AI - TensorFlow - 示例04：过拟合与欠拟合

忘是亡心i 2022-01-08 02:21 298阅读 0赞

# 过拟合与欠拟合（Overfitting and underfitting） #

官网示例：[https://www.tensorflow.org/tutorials/keras/overfit\_and\_underfit][https_www.tensorflow.org_tutorials_keras_overfit_and_underfit]  
主要步骤：

*  演示过拟合
 *    - 创建基准模型
 *    - 创建一个更小的模型
 *    - 创建一个更大的模型
 *    - 绘制训练损失和验证损失函数
 *  策略
 *    - 添加权重正则化
 *    - 添加丢弃层

# 一些知识点 #

## 过拟合 ##

在训练集上可以实现很高的准确率，但无法很好地泛化到测试数据（或之前未见过的数据）。  
可能导致欠拟合的原因：训练时间过长等。  
防止过拟合的最常见方法：  
  - 推荐：获取并使用更多训练数据  
  - 最简单：适当缩小模型（降低网络容量）  
  - 添加权重正则化（限制网络的复杂性，也就是限制可存储信息的数量和类型）  
  - 添加丢弃层  
此外还有两个重要的方法：数据增强和批次归一化。

## 欠拟合 ##

与过拟合相对的就是欠拟合，测试数据仍存在改进空间，意味着模型未学习到训练数据中的相关模式。  
可能导致欠拟合的原因：模型不够强大、过于正则化、或者根本没有训练足够长的时间等。

## 模型大小 ##

防止过拟合，最简单的方法是缩小模型，即减少模型中可学习参数的数量（由层数和每层的单元数决定）。  
在深度学习中，模型中可学习参数的数量通常称为模型的“容量”。  
模型“记忆容量”越大，越能轻松学习训练样本与其目标之间的字典式完美映射（无任何泛化能力的映射），但无法对未见过的数据做出预测。  
也就是说，网络容量越大，便能够越快对训练数据进行建模（产生较低的训练损失），但越容易过拟合（导致训练损失与验证损失之间的差异很大）。  
如果模型太小（记忆资源有限），便无法轻松学习映射，难以与训练数据拟合。  
需要尝试不断地尝试来确定合适的模型大小或架构（由层数或每层的合适大小决定）。  
最好先使用相对较少的层和参数，然后开始增加层的大小或添加新的层，直到看到返回的验证损失不断减小为止。

## 奥卡姆剃刀定律 ##

如果对于同一现象有两种解释，最可能正确的解释是“最简单”的解释，即做出最少量假设的解释。  
也适用于神经网络学习的模型：给定一些训练数据和一个网络架构，有多组权重值（多个模型）可以解释数据，而简单模型比复杂模型更不容易过拟合。  
简单模型”是一种参数值分布的熵较低的模型（或者具有较少参数的模型）。

## 权重正则化 ##

限制网络的复杂性，具体方法是强制要求其权重仅采用较小的值，使权重值的分布更“规则”。  
通过向网络的损失函数添加与权重较大相关的代价来实现。  
\- L1正则化，其中所添加的代价与权重系数的绝对值（即所谓的权重“L1 范数”）成正比。  
\- L2正则化，其中所添加的代价与权重系数值的平方（即所谓的权重“L2 范数”）成正比，也称为权重衰减。

## 丢弃层 ##

添加丢弃层可明显改善基准模型，是最有效且最常用的神经网络正则化技术之一。  
丢弃（应用于某个层）是指在训练期间随机“丢弃”（即设置为 0）该层的多个输出特征。  
“丢弃率”指变为 0 的特征所占的比例，通常设置在 0.2 和 0.5 之间。  
因为“测试时的活跃单元数大于训练时的活跃单元数”，因此测试时网络不会丢弃任何单元，而是将层的输出值按等同于丢弃率的比例进行缩减。

