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TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！

模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！

文章目录

一、模型的保存与加载(非常重要)！

1.1、模式1：save/load weights
1.2、python实战
1.3、模式2：save/load entire model
1.4、python实战
1.5、模式3：save_model

二、CIFAR10自定义网络实现

2.1、测试代码1
2.2、测试结果1：
2.3、测试代码2：
2.4、测试结果2：

三、需要全套课程视频+PPT+代码资源可以私聊我!

一、模型的保存与加载(非常重要)！

三种保存模式

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第1张图片

1.1、模式1：save/load weights

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第2张图片

1.2、python实战

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第3张图片

代码演示

import tensorflow as tf
from tensorflow.python.keras import datasets, layers, optimizers, Sequential, metrics
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


def preprocess(x, y):
    """
    x is a simple image, not a batch
    """
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = tf.reshape(x, [28*28])
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=10)
    return x, y


batchsz = 256*2
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())

db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)

sample = next(iter(db))
print(sample[0].shape, sample[1].shape)

network = Sequential([layers.Dense(256, activation='relu'),
                      layers.Dense(128, activation='relu'),
                      layers.Dense(64, activation='relu'),
                      layers.Dense(32, activation='relu'),
                      layers.Dense(10)])
network.build(input_shape=(None, 28 * 28))
network.summary()

network.compile(optimizer=optimizers.Adam(lr=0.01),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy']
                )

network.fit(db, epochs=4, validation_data=ds_val, validation_freq=2)

network.evaluate(ds_val)

network.save_weights('weight.ckpt')
print('saved weights')
del network

# 这个创建过程必须和上面的过程一模一样。
network = Sequential([layers.Dense(256, activation='relu'),
                     layers.Dense(128, activation='relu'),
                     layers.Dense(64, activation='relu'),
                     layers.Dense(32, activation='relu'),
                     layers.Dense(10)])
network.compile(optimizer=optimizers.Adam(lr=0.01),
		loss=tf.losses.CategoricalCrossentropy(from_logits=True),
		metrics=['accuracy'])

network.load_weights('weight.ckpt')
print('loaded weights!')
network.evaluate(ds_val)

运行结果：

C:\Anaconda3\envs\tf2\python.exe E:/Codes/MyCodes/TF2/save_load_weight.py
datasets: (60000, 28, 28) (60000,) 0 255
(512, 784) (512, 10)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                multiple                  200960    
_________________________________________________________________
dense_1 (Dense)              multiple                  32896     
_________________________________________________________________
dense_2 (Dense)              multiple                  8256      
_________________________________________________________________
dense_3 (Dense)              multiple                  2080      
_________________________________________________________________
dense_4 (Dense)              multiple                  330       
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
Epoch 1/4

  1/118 [..............................] - ETA: 3:41 - loss: 2.3026 - accuracy: 0.0996
  7/118 [>.............................] - ETA: 30s - loss: 2.1395 - accuracy: 0.1506 
 13/118 [==>...........................] - ETA: 16s - loss: 1.6017 - accuracy: 0.2469
 19/118 [===>..........................] - ETA: 10s - loss: 1.2732 - accuracy: 0.3298
 25/118 [=====>........................] - ETA: 7s - loss: 1.0719 - accuracy: 0.3955 
 31/118 [======>.......................] - ETA: 6s - loss: 0.9313 - accuracy: 0.4474
 37/118 [========>.....................] - ETA: 4s - loss: 0.8256 - accuracy: 0.4894
 43/118 [=========>....................] - ETA: 3s - loss: 0.7480 - accuracy: 0.5241
 49/118 [===========>..................] - ETA: 3s - loss: 0.6862 - accuracy: 0.5532
 55/118 [============>.................] - ETA: 2s - loss: 0.6361 - accuracy: 0.5782
 61/118 [==============>...............] - ETA: 2s - loss: 0.5935 - accuracy: 0.5997
 68/118 [================>.............] - ETA: 1s - loss: 0.5514 - accuracy: 0.6215
 74/118 [=================>............] - ETA: 1s - loss: 0.5238 - accuracy: 0.6379
 80/118 [===================>..........] - ETA: 1s - loss: 0.4981 - accuracy: 0.6525
 86/118 [====================>.........] - ETA: 0s - loss: 0.4752 - accuracy: 0.6656
 93/118 [======================>.......] - ETA: 0s - loss: 0.4524 - accuracy: 0.6794
 99/118 [========================>.....] - ETA: 0s - loss: 0.4344 - accuracy: 0.6900
105/118 [=========================>....] - ETA: 0s - loss: 0.4195 - accuracy: 0.6998
111/118 [===========================>..] - ETA: 0s - loss: 0.4056 - accuracy: 0.7087
116/118 [============================>.] - ETA: 0s - loss: 0.3943 - accuracy: 0.7157
118/118 [==============================] - 3s 25ms/step - loss: 0.3898 - accuracy: 0.7196
Epoch 2/4

