 0.1589 - acc: 0.9688 288/1000 [=======>......................] - ETA: 0s - loss: 0.2169 - acc: 0.9410 608/1000 [=================>............] - ETA: 0s - loss: 0.2186 - acc: 0.9457 864/1000 [========================>.....] - ETA: 0s - loss: 0.2231 - acc: 0.9479 1000/1000 [==============================] - 0s 200us/step - loss: 0.2164 - acc: 0.9480 Epoch 5/5 32/1000 [..............................] - ETA: 0s - loss: 0.1095 - acc: 1.0000 352/1000 [=========>....................] - ETA: 0s - loss: 0.1631 - acc: 0.9744 608/1000 [=================>............] - ETA: 0s - loss: 0.1671 - acc: 0.9638 864/1000 [========================>.....] - ETA: 0s - loss: 0.1545 - acc: 0.9688 1000/1000 [==============================] - 0s 210us/step - loss: 0.1538 - acc: 0.9670 _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_14 (Dense) (None, 512) 401920 _________________________________________________________________ dropout_7 (Dropout) (None, 512) 0 _________________________________________________________________ dense_15 (Dense) (None, 10) 5130 ================================================================= Total params: 407,050 Trainable params: 407,050 Non-trainable params: 0 _________________________________________________________________ 32/1000 [..............................] - ETA: 3s 1000/1000 [==============================] - 0s 150us/step Restored model8, accuracy: 86.10% Process finished with exit code 0
生成的文件
anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ ll training_1 total 1601 -rw-r--r-- 1 anliven 197121 71 5月 5 23:36 checkpoint -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp.ckpt.index anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ ls -l training_1 total 1601 -rw-r--r-- 1 anliven 197121 71 5月 5 23:36 checkpoint -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp.ckpt.index anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ ls -l training_2 total 16001 -rw-r--r-- 1 anliven 197121 81 5月 5 23:37 checkpoint -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp-0005.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp-0005.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp-0010.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp-0010.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp-0015.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp-0015.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp-0020.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp-0020.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:36 cp-0025.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:36 cp-0025.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:37 cp-0030.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:37 cp-0030.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:37 cp-0035.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:37 cp-0035.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:37 cp-0040.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:37 cp-0040.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:37 cp-0045.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:37 cp-0045.ckpt.index -rw-r--r-- 1 anliven 197121 1631508 5月 5 23:37 cp-0050.ckpt.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:37 cp-0050.ckpt.index anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ ls -l training_3 total 1601 -rw-r--r-- 1 anliven 197121 83 5月 5 23:37 checkpoint -rw-r--r-- 1 anliven 197121 1631517 5月 5 23:37 my_checkpoint.data-00000-of-00001 -rw-r--r-- 1 anliven 197121 647 5月 5 23:37 my_checkpoint.index anliven@ANLIVEN MINGW64 /d/Anliven/Anliven-Code/PycharmProjects/Google-Learn-and-use-ML $ ls -l my_model.h5 -rw-r--r-- 1 anliven 197121 4909112 5月 5 23:37 my_model.h5
问题处理
问题描述：出现如下告警信息。
WARNING:tensorflow:This model was compiled with a Keras optimizer (<tensorflow.python.keras.optimizers.Adam object at 0x00000280FD318780>) but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved. Consider using a TensorFlow optimizer from `tf.train`.
问题处理：
正常告警，对脚本运行和结果无影响，暂不关注。