# 示例 #

## 脚本内容 ##

GitHub：[https://github.com/anliven/Hello-AI/blob/master/Google-Learn-and-use-ML/4\_overfit\_and\_underfit.py][https_github.com_anliven_Hello-AI_blob_master_Google-Learn-and-use-ML_4_overfit_and_underfit.py]

1 # coding=utf-8
      2 import tensorflow as tf
      3 from tensorflow import keras
      4 import numpy as np
      5 import matplotlib.pyplot as plt
      6 import pathlib
      7 import os
      8 
      9 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
     10 print("# TensorFlow version: {}  - tf.keras version: {}".format(tf.VERSION, tf.keras.__version__))  # 查看版本
     11 ds_path = str(pathlib.Path.cwd()) + "\\datasets\\imdb\\"  # 数据集路径
     12 
     13 # ### 查看numpy格式数据
     14 np_data = np.load(ds_path + "imdb.npz")
     15 print("# np_data keys: ", list(np_data.keys()))  # 查看所有的键
     16 # print("# np_data values: ", list(np_data.values()))  # 查看所有的值
     17 # print("# np_data items: ", list(np_data.items()))  # 查看所有的item
     18 
     19 # ### 加载IMDB数据集
     20 NUM_WORDS = 10000
     21 
     22 imdb = keras.datasets.imdb
     23 (train_data, train_labels), (test_data, test_labels) = imdb.load_data(
     24     path=ds_path + "imdb.npz",
     25     num_words=NUM_WORDS  # 保留训练数据中出现频次在前10000位的字词
     26 )
     27 
     28 
     29 def multi_hot_sequences(sequences, dimension):
     30     results = np.zeros((len(sequences), dimension))
     31     for i, word_indices in enumerate(sequences):
     32         results[i, word_indices] = 1.0
     33     return results
     34 
     35 
     36 train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)
     37 test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)
     38 
     39 plt.plot(train_data[0])  # 查看其中的一个多热向量（字词索引按频率排序，因此索引0附近应该有更多的1值）
     40 plt.savefig("./outputs/sample-4-figure-1.png", dpi=200, format='png')
     41 plt.show()
     42 plt.close()
     43 
     44 # ### 演示过拟合
     45 # 创建基准模型
     46 baseline_model = keras.Sequential([
     47     keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),
     48     keras.layers.Dense(16, activation=tf.nn.relu),
     49     keras.layers.Dense(1, activation=tf.nn.sigmoid)
     50 ])  # 仅使用Dense层创建一个简单的基准模型
     51 baseline_model.compile(optimizer='adam',
     52                        loss='binary_crossentropy',
     53                        metrics=['accuracy', 'binary_crossentropy'])  # 编译模型
     54 baseline_model.summary()  # 打印出关于模型的简单描述
     55 baseline_history = baseline_model.fit(train_data,
     56                                       train_labels,
     57                                       epochs=20,
     58                                       batch_size=512,
     59                                       validation_data=(test_data, test_labels),
     60                                       verbose=2)  # 训练模型
     61 
     62 # 创建一个更小的模型（隐藏单元更少）
     63 smaller_model = keras.Sequential([
     64     keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),
     65     keras.layers.Dense(4, activation=tf.nn.relu),
     66     keras.layers.Dense(1, activation=tf.nn.sigmoid)
     67 ])
     68 smaller_model.compile(optimizer='adam',
     69                       loss='binary_crossentropy',
     70                       metrics=['accuracy', 'binary_crossentropy'])
     71 smaller_model.summary()
     72 smaller_history = smaller_model.fit(train_data,
     73                                     train_labels,
     74                                     epochs=20,
     75                                     batch_size=512,
     76                                     validation_data=(test_data, test_labels),
     77                                     verbose=2)  # 使用相同的数据训练
     78 
     79 # 创建一个更大的模型（远超出解决问题所需的容量）
     80 bigger_model = keras.models.Sequential([
     81     keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),
     82     keras.layers.Dense(512, activation=tf.nn.relu),
     83     keras.layers.Dense(1, activation=tf.nn.sigmoid)
     84 ])
     85 bigger_model.compile(optimizer='adam',