  1/118 [..............................] - ETA: 1:49 - loss: 0.1223 - accuracy: 0.9609
  8/118 [=>............................] - ETA: 13s - loss: 0.1451 - accuracy: 0.9549 
 15/118 [==>...........................] - ETA: 7s - loss: 0.1468 - accuracy: 0.9545 
 22/118 [====>.........................] - ETA: 4s - loss: 0.1421 - accuracy: 0.9550
 28/118 [======>.......................] - ETA: 3s - loss: 0.1387 - accuracy: 0.9553
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 40/118 [=========>....................] - ETA: 2s - loss: 0.1347 - accuracy: 0.9563
 46/118 [==========>...................] - ETA: 2s - loss: 0.1345 - accuracy: 0.9566
 52/118 [============>.................] - ETA: 1s - loss: 0.1357 - accuracy: 0.9569
 58/118 [=============>................] - ETA: 1s - loss: 0.1351 - accuracy: 0.9571
 64/118 [===============>..............] - ETA: 1s - loss: 0.1336 - accuracy: 0.9573
 70/118 [================>.............] - ETA: 1s - loss: 0.1314 - accuracy: 0.9575
 76/118 [==================>...........] - ETA: 0s - loss: 0.1309 - accuracy: 0.9577
 82/118 [===================>..........] - ETA: 0s - loss: 0.1291 - accuracy: 0.9579
 88/118 [=====================>........] - ETA: 0s - loss: 0.1271 - accuracy: 0.9581
 94/118 [======================>.......] - ETA: 0s - loss: 0.1257 - accuracy: 0.9583
100/118 [========================>.....] - ETA: 0s - loss: 0.1244 - accuracy: 0.9585
106/118 [=========================>....] - ETA: 0s - loss: 0.1242 - accuracy: 0.9587
112/118 [===========================>..] - ETA: 0s - loss: 0.1234 - accuracy: 0.9589
117/118 [============================>.] - ETA: 0s - loss: 0.1227 - accuracy: 0.9590
118/118 [==============================] - 2s 20ms/step - loss: 0.1219 - accuracy: 0.9591 - val_loss: 0.1277 - val_accuracy: 0.9621
Epoch 3/4

  1/118 [..............................] - ETA: 1:35 - loss: 0.0803 - accuracy: 0.9746
  8/118 [=>............................] - ETA: 11s - loss: 0.0922 - accuracy: 0.9751 
 15/118 [==>...........................] - ETA: 6s - loss: 0.0927 - accuracy: 0.9742 
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 39/118 [========>.....................] - ETA: 2s - loss: 0.0909 - accuracy: 0.9736
 45/118 [==========>...................] - ETA: 1s - loss: 0.0901 - accuracy: 0.9735
 51/118 [===========>..................] - ETA: 1s - loss: 0.0921 - accuracy: 0.9734
 57/118 [=============>................] - ETA: 1s - loss: 0.0939 - accuracy: 0.9732
 63/118 [===============>..............] - ETA: 1s - loss: 0.0947 - accuracy: 0.9730
 69/118 [================>.............] - ETA: 0s - loss: 0.0951 - accuracy: 0.9728
 75/118 [==================>...........] - ETA: 0s - loss: 0.0950 - accuracy: 0.9726
 81/118 [===================>..........] - ETA: 0s - loss: 0.0941 - accuracy: 0.9725
 87/118 [=====================>........] - ETA: 0s - loss: 0.0932 - accuracy: 0.9724
 93/118 [======================>.......] - ETA: 0s - loss: 0.0930 - accuracy: 0.9723
 99/118 [========================>.....] - ETA: 0s - loss: 0.0933 - accuracy: 0.9722
105/118 [=========================>....] - ETA: 0s - loss: 0.0940 - accuracy: 0.9721
111/118 [===========================>..] - ETA: 0s - loss: 0.0941 - accuracy: 0.9721
117/118 [============================>.] - ETA: 0s - loss: 0.0935 - accuracy: 0.9720
118/118 [==============================] - 2s 16ms/step - loss: 0.0929 - accuracy: 0.9720
Epoch 4/4

  1/118 [..............................] - ETA: 1:53 - loss: 0.0628 - accuracy: 0.9785
  7/118 [>.............................] - ETA: 16s - loss: 0.0742 - accuracy: 0.9779 
 14/118 [==>...........................] - ETA: 8s - loss: 0.0821 - accuracy: 0.9768 
 21/118 [====>.........................] - ETA: 5s - loss: 0.0766 - accuracy: 0.9764
 27/118 [=====>........................] - ETA: 4s - loss: 0.0773 - accuracy: 0.9762
 33/118 [=======>......................] - ETA: 3s - loss: 0.0773 - accuracy: 0.9761
 39/118 [========>.....................] - ETA: 2s - loss: 0.0772 - accuracy: 0.9760
 45/118 [==========>...................] - ETA: 2s - loss: 0.0774 - accuracy: 0.9760
 51/118 [===========>..................] - ETA: 1s - loss: 0.0779 - accuracy: 0.9759
 57/118 [=============>................] - ETA: 1s - loss: 0.0778 - accuracy: 0.9758
 63/118 [===============>..............] - ETA: 1s - loss: 0.0777 - accuracy: 0.9758
 69/118 [================>.............] - ETA: 1s - loss: 0.0782 - accuracy: 0.9758
 75/118 [==================>...........] - ETA: 0s - loss: 0.0783 - accuracy: 0.9757
 81/118 [===================>..........] - ETA: 0s - loss: 0.0769 - accuracy: 0.9757
 87/118 [=====================>........] - ETA: 0s - loss: 0.0759 - accuracy: 0.9757
 93/118 [======================>.......] - ETA: 0s - loss: 0.0756 - accuracy: 0.9757
 99/118 [========================>.....] - ETA: 0s - loss: 0.0753 - accuracy: 0.9758
105/118 [=========================>....] - ETA: 0s - loss: 0.0751 - accuracy: 0.9758
111/118 [===========================>..] - ETA: 0s - loss: 0.0750 - accuracy: 0.9758
116/118 [============================>.] - ETA: 0s - loss: 0.0744 - accuracy: 0.9759
118/118 [==============================] - 2s 19ms/step - loss: 0.0737 - accuracy: 0.9759 - val_loss: 0.1082 - val_accuracy: 0.9693

 1/20 [>.............................] - ETA: 0s - loss: 0.0897 - accuracy: 0.9766
 6/20 [========>.....................] - ETA: 0s - loss: 0.1456 - accuracy: 0.9583
11/20 [===============>..............] - ETA: 0s - loss: 0.1283 - accuracy: 0.9625
15/20 [=====================>........] - ETA: 0s - loss: 0.1195 - accuracy: 0.9664
WARNING: Logging before flag parsing goes to stderr.
20/20 [==============================] - 0s 11ms/step - loss: 0.1082 - accuracy: 0.9693
W0505 11:09:55.762711  3344 network.py:1410] This model was compiled with a Keras optimizer (<tensorflow.python.keras.optimizers.Adam object at 0x000002A2F9F35D30>) 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`.
saved weights
loaded weights!