     86                      loss='binary_crossentropy',
     87                      metrics=['accuracy', 'binary_crossentropy'])
     88 bigger_model.summary()
     89 bigger_history = bigger_model.fit(train_data,
     90                                   train_labels,
     91                                   epochs=20,
     92                                   batch_size=512,
     93                                   validation_data=(test_data, test_labels),
     94                                   verbose=2)  # 使用相同的数据训练
     95 
     96 
     97 # 绘制训练损失和验证损失图表
     98 # 这里实线表示训练损失，虚线表示验证损失(验证损失越低，表示模型越好)
     99 def plot_history(histories, key='binary_crossentropy'):
    100     plt.figure(figsize=(16, 10))
    101     for name, his in histories:
    102         val = plt.plot(his.epoch,
    103                        his.history['val_' + key],
    104                        linestyle='--',  # 默认颜色的虚线（dashed line with default color）
    105                        label=name.title() + ' Val')
    106         plt.plot(his.epoch,
    107                  his.history[key],
    108                  color=val[0].get_color(),
    109                  label=name.title() + ' Train')
    110     plt.xlabel('Epochs')
    111     plt.ylabel(key.replace('_', ' ').title())
    112     plt.legend()
    113     plt.xlim([0, max(his.epoch)])
    114 
    115 
    116 plot_history([('baseline', baseline_history), ('smaller', smaller_history), ('bigger', bigger_history)])
    117 plt.savefig("./outputs/sample-4-figure-2.png", dpi=200, format='png')
    118 plt.show()
    119 plt.close()
    120 
    121 # ### 策略
    122 # 添加权重正则化
    123 l2_model = keras.models.Sequential([
    124     keras.layers.Dense(16,
    125                        kernel_regularizer=keras.regularizers.l2(0.001),  # 添加L2权重正则化
    126                        activation=tf.nn.relu,
    127                        input_shape=(NUM_WORDS,)),
    128     keras.layers.Dense(16,
    129                        kernel_regularizer=keras.regularizers.l2(0.001),  # 实际上是将权重正则化项实例作为关键字参数传递给层
    130                        activation=tf.nn.relu),
    131     keras.layers.Dense(1, activation=tf.nn.sigmoid)])
    132 l2_model.compile(optimizer='adam',
    133                  loss='binary_crossentropy',
    134                  metrics=['accuracy', 'binary_crossentropy'])
    135 l2_model_history = l2_model.fit(train_data,
    136                                 train_labels,
    137                                 epochs=20,
    138                                 batch_size=512,
    139                                 validation_data=(test_data, test_labels),
    140                                 verbose=2)  # 使用相同的数据训练
    141 plot_history([('baseline', baseline_history), ('l2', l2_model_history)])
    142 plt.savefig("./outputs/sample-4-figure-3.png", dpi=200, format='png')
    143 plt.show()  # 查看在训练阶段添加L2正则化惩罚的影响，此过拟合抵抗能力强于基准模型
    144 plt.close()
    145 
    146 # 添加丢弃层
    147 dpt_model = keras.models.Sequential([
    148     keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),
    149     keras.layers.Dropout(0.5),  # 通过丢弃层将丢弃引入网络中，以便事先将其应用于层的输出
    150     keras.layers.Dense(16, activation=tf.nn.relu),
    151     keras.layers.Dropout(0.5),  # 添加丢弃层，“丢弃率”设置在0.5
    152     keras.layers.Dense(1, activation=tf.nn.sigmoid)
    153 ])
    154 dpt_model.compile(optimizer='adam',
    155                   loss='binary_crossentropy',
    156                   metrics=['accuracy', 'binary_crossentropy'])
    157 dpt_model_history = dpt_model.fit(train_data, train_labels,
    158                                   epochs=20,
    159                                   batch_size=512,
    160                                   validation_data=(test_data, test_labels),
    161                                   verbose=2)
    162 plot_history([('baseline', baseline_history), ('dropout', dpt_model_history)])
    163 plt.savefig("./outputs/sample-4-figure-4.png", dpi=200, format='png')
    164 plt.show()
    165 plt.close()