 1/20 [>.............................] - ETA: 4s - loss: 0.0897 - accuracy: 0.9766
 7/20 [=========>....................] - ETA: 0s - loss: 0.1418 - accuracy: 0.9637
12/20 [=================>............] - ETA: 0s - loss: 0.1309 - accuracy: 0.9625
17/20 [========================>.....] - ETA: 0s - loss: 0.1112 - accuracy: 0.9635
20/20 [==============================] - 0s 23ms/step - loss: 0.1082 - accuracy: 0.9647

Process finished with exit code 0

注意： 最后测试的是accuracy为0.9647，上面测试的为0.9693有些细微的差别，这是因为我们的神经网络不止有w，b参数，还有其他一些额外的其他影响因子（比如随机种子没有设置），有些影响的，如果你想完全还原这个模型状态的话，就是下面的内容，把整个模型的状态保存下来。

1.3、模式2：save/load entire model

1.4、python实战

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第4张图片

import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def preprocess(x, y):
    """
    x is a simple image, not a batch
    """
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = tf.reshape(x, [28 * 28])
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=10)
    return x, y


batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())

db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)

sample = next(iter(db))
print(sample[0].shape, sample[1].shape)

network = Sequential([layers.Dense(256, activation='relu'),
                      layers.Dense(128, activation='relu'),
                      layers.Dense(64, activation='relu'),
                      layers.Dense(32, activation='relu'),
                      layers.Dense(10)])
network.build(input_shape=(None, 28 * 28))
network.summary()

network.compile(optimizer=optimizers.Adam(lr=0.01),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy']
                )

network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)

network.evaluate(ds_val)

network.save('./savemodel/model.h5')
print('saved total model.')
del network

print('load model from file')
network = tf.keras.models.load_model('./savemodel/model.h5')
network.compile(optimizer=optimizers.Adam(lr=0.01),
        loss=tf.losses.CategoricalCrossentropy(from_logits=True),
        metrics=['accuracy'])
x_val = tf.cast(x_val, dtype=tf.float32) / 255.
x_val = tf.reshape(x_val, [-1, 28 * 28])
y_val = tf.cast(y_val, dtype=tf.int32)
y_val = tf.one_hot(y_val, depth=10)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(128)
network.evaluate(ds_val)

运行结果：

C:\Anaconda3\envs\tf2\python.exe E:/Codes/MyCodes/TF2/save_load_model.py
datasets: (60000, 28, 28) (60000,) 0 255
(128, 784) (128, 10)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                multiple                  200960    
_________________________________________________________________
dense_1 (Dense)              multiple                  32896     
_________________________________________________________________
dense_2 (Dense)              multiple                  8256      
_________________________________________________________________
dense_3 (Dense)              multiple                  2080      
_________________________________________________________________
dense_4 (Dense)              multiple                  330       
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
Epoch 1/3

  1/469 [..............................] - ETA: 12:10 - loss: 2.3746 - accuracy: 0.0781
 11/469 [..............................] - ETA: 1:07 - loss: 1.6461 - accuracy: 0.2697 
 22/469 [>.............................] - ETA: 33s - loss: 1.1244 - accuracy: 0.4077 
 33/469 [=>............................] - ETA: 22s - loss: 0.9076 - accuracy: 0.4947
 44/469 [=>............................] - ETA: 17s - loss: 0.7778 - accuracy: 0.5533
 54/469 [==>...........................] - ETA: 14s - loss: 0.6928 - accuracy: 0.5925
 65/469 [===>..........................] - ETA: 11s - loss: 0.6378 - accuracy: 0.6257
 76/469 [===>..........................] - ETA: 10s - loss: 0.5904 - accuracy: 0.6518
 87/469 [====>.........................] - ETA: 8s - loss: 0.5485 - accuracy: 0.6732 
 98/469 [=====>........................] - ETA: 7s - loss: 0.5158 - accuracy: 0.6911
109/469 [=====>........................] - ETA: 6s - loss: 0.4916 - accuracy: 0.7063
120/469 [======>.......................] - ETA: 6s - loss: 0.4696 - accuracy: 0.7195
131/469 [=======>......................] - ETA: 5s - loss: 0.4542 - accuracy: 0.7309
141/469 [========>.....................] - ETA: 5s - loss: 0.4398 - accuracy: 0.7401
151/469 [========>.....................] - ETA: 4s - loss: 0.4256 - accuracy: 0.7484
162/469 [=========>....................] - ETA: 4s - loss: 0.4084 - accuracy: 0.7567
172/469 [==========>...................] - ETA: 4s - loss: 0.3997 - accuracy: 0.7636
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193/469 [===========>..................] - ETA: 3s - loss: 0.3808 - accuracy: 0.7763
204/469 [============>.................] - ETA: 3s - loss: 0.3721 - accuracy: 0.7821
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369/469 [======================>.......] - ETA: 0s - loss: 0.2956 - accuracy: 0.8350
380/469 [=======================>......] - ETA: 0s - loss: 0.2915 - accuracy: 0.8372
391/469 [========================>.....] - ETA: 0s - loss: 0.2868 - accuracy: 0.8394
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423/469 [==========================>...] - ETA: 0s - loss: 0.2780 - accuracy: 0.8451
434/469 [==========================>...] - ETA: 0s - loss: 0.2766 - accuracy: 0.8470
445/469 [===========================>..] - ETA: 0s - loss: 0.2743 - accuracy: 0.8487
456/469 [============================>.] - ETA: 0s - loss: 0.2717 - accuracy: 0.8504
465/469 [============================>.] - ETA: 0s - loss: 0.2701 - accuracy: 0.8518
469/469 [==============================] - 4s 8ms/step - loss: 0.2689 - accuracy: 0.8525
Epoch 2/3

  1/469 [..............................] - ETA: 6:24 - loss: 0.2015 - accuracy: 0.9375
 13/469 [..............................] - ETA: 30s - loss: 0.1865 - accuracy: 0.9441 
 25/469 [>.............................] - ETA: 16s - loss: 0.1681 - accuracy: 0.9477
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469/469 [==============================] - 4s 8ms/step - loss: 0.1376 - accuracy: 0.9584 - val_loss: 0.1267 - val_accuracy: 0.9669
Epoch 3/3

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469/469 [==============================] - 3s 7ms/step - loss: 0.1079 - accuracy: 0.9707

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saved total model.
load model from file
WARNING: Logging before flag parsing goes to stderr.
W0505 12:25:20.992213  1504 hdf5_format.py:266] Sequential models without an `input_shape` passed to the first layer cannot reload their optimizer state. As a result, your model isstarting with a freshly initialized optimizer.

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Process finished with exit code 0

1.5、模式3：save_model

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第5张图片

二、CIFAR10自定义网络实现

关于cifar-10的介绍：数据集加载以及测试(张量)—实战！
这个数据集肯定比mnist数据集复杂很多，大小为32*32的彩色图片。比较模糊，这个数据集现在识别最好的就是80%左右。如果我们使用简单的网络也就60%左右，本身来说由于照片比较模糊。
下面要使用自定义的网络层MyDense

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第6张图片

注意cifar10这里和mnist的区别：直接one_hot不行，这就是tf.squeeze()的作用就是把维度挤压掉。

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第7张图片

$t f . s q u e e z e ()$ 用于压缩张量中为1的轴

TensorFlow2.0笔记14：模型的保存与加载(非常重要)以及CIFAR10自定义网络实现！_第8张图片

2.1、测试代码1

import tensorflow as tf
from tensorflow.python.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow.python import keras
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def preprocess(x, y):
    # [0~255] => [-1~1]
    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)
    return x,y


batchsz = 256
# [50k, 32, 32, 3], [10k, 1]
(x, y), (x_val, y_val) = datasets.cifar10.load_data()
y = tf.squeeze(y)
y_val = tf.squeeze(y_val)
y = tf.one_hot(y, depth=10) # [50k, 10]
y_val = tf.one_hot(y_val, depth=10) # [10k, 10]
print('datasets:', x.shape, y.shape, x_val.shape, y_val.shape, x.min(), x.max())

train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.map(preprocess).shuffle(10000).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))
test_db = test_db.map(preprocess).batch(batchsz)

sample = next(iter(train_db))
print("batch: ", sample[0].shape, sample[1].shape)


# 下面新建一个网络对象，这里不再使用标准的layers.Dense方式，使用自己创建的类。
# 因为要使用一些标准的keras中的接口，这里要使用母类layers.Layer
# 这就是我们自定义的简单的线性层，只需要给入简单的：输入维度，输出维度
class MyDense(layers.Layer):
    # 去替代标准的 layers.Dense()
    def __init__(self, inp_dim, outp_dim):
        super(MyDense, self).__init__()

        self.kernel = self.add_variable('w', [inp_dim, outp_dim])
        # self.bias = self.add_variable('b', [outp_dim]) 这里自定义的，去掉了这个。

    def call(self, inputs, training=None):
        x = inputs @ self.kernel
        return x


# 实现自定义的层以后，现在实现自定义网络，这个网络包含了5层
# 首先MyNetwork是调用MyDense层，还可以调用其他的层来组成统一的网络结构。
class MyNetwork(keras.Model):
    
    def __init__(self):
        super(MyNetwork, self).__init__()
        self.fc1 = MyDense(32*32*3, 256)   #新建的5个网络层
        self.fc2 = MyDense(256, 128)
        self.fc3 = MyDense(128, 64)
        self.fc4 = MyDense(64, 32)
        self.fc5 = MyDense(32, 10)



    def call(self, inputs, training=None):
        """

        :param inputs: [b, 32, 32, 3]
        :param training:
        :return:
        """
        x = tf.reshape(inputs, [-1, 32*32*3])
        # [b, 32*32*3] => [b, 256]
        x = self.fc1(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 256] => [b, 128]
        x = self.fc2(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 128] => [b, 64]
        x = self.fc3(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 64] => [b, 32]
        x = self.fc4(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 32] => [b, 10]
        x = self.fc5(x)      # 会调用__call__方法 => call()

        return x

# 下面新建一个网络对象;这里是没有参数的。
network = MyNetwork()
# 得到network之后，我们把它装配起来。
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])

network.fit(train_db, epochs=5, validation_data=test_db, validation_freq=1)

2.2、测试结果1：

C:\Anaconda3\envs\tf2\python.exe E:/Codes/MyCodes/TF2/keras_train.py
datasets: (50000, 32, 32, 3) (50000, 10) (10000, 32, 32, 3) (10000, 10) 0 255
batch:  (256, 32, 32, 3) (256, 10)
Epoch 1/5

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196/196 [==============================] - 4s 23ms/step - loss: 1.9902 - accuracy: 0.2130 - val_loss: 1.8445 - val_accuracy: 0.3282
Epoch 2/5

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196/196 [==============================] - 4s 18ms/step - loss: 1.7583 - accuracy: 0.3634 - val_loss: 1.7307 - val_accuracy: 0.3812
Epoch 3/5

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196/196 [==============================] - 4s 18ms/step - loss: 1.6746 - accuracy: 0.3928 - val_loss: 1.6366 - val_accuracy: 0.4122
Epoch 4/5

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196/196 [==============================] - 4s 18ms/step - loss: 1.6081 - accuracy: 0.4138 - val_loss: 1.5944 - val_accuracy: 0.4300
Epoch 5/5

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Process finished with exit code 0