## 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/4_overfit_and_underfit.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, 16)                160016    
    _________________________________________________________________
    dense_1 (Dense)              (None, 16)                272       
    _________________________________________________________________
    dense_2 (Dense)              (None, 1)                 17        
    =================================================================
    Total params: 160,305
    Trainable params: 160,305
    Non-trainable params: 0
    _________________________________________________________________
    Train on 25000 samples, validate on 25000 samples
    Epoch 1/20
     - 6s - loss: 0.4610 - acc: 0.8232 - binary_crossentropy: 0.4610 - val_loss: 0.3217 - val_acc: 0.8791 - val_binary_crossentropy: 0.3217
    Epoch 2/20
     - 5s - loss: 0.2379 - acc: 0.9151 - binary_crossentropy: 0.2379 - val_loss: 0.2829 - val_acc: 0.8876 - val_binary_crossentropy: 0.2829
    Epoch 3/20
     - 4s - loss: 0.1763 - acc: 0.9379 - binary_crossentropy: 0.1763 - val_loss: 0.2900 - val_acc: 0.8847 - val_binary_crossentropy: 0.2900
    Epoch 4/20
     - 4s - loss: 0.1402 - acc: 0.9524 - binary_crossentropy: 0.1402 - val_loss: 0.3163 - val_acc: 0.8788 - val_binary_crossentropy: 0.3163
    Epoch 5/20
     - 4s - loss: 0.1145 - acc: 0.9633 - binary_crossentropy: 0.1145 - val_loss: 0.3436 - val_acc: 0.8731 - val_binary_crossentropy: 0.3436
    Epoch 6/20
     - 4s - loss: 0.0913 - acc: 0.9738 - binary_crossentropy: 0.0913 - val_loss: 0.3759 - val_acc: 0.8697 - val_binary_crossentropy: 0.3759
    Epoch 7/20
     - 4s - loss: 0.0719 - acc: 0.9811 - binary_crossentropy: 0.0719 - val_loss: 0.4285 - val_acc: 0.8619 - val_binary_crossentropy: 0.4285
    Epoch 8/20
     - 4s - loss: 0.0562 - acc: 0.9869 - binary_crossentropy: 0.0562 - val_loss: 0.4527 - val_acc: 0.8634 - val_binary_crossentropy: 0.4527
    Epoch 9/20
     - 5s - loss: 0.0432 - acc: 0.9921 - binary_crossentropy: 0.0432 - val_loss: 0.4926 - val_acc: 0.8598 - val_binary_crossentropy: 0.4926
    Epoch 10/20
     - 4s - loss: 0.0326 - acc: 0.9949 - binary_crossentropy: 0.0326 - val_loss: 0.5394 - val_acc: 0.8570 - val_binary_crossentropy: 0.5394
    Epoch 11/20
     - 5s - loss: 0.0243 - acc: 0.9972 - binary_crossentropy: 0.0243 - val_loss: 0.5726 - val_acc: 0.8569 - val_binary_crossentropy: 0.5726
    Epoch 12/20
     - 4s - loss: 0.0182 - acc: 0.9984 - binary_crossentropy: 0.0182 - val_loss: 0.6164 - val_acc: 0.8562 - val_binary_crossentropy: 0.6164
    Epoch 13/20
     - 4s - loss: 0.0133 - acc: 0.9991 - binary_crossentropy: 0.0133 - val_loss: 0.6419 - val_acc: 0.8547 - val_binary_crossentropy: 0.6419
    Epoch 14/20
     - 4s - loss: 0.0102 - acc: 0.9995 - binary_crossentropy: 0.0102 - val_loss: 0.6774 - val_acc: 0.8531 - val_binary_crossentropy: 0.6774