2.3、测试代码2：

为了提升准确率，如下调整！

把输入数据归一化到[-1, 1]；这里我们把它放在0-1之间其实不是最好的，[0~255] => [-1~1],这个范围可能是最适合神经网络优化的范围。
上面我们可以把参数量调大一些，训练起来效果可能会好一些。
epoch调整到100，这次使用服务器进行测试！

import tensorflow as tf
from tensorflow.python.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow.python import keras
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def preprocess(x, y):
    #  #这里我们把它放在0-1之间其实不是最好的，[0~255] => [-1~1],这个范围可能是最适合的范围。
    x = 2 * tf.cast(x, dtype=tf.float32) / 255. -1.
    y = tf.cast(y, dtype=tf.int32)
    return x,y


batchsz = 256
# [50k, 32, 32, 3], [10k, 1]
(x, y), (x_val, y_val) = datasets.cifar10.load_data()
y = tf.squeeze(y)
y_val = tf.squeeze(y_val)
y = tf.one_hot(y, depth=10) # [50k, 10]
y_val = tf.one_hot(y_val, depth=10) # [10k, 10]
print('datasets:', x.shape, y.shape, x_val.shape, y_val.shape, x.min(), x.max())

train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.map(preprocess).shuffle(10000).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))
test_db = test_db.map(preprocess).batch(batchsz)

sample = next(iter(train_db))
print("batch: ", sample[0].shape, sample[1].shape)


# 下面新建一个网络对象，这里不再使用标准的layers.Dense方式，使用自己创建的类。
# 因为要使用一些标准的keras中的接口，这里要使用母类layers.Layer
# 这就是我们自定义的简单的线性层，只需要给入简单的：输入维度，输出维度
class MyDense(layers.Layer):
    # 去替代标准的 layers.Dense()
    def __init__(self, inp_dim, outp_dim):
        super(MyDense, self).__init__()

        self.kernel = self.add_variable('w', [inp_dim, outp_dim])
        # self.bias = self.add_variable('b', [outp_dim]) 这里自定义的，去掉了这个。

    def call(self, inputs, training=None):
        x = inputs @ self.kernel
        return x


# 实现自定义的层以后，现在实现自定义网络，这个网络包含了5层
# 首先MyNetwork是调用MyDense层，还可以调用其他的层来组成统一的网络结构。
class MyNetwork(keras.Model):
    
    def __init__(self):
        super(MyNetwork, self).__init__()
        self.fc1 = MyDense(32*32*3, 256)   #新建的5个网络层
        self.fc2 = MyDense(256, 256)
        self.fc3 = MyDense(256, 256)
        self.fc4 = MyDense(256, 256)
        self.fc5 = MyDense(256, 10)
        # =============上面我们可以把参数量调大一些，训练起来效果可能会好一些。


    def call(self, inputs, training=None):
        """

        :param inputs: [b, 32, 32, 3]
        :param training:
        :return:
        """
        x = tf.reshape(inputs, [-1, 32*32*3])
        # [b, 32*32*3] => [b, 256]
        x = self.fc1(x)      # fc1(x)会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 256] => [b, 128]
        x = self.fc2(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 128] => [b, 64]
        x = self.fc3(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 64] => [b, 32]
        x = self.fc4(x)      # 会调用__call__方法 => call()
        x = tf.nn.relu(x)    #激活函数可以卸载MyDense里面。
        # [b, 32] => [b, 10]
        x = self.fc5(x)      # 会调用__call__方法 => call()

        return x

# 下面新建一个网络对象;这里是没有参数的。
network = MyNetwork()
# 得到network之后，我们把它装配起来。
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])

network.fit(train_db, epochs=100, validation_data=test_db, validation_freq=1)

network.evaluate(test_db)

network.save_weights('ckpt/weights.ckpt')   #后缀名可以随便的取。
del network                                 #把这个网络删除一下。
print('saved to ckpt/weights.ckpt')


# 再创建一下，因为这里只是单纯的保存一下权值，需要把网络创建加进来。
network = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
network.load_weights('ckpt/weights.ckpt')
print('loaded weights from file.')
# 再做一个测试，使用同一个数据集。
network.evaluate(test_db)