    Epoch 15/20
     - 4s - loss: 0.0077 - acc: 0.9997 - binary_crossentropy: 0.0077 - val_loss: 0.7014 - val_acc: 0.8539 - val_binary_crossentropy: 0.7014
    Epoch 16/20
     - 5s - loss: 0.0060 - acc: 0.9998 - binary_crossentropy: 0.0060 - val_loss: 0.7268 - val_acc: 0.8532 - val_binary_crossentropy: 0.7268
    Epoch 17/20
     - 5s - loss: 0.0048 - acc: 1.0000 - binary_crossentropy: 0.0048 - val_loss: 0.7493 - val_acc: 0.8541 - val_binary_crossentropy: 0.7493
    Epoch 18/20
     - 5s - loss: 0.0039 - acc: 1.0000 - binary_crossentropy: 0.0039 - val_loss: 0.7733 - val_acc: 0.8530 - val_binary_crossentropy: 0.7733
    Epoch 19/20
     - 5s - loss: 0.0033 - acc: 1.0000 - binary_crossentropy: 0.0033 - val_loss: 0.7883 - val_acc: 0.8535 - val_binary_crossentropy: 0.7883
    Epoch 20/20
     - 5s - loss: 0.0028 - acc: 1.0000 - binary_crossentropy: 0.0028 - val_loss: 0.8105 - val_acc: 0.8533 - val_binary_crossentropy: 0.8105
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    dense_3 (Dense)              (None, 4)                 40004     
    _________________________________________________________________
    dense_4 (Dense)              (None, 4)                 20        
    _________________________________________________________________
    dense_5 (Dense)              (None, 1)                 5         
    =================================================================
    Total params: 40,029
    Trainable params: 40,029
    Non-trainable params: 0
    _________________________________________________________________
    Train on 25000 samples, validate on 25000 samples
    Epoch 1/20
     - 5s - loss: 0.6191 - acc: 0.6490 - binary_crossentropy: 0.6191 - val_loss: 0.5584 - val_acc: 0.7492 - val_binary_crossentropy: 0.5584
    Epoch 2/20
     - 5s - loss: 0.4989 - acc: 0.8361 - binary_crossentropy: 0.4989 - val_loss: 0.4795 - val_acc: 0.8600 - val_binary_crossentropy: 0.4795
    Epoch 3/20
     - 5s - loss: 0.4216 - acc: 0.9002 - binary_crossentropy: 0.4216 - val_loss: 0.4286 - val_acc: 0.8737 - val_binary_crossentropy: 0.4286
    Epoch 4/20
     - 5s - loss: 0.3551 - acc: 0.9257 - binary_crossentropy: 0.3551 - val_loss: 0.3811 - val_acc: 0.8820 - val_binary_crossentropy: 0.3811
    Epoch 5/20
     - 5s - loss: 0.2593 - acc: 0.9428 - binary_crossentropy: 0.2593 - val_loss: 0.3011 - val_acc: 0.8829 - val_binary_crossentropy: 0.3011
    Epoch 6/20
     - 5s - loss: 0.1775 - acc: 0.9496 - binary_crossentropy: 0.1775 - val_loss: 0.2934 - val_acc: 0.8827 - val_binary_crossentropy: 0.2934
    Epoch 7/20
     - 5s - loss: 0.1435 - acc: 0.9575 - binary_crossentropy: 0.1435 - val_loss: 0.3068 - val_acc: 0.8790 - val_binary_crossentropy: 0.3068
    Epoch 8/20
     - 5s - loss: 0.1213 - acc: 0.9651 - binary_crossentropy: 0.1213 - val_loss: 0.3301 - val_acc: 0.8760 - val_binary_crossentropy: 0.3301
    Epoch 9/20
     - 5s - loss: 0.1043 - acc: 0.9701 - binary_crossentropy: 0.1043 - val_loss: 0.3437 - val_acc: 0.8736 - val_binary_crossentropy: 0.3437
    Epoch 10/20
     - 5s - loss: 0.0907 - acc: 0.9756 - binary_crossentropy: 0.0907 - val_loss: 0.3651 - val_acc: 0.8706 - val_binary_crossentropy: 0.3651
    Epoch 11/20
     - 5s - loss: 0.0793 - acc: 0.9793 - binary_crossentropy: 0.0793 - val_loss: 0.3876 - val_acc: 0.8688 - val_binary_crossentropy: 0.3876
    Epoch 12/20
     - 5s - loss: 0.0690 - acc: 0.9836 - binary_crossentropy: 0.0690 - val_loss: 0.4072 - val_acc: 0.8667 - val_binary_crossentropy: 0.4072
    Epoch 13/20
     - 5s - loss: 0.0606 - acc: 0.9864 - binary_crossentropy: 0.0606 - val_loss: 0.4332 - val_acc: 0.8650 - val_binary_crossentropy: 0.4332
    Epoch 14/20
     - 5s - loss: 0.0528 - acc: 0.9896 - binary_crossentropy: 0.0528 - val_loss: 0.4589 - val_acc: 0.8640 - val_binary_crossentropy: 0.4589
    Epoch 15/20
     - 5s - loss: 0.0460 - acc: 0.9915 - binary_crossentropy: 0.0460 - val_loss: 0.4782 - val_acc: 0.8636 - val_binary_crossentropy: 0.4782
    Epoch 16/20
     - 5s - loss: 0.0404 - acc: 0.9936 - binary_crossentropy: 0.0404 - val_loss: 0.5011 - val_acc: 0.8617 - val_binary_crossentropy: 0.5011
    Epoch 17/20
     - 5s - loss: 0.0355 - acc: 0.9946 - binary_crossentropy: 0.0355 - val_loss: 0.5238 - val_acc: 0.8601 - val_binary_crossentropy: 0.5238
    Epoch 18/20
     - 5s - loss: 0.0310 - acc: 0.9959 - binary_crossentropy: 0.0310 - val_loss: 0.5618 - val_acc: 0.8597 - val_binary_crossentropy: 0.5618
    Epoch 19/20
     - 5s - loss: 0.0271 - acc: 0.9966 - binary_crossentropy: 0.0271 - val_loss: 0.5733 - val_acc: 0.8586 - val_binary_crossentropy: 0.5733
    Epoch 20/20
     - 5s - loss: 0.0239 - acc: 0.9973 - binary_crossentropy: 0.0239 - val_loss: 0.5941 - val_acc: 0.8590 - val_binary_crossentropy: 0.5941
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    dense_6 (Dense)              (None, 512)               5120512   
    _________________________________________________________________
    dense_7 (Dense)              (None, 512)               262656    
    _________________________________________________________________
    dense_8 (Dense)              (None, 1)                 513       
    =================================================================
    Total params: 5,383,681
    Trainable params: 5,383,681
    Non-trainable params: 0
    _________________________________________________________________
    Train on 25000 samples, validate on 25000 samples
    Epoch 1/20
     - 12s - loss: 0.3449 - acc: 0.8565 - binary_crossentropy: 0.3449 - val_loss: 0.2915 - val_acc: 0.8830 - val_binary_crossentropy: 0.2915
    Epoch 2/20
     - 11s - loss: 0.1326 - acc: 0.9509 - binary_crossentropy: 0.1326 - val_loss: 0.3543 - val_acc: 0.8648 - val_binary_crossentropy: 0.3543