2.4、测试结果2：

ssh://[email protected]:22/home/zhangkf/anaconda3/envs/tf2gpu/bin/python -u /home/zhangkf/tf1/TF2/keras_train.py
datasets: (50000, 32, 32, 3) (50000, 10) (10000, 32, 32, 3) (10000, 10) 0 255
batch:  (256, 32, 32, 3) (256, 10)
Epoch 1/100
196/196 [==============================] - 7s 37ms/step - loss: 1.7186 - accuracy: 0.3382 - val_loss: 1.5680 - val_accuracy: 0.4423
Epoch 2/100
196/196 [==============================] - 6s 32ms/step - loss: 1.4942 - accuracy: 0.4604 - val_loss: 1.4738 - val_accuracy: 0.4846
Epoch 3/100
196/196 [==============================] - 7s 33ms/step - loss: 1.3767 - accuracy: 0.5105 - val_loss: 1.4411 - val_accuracy: 0.4921
Epoch 4/100
196/196 [==============================] - 7s 34ms/step - loss: 1.2899 - accuracy: 0.5433 - val_loss: 1.4346 - val_accuracy: 0.4982
Epoch 5/100
196/196 [==============================] - 7s 35ms/step - loss: 1.2098 - accuracy: 0.5756 - val_loss: 1.4449 - val_accuracy: 0.5011
Epoch 6/100
196/196 [==============================] - 7s 34ms/step - loss: 1.1403 - accuracy: 0.5974 - val_loss: 1.4457 - val_accuracy: 0.5029
Epoch 7/100
196/196 [==============================] - 6s 33ms/step - loss: 1.0750 - accuracy: 0.6230 - val_loss: 1.4638 - val_accuracy: 0.5016
Epoch 8/100
196/196 [==============================] - 6s 32ms/step - loss: 1.0248 - accuracy: 0.6418 - val_loss: 1.4997 - val_accuracy: 0.5004
Epoch 9/100
196/196 [==============================] - 7s 34ms/step - loss: 0.9679 - accuracy: 0.6619 - val_loss: 1.5376 - val_accuracy: 0.4943
Epoch 10/100
196/196 [==============================] - 7s 34ms/step - loss: 0.9331 - accuracy: 0.6737 - val_loss: 1.6079 - val_accuracy: 0.4900
Epoch 11/100
196/196 [==============================] - 6s 30ms/step - loss: 0.8987 - accuracy: 0.6835 - val_loss: 1.6212 - val_accuracy: 0.4983
Epoch 12/100
196/196 [==============================] - 6s 30ms/step - loss: 0.8581 - accuracy: 0.6953 - val_loss: 1.6736 - val_accuracy: 0.4973
Epoch 13/100
196/196 [==============================] - 6s 29ms/step - loss: 0.8204 - accuracy: 0.7089 - val_loss: 1.7515 - val_accuracy: 0.4917
Epoch 14/100
196/196 [==============================] - 7s 34ms/step - loss: 0.7912 - accuracy: 0.7218 - val_loss: 1.7996 - val_accuracy: 0.4867
Epoch 15/100
196/196 [==============================] - 7s 36ms/step - loss: 0.7640 - accuracy: 0.7257 - val_loss: 1.8401 - val_accuracy: 0.4897
Epoch 16/100
196/196 [==============================] - 7s 34ms/step - loss: 0.7333 - accuracy: 0.7365 - val_loss: 1.8550 - val_accuracy: 0.4910
Epoch 17/100
196/196 [==============================] - 7s 35ms/step - loss: 0.7097 - accuracy: 0.7502 - val_loss: 1.9408 - val_accuracy: 0.4865
Epoch 18/100
196/196 [==============================] - 6s 31ms/step - loss: 0.6825 - accuracy: 0.7575 - val_loss: 1.9862 - val_accuracy: 0.4890
Epoch 19/100
196/196 [==============================] - 7s 35ms/step - loss: 0.6543 - accuracy: 0.7672 - val_loss: 2.0883 - val_accuracy: 0.4808
Epoch 20/100
196/196 [==============================] - 6s 29ms/step - loss: 0.6261 - accuracy: 0.7760 - val_loss: 2.1840 - val_accuracy: 0.4740
Epoch 21/100
196/196 [==============================] - 6s 29ms/step - loss: 0.6076 - accuracy: 0.7799 - val_loss: 2.2328 - val_accuracy: 0.4754
Epoch 22/100
196/196 [==============================] - 6s 30ms/step - loss: 0.5753 - accuracy: 0.7978 - val_loss: 2.3189 - val_accuracy: 0.4714
Epoch 23/100
196/196 [==============================] - 7s 35ms/step - loss: 0.5642 - accuracy: 0.7972 - val_loss: 2.3504 - val_accuracy: 0.4723
Epoch 24/100
196/196 [==============================] - 7s 35ms/step - loss: 0.5517 - accuracy: 0.8000 - val_loss: 2.4260 - val_accuracy: 0.4656
Epoch 25/100
196/196 [==============================] - 7s 33ms/step - loss: 0.5353 - accuracy: 0.8124 - val_loss: 2.4914 - val_accuracy: 0.4660
Epoch 26/100
196/196 [==============================] - 7s 34ms/step - loss: 0.5298 - accuracy: 0.8110 - val_loss: 2.5469 - val_accuracy: 0.4630
Epoch 27/100
196/196 [==============================] - 7s 34ms/step - loss: 0.5117 - accuracy: 0.8149 - val_loss: 2.4893 - val_accuracy: 0.4755
Epoch 28/100
196/196 [==============================] - 7s 36ms/step - loss: 0.5030 - accuracy: 0.8210 - val_loss: 2.4983 - val_accuracy: 0.4830
Epoch 29/100
196/196 [==============================] - 7s 34ms/step - loss: 0.4778 - accuracy: 0.8295 - val_loss: 2.5940 - val_accuracy: 0.4723
Epoch 30/100
196/196 [==============================] - 7s 35ms/step - loss: 0.4644 - accuracy: 0.8314 - val_loss: 2.7092 - val_accuracy: 0.4788
Epoch 31/100
196/196 [==============================] - 6s 33ms/step - loss: 0.4422 - accuracy: 0.8427 - val_loss: 2.7854 - val_accuracy: 0.4819
Epoch 32/100
196/196 [==============================] - 7s 35ms/step - loss: 0.4319 - accuracy: 0.8434 - val_loss: 2.7766 - val_accuracy: 0.4751
Epoch 33/100
196/196 [==============================] - 7s 34ms/step - loss: 0.4172 - accuracy: 0.8508 - val_loss: 2.8281 - val_accuracy: 0.4791
Epoch 34/100