    Epoch 3/20
     - 11s - loss: 0.0385 - acc: 0.9890 - binary_crossentropy: 0.0385 - val_loss: 0.4418 - val_acc: 0.8698 - val_binary_crossentropy: 0.4418
    Epoch 4/20
     - 11s - loss: 0.0063 - acc: 0.9990 - binary_crossentropy: 0.0063 - val_loss: 0.5891 - val_acc: 0.8718 - val_binary_crossentropy: 0.5891
    Epoch 5/20
     - 11s - loss: 9.8004e-04 - acc: 1.0000 - binary_crossentropy: 9.8004e-04 - val_loss: 0.6631 - val_acc: 0.8728 - val_binary_crossentropy: 0.6631
    Epoch 6/20
     - 11s - loss: 2.3721e-04 - acc: 1.0000 - binary_crossentropy: 2.3721e-04 - val_loss: 0.7085 - val_acc: 0.8729 - val_binary_crossentropy: 0.7085
    Epoch 7/20
     - 11s - loss: 1.3367e-04 - acc: 1.0000 - binary_crossentropy: 1.3367e-04 - val_loss: 0.7327 - val_acc: 0.8731 - val_binary_crossentropy: 0.7327
    Epoch 8/20
     - 11s - loss: 9.4933e-05 - acc: 1.0000 - binary_crossentropy: 9.4933e-05 - val_loss: 0.7523 - val_acc: 0.8731 - val_binary_crossentropy: 0.7523
    Epoch 9/20
     - 11s - loss: 7.2803e-05 - acc: 1.0000 - binary_crossentropy: 7.2803e-05 - val_loss: 0.7686 - val_acc: 0.8728 - val_binary_crossentropy: 0.7686
    Epoch 10/20
     - 11s - loss: 5.8082e-05 - acc: 1.0000 - binary_crossentropy: 5.8082e-05 - val_loss: 0.7809 - val_acc: 0.8728 - val_binary_crossentropy: 0.7809
    Epoch 11/20
     - 11s - loss: 4.7624e-05 - acc: 1.0000 - binary_crossentropy: 4.7624e-05 - val_loss: 0.7929 - val_acc: 0.8728 - val_binary_crossentropy: 0.7929
    Epoch 12/20
     - 11s - loss: 3.9747e-05 - acc: 1.0000 - binary_crossentropy: 3.9747e-05 - val_loss: 0.8028 - val_acc: 0.8728 - val_binary_crossentropy: 0.8028
    Epoch 13/20
     - 11s - loss: 3.3734e-05 - acc: 1.0000 - binary_crossentropy: 3.3734e-05 - val_loss: 0.8119 - val_acc: 0.8728 - val_binary_crossentropy: 0.8119
    Epoch 14/20
     - 11s - loss: 2.8974e-05 - acc: 1.0000 - binary_crossentropy: 2.8974e-05 - val_loss: 0.8207 - val_acc: 0.8730 - val_binary_crossentropy: 0.8207
    Epoch 15/20
     - 11s - loss: 2.5101e-05 - acc: 1.0000 - binary_crossentropy: 2.5101e-05 - val_loss: 0.8285 - val_acc: 0.8732 - val_binary_crossentropy: 0.8285
    Epoch 16/20
     - 11s - loss: 2.1962e-05 - acc: 1.0000 - binary_crossentropy: 2.1962e-05 - val_loss: 0.8352 - val_acc: 0.8729 - val_binary_crossentropy: 0.8352
    Epoch 17/20
     - 11s - loss: 1.9356e-05 - acc: 1.0000 - binary_crossentropy: 1.9356e-05 - val_loss: 0.8432 - val_acc: 0.8731 - val_binary_crossentropy: 0.8432
    Epoch 18/20
     - 11s - loss: 1.7169e-05 - acc: 1.0000 - binary_crossentropy: 1.7169e-05 - val_loss: 0.8488 - val_acc: 0.8731 - val_binary_crossentropy: 0.8488
    Epoch 19/20
     - 11s - loss: 1.5288e-05 - acc: 1.0000 - binary_crossentropy: 1.5288e-05 - val_loss: 0.8549 - val_acc: 0.8733 - val_binary_crossentropy: 0.8549
    Epoch 20/20