196/196 [==============================] - 7s 34ms/step - loss: 0.3999 - accuracy: 0.8584 - val_loss: 2.9011 - val_accuracy: 0.4781
Epoch 35/100
196/196 [==============================] - 6s 33ms/step - loss: 0.3896 - accuracy: 0.8667 - val_loss: 2.9909 - val_accuracy: 0.4802
Epoch 36/100
196/196 [==============================] - 6s 30ms/step - loss: 0.3858 - accuracy: 0.8614 - val_loss: 3.1531 - val_accuracy: 0.4738
Epoch 37/100
196/196 [==============================] - 6s 33ms/step - loss: 0.3799 - accuracy: 0.8654 - val_loss: 3.1965 - val_accuracy: 0.4758
Epoch 38/100
196/196 [==============================] - 7s 34ms/step - loss: 0.3690 - accuracy: 0.8687 - val_loss: 3.2377 - val_accuracy: 0.4766
Epoch 39/100
196/196 [==============================] - 5s 27ms/step - loss: 0.3681 - accuracy: 0.8698 - val_loss: 3.2637 - val_accuracy: 0.4778
Epoch 40/100
196/196 [==============================] - 6s 29ms/step - loss: 0.3670 - accuracy: 0.8660 - val_loss: 3.1468 - val_accuracy: 0.4827
Epoch 41/100
196/196 [==============================] - 7s 34ms/step - loss: 0.3523 - accuracy: 0.8731 - val_loss: 3.0965 - val_accuracy: 0.4896
Epoch 42/100
196/196 [==============================] - 6s 32ms/step - loss: 0.3431 - accuracy: 0.8792 - val_loss: 3.2617 - val_accuracy: 0.4880
Epoch 43/100
196/196 [==============================] - 7s 34ms/step - loss: 0.3245 - accuracy: 0.8869 - val_loss: 3.2619 - val_accuracy: 0.4857
Epoch 44/100
196/196 [==============================] - 7s 33ms/step - loss: 0.3237 - accuracy: 0.8879 - val_loss: 3.3753 - val_accuracy: 0.4814
Epoch 45/100
196/196 [==============================] - 6s 33ms/step - loss: 0.3092 - accuracy: 0.8912 - val_loss: 3.5018 - val_accuracy: 0.4854
Epoch 46/100
196/196 [==============================] - 7s 33ms/step - loss: 0.3046 - accuracy: 0.8942 - val_loss: 3.4471 - val_accuracy: 0.4888
Epoch 47/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2889 - accuracy: 0.8992 - val_loss: 3.5435 - val_accuracy: 0.4825
Epoch 48/100
196/196 [==============================] - 7s 34ms/step - loss: 0.2820 - accuracy: 0.9013 - val_loss: 3.5380 - val_accuracy: 0.4820
Epoch 49/100
196/196 [==============================] - 7s 34ms/step - loss: 0.2727 - accuracy: 0.9048 - val_loss: 3.6659 - val_accuracy: 0.4790
Epoch 50/100
196/196 [==============================] - 7s 34ms/step - loss: 0.2780 - accuracy: 0.9020 - val_loss: 3.6552 - val_accuracy: 0.4823
Epoch 51/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2747 - accuracy: 0.9022 - val_loss: 3.6271 - val_accuracy: 0.4781
Epoch 52/100
196/196 [==============================] - 6s 29ms/step - loss: 0.2579 - accuracy: 0.9069 - val_loss: 3.8432 - val_accuracy: 0.4793
Epoch 53/100
196/196 [==============================] - 6s 29ms/step - loss: 0.2560 - accuracy: 0.9111 - val_loss: 3.7360 - val_accuracy: 0.4757
Epoch 54/100
196/196 [==============================] - 6s 29ms/step - loss: 0.2561 - accuracy: 0.9110 - val_loss: 3.8931 - val_accuracy: 0.4768
Epoch 55/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2502 - accuracy: 0.9114 - val_loss: 3.9386 - val_accuracy: 0.4748
Epoch 56/100
196/196 [==============================] - 7s 35ms/step - loss: 0.2295 - accuracy: 0.9196 - val_loss: 3.9047 - val_accuracy: 0.4764
Epoch 57/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2322 - accuracy: 0.9190 - val_loss: 4.0256 - val_accuracy: 0.4743
Epoch 58/100
196/196 [==============================] - 7s 35ms/step - loss: 0.2333 - accuracy: 0.9194 - val_loss: 4.1367 - val_accuracy: 0.4711
Epoch 59/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2277 - accuracy: 0.9190 - val_loss: 4.0834 - val_accuracy: 0.4761
Epoch 60/100
196/196 [==============================] - 6s 32ms/step - loss: 0.2214 - accuracy: 0.9239 - val_loss: 4.1716 - val_accuracy: 0.4737
Epoch 61/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2207 - accuracy: 0.9209 - val_loss: 4.1157 - val_accuracy: 0.4721
Epoch 62/100
196/196 [==============================] - 7s 34ms/step - loss: 0.2295 - accuracy: 0.9211 - val_loss: 4.3243 - val_accuracy: 0.4712
Epoch 63/100
196/196 [==============================] - 7s 34ms/step - loss: 0.2172 - accuracy: 0.9217 - val_loss: 4.3557 - val_accuracy: 0.4717
Epoch 64/100
196/196 [==============================] - 6s 30ms/step - loss: 0.2082 - accuracy: 0.9245 - val_loss: 4.4790 - val_accuracy: 0.4745
Epoch 65/100
196/196 [==============================] - 6s 31ms/step - loss: 0.2135 - accuracy: 0.9239 - val_loss: 4.3580 - val_accuracy: 0.4727
Epoch 66/100
196/196 [==============================] - 6s 29ms/step - loss: 0.2251 - accuracy: 0.9204 - val_loss: 4.4125 - val_accuracy: 0.4690
Epoch 67/100
196/196 [==============================] - 6s 30ms/step - loss: 0.2125 - accuracy: 0.9247 - val_loss: 4.4460 - val_accuracy: 0.4690
Epoch 68/100
196/196 [==============================] - 7s 33ms/step - loss: 0.2121 - accuracy: 0.9243 - val_loss: 4.2774 - val_accuracy: 0.4720
Epoch 69/100
196/196 [==============================] - 6s 33ms/step - loss: 0.2046 - accuracy: 0.9256 - val_loss: 4.2823 - val_accuracy: 0.4743
Epoch 70/100
196/196 [==============================] - 5s 27ms/step - loss: 0.1975 - accuracy: 0.9304 - val_loss: 4.2939 - val_accuracy: 0.4801
Epoch 71/100
196/196 [==============================] - 5s 27ms/step - loss: 0.1881 - accuracy: 0.9361 - val_loss: 4.4338 - val_accuracy: 0.4775
Epoch 72/100
196/196 [==============================] - 5s 28ms/step - loss: 0.1862 - accuracy: 0.9347 - val_loss: 4.4342 - val_accuracy: 0.4761
Epoch 73/100
196/196 [==============================] - 6s 31ms/step - loss: 0.1810 - accuracy: 0.9371 - val_loss: 4.4888 - val_accuracy: 0.4732
Epoch 74/100
196/196 [==============================] - 7s 36ms/step - loss: 0.1889 - accuracy: 0.9347 - val_loss: 4.5377 - val_accuracy: 0.4729
Epoch 75/100
196/196 [==============================] - 6s 33ms/step - loss: 0.1821 - accuracy: 0.9364 - val_loss: 4.5335 - val_accuracy: 0.4786
Epoch 76/100
196/196 [==============================] - 6s 31ms/step - loss: 0.1816 - accuracy: 0.9371 - val_loss: 4.5314 - val_accuracy: 0.4739
Epoch 77/100
196/196 [==============================] - 6s 30ms/step - loss: 0.1886 - accuracy: 0.9356 - val_loss: 4.6643 - val_accuracy: 0.4716
Epoch 78/100
196/196 [==============================] - 6s 30ms/step - loss: 0.1808 - accuracy: 0.9353 - val_loss: 4.5243 - val_accuracy: 0.4714
Epoch 79/100
196/196 [==============================] - 6s 32ms/step - loss: 0.1640 - accuracy: 0.9425 - val_loss: 4.5498 - val_accuracy: 0.4781
Epoch 80/100
196/196 [==============================] - 6s 32ms/step - loss: 0.1609 - accuracy: 0.9413 - val_loss: 4.7355 - val_accuracy: 0.4760
Epoch 81/100
196/196 [==============================] - 6s 29ms/step - loss: 0.1657 - accuracy: 0.9419 - val_loss: 4.7074 - val_accuracy: 0.4787
Epoch 82/100
196/196 [==============================] - 5s 28ms/step - loss: 0.1630 - accuracy: 0.9424 - val_loss: 4.7073 - val_accuracy: 0.4812
Epoch 83/100
196/196 [==============================] - 6s 29ms/step - loss: 0.1609 - accuracy: 0.9427 - val_loss: 4.7601 - val_accuracy: 0.4861
Epoch 84/100
196/196 [==============================] - 6s 31ms/step - loss: 0.1487 - accuracy: 0.9469 - val_loss: 4.8887 - val_accuracy: 0.4781
Epoch 85/100
196/196 [==============================] - 7s 34ms/step - loss: 0.1485 - accuracy: 0.9477 - val_loss: 4.7442 - val_accuracy: 0.4807
Epoch 86/100
196/196 [==============================] - 6s 33ms/step - loss: 0.1476 - accuracy: 0.9490 - val_loss: 4.9249 - val_accuracy: 0.4870
Epoch 87/100
196/196 [==============================] - 6s 32ms/step - loss: 0.1626 - accuracy: 0.9427 - val_loss: 4.9209 - val_accuracy: 0.4828
Epoch 88/100
196/196 [==============================] - 6s 33ms/step - loss: 0.1679 - accuracy: 0.9396 - val_loss: 4.8001 - val_accuracy: 0.4829
Epoch 89/100
196/196 [==============================] - 6s 33ms/step - loss: 0.1422 - accuracy: 0.9505 - val_loss: 4.8969 - val_accuracy: 0.4801
Epoch 90/100
196/196 [==============================] - 6s 30ms/step - loss: 0.1458 - accuracy: 0.9501 - val_loss: 5.1439 - val_accuracy: 0.4799
Epoch 91/100
196/196 [==============================] - 6s 29ms/step - loss: 0.1445 - accuracy: 0.9490 - val_loss: 5.0177 - val_accuracy: 0.4748
Epoch 92/100
196/196 [==============================] - 5s 25ms/step - loss: 0.1524 - accuracy: 0.9491 - val_loss: 5.0628 - val_accuracy: 0.4838
Epoch 93/100
196/196 [==============================] - 6s 31ms/step - loss: 0.1390 - accuracy: 0.9512 - val_loss: 5.0847 - val_accuracy: 0.4812
Epoch 94/100
196/196 [==============================] - 7s 34ms/step - loss: 0.1459 - accuracy: 0.9461 - val_loss: 5.0484 - val_accuracy: 0.4802
Epoch 95/100
196/196 [==============================] - 6s 33ms/step - loss: 0.1379 - accuracy: 0.9537 - val_loss: 5.1362 - val_accuracy: 0.4747
Epoch 96/100
196/196 [==============================] - 6s 29ms/step - loss: 0.1519 - accuracy: 0.9484 - val_loss: 5.1338 - val_accuracy: 0.4810
Epoch 97/100
196/196 [==============================] - 5s 28ms/step - loss: 0.1464 - accuracy: 0.9505 - val_loss: 5.1788 - val_accuracy: 0.4816
Epoch 98/100
196/196 [==============================] - 6s 29ms/step - loss: 0.1342 - accuracy: 0.9531 - val_loss: 5.0979 - val_accuracy: 0.4797
Epoch 99/100
196/196 [==============================] - 6s 33ms/step - loss: 0.1370 - accuracy: 0.9508 - val_loss: 5.1631 - val_accuracy: 0.4815
Epoch 100/100
196/196 [==============================] - 7s 34ms/step - loss: 0.1298 - accuracy: 0.9559 - val_loss: 5.1771 - val_accuracy: 0.4786
40/40 [==============================] - 1s 24ms/step - loss: 5.1771 - accuracy: 0.4786

saved to ckpt/weights.ckpt
loaded weights from file.
40/40 [==============================] - 1s 29ms/step - loss: 5.1771 - accuracy: 0.4788

Process finished with exit code 0

注意： 可以发现上面的程序训练起来的准确率比较高，但是测试的时候准确率不高。出现了了过拟合的现象。

三、需要全套课程视频+PPT+代码资源可以私聊我!

方式1：CSDN私信我!
方式2：QQ邮箱:[email protected]或者直接加我QQ!

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