     - 11s - loss: 1.3696e-05 - acc: 1.0000 - binary_crossentropy: 1.3696e-05 - val_loss: 0.8616 - val_acc: 0.8730 - val_binary_crossentropy: 0.8616
    Train on 25000 samples, validate on 25000 samples
    Epoch 1/20
     - 5s - loss: 0.5584 - acc: 0.7977 - binary_crossentropy: 0.5183 - val_loss: 0.4038 - val_acc: 0.8720 - val_binary_crossentropy: 0.3629
    Epoch 2/20
     - 5s - loss: 0.3198 - acc: 0.9048 - binary_crossentropy: 0.2738 - val_loss: 0.3381 - val_acc: 0.8876 - val_binary_crossentropy: 0.2881
    Epoch 3/20
     - 5s - loss: 0.2625 - acc: 0.9258 - binary_crossentropy: 0.2096 - val_loss: 0.3383 - val_acc: 0.8869 - val_binary_crossentropy: 0.2832
    Epoch 4/20
     - 5s - loss: 0.2360 - acc: 0.9384 - binary_crossentropy: 0.1793 - val_loss: 0.3496 - val_acc: 0.8838 - val_binary_crossentropy: 0.2918
    Epoch 5/20
     - 5s - loss: 0.2226 - acc: 0.9444 - binary_crossentropy: 0.1631 - val_loss: 0.3622 - val_acc: 0.8798 - val_binary_crossentropy: 0.3019
    Epoch 6/20
     - 5s - loss: 0.2091 - acc: 0.9502 - binary_crossentropy: 0.1481 - val_loss: 0.3760 - val_acc: 0.8769 - val_binary_crossentropy: 0.3147
    Epoch 7/20
     - 5s - loss: 0.2018 - acc: 0.9530 - binary_crossentropy: 0.1395 - val_loss: 0.3891 - val_acc: 0.8734 - val_binary_crossentropy: 0.3261
    Epoch 8/20
     - 5s - loss: 0.1934 - acc: 0.9567 - binary_crossentropy: 0.1297 - val_loss: 0.3999 - val_acc: 0.8723 - val_binary_crossentropy: 0.3361
    Epoch 9/20
     - 5s - loss: 0.1876 - acc: 0.9576 - binary_crossentropy: 0.1234 - val_loss: 0.4165 - val_acc: 0.8708 - val_binary_crossentropy: 0.3518
    Epoch 10/20
     - 5s - loss: 0.1863 - acc: 0.9590 - binary_crossentropy: 0.1208 - val_loss: 0.4324 - val_acc: 0.8658 - val_binary_crossentropy: 0.3665
    Epoch 11/20
     - 5s - loss: 0.1800 - acc: 0.9622 - binary_crossentropy: 0.1137 - val_loss: 0.4393 - val_acc: 0.8662 - val_binary_crossentropy: 0.3725
    Epoch 12/20
     - 5s - loss: 0.1762 - acc: 0.9629 - binary_crossentropy: 0.1095 - val_loss: 0.4561 - val_acc: 0.8625 - val_binary_crossentropy: 0.3891
    Epoch 13/20
     - 5s - loss: 0.1752 - acc: 0.9629 - binary_crossentropy: 0.1073 - val_loss: 0.4601 - val_acc: 0.8643 - val_binary_crossentropy: 0.3919
    Epoch 14/20
     - 5s - loss: 0.1654 - acc: 0.9684 - binary_crossentropy: 0.0974 - val_loss: 0.4675 - val_acc: 0.8608 - val_binary_crossentropy: 0.3999
    Epoch 15/20
     - 5s - loss: 0.1592 - acc: 0.9708 - binary_crossentropy: 0.0917 - val_loss: 0.4790 - val_acc: 0.8616 - val_binary_crossentropy: 0.4114
    Epoch 16/20
     - 5s - loss: 0.1559 - acc: 0.9723 - binary_crossentropy: 0.0880 - val_loss: 0.4906 - val_acc: 0.8592 - val_binary_crossentropy: 0.4226
    Epoch 17/20
     - 5s - loss: 0.1538 - acc: 0.9722 - binary_crossentropy: 0.0857 - val_loss: 0.4978 - val_acc: 0.8580 - val_binary_crossentropy: 0.4293
    Epoch 18/20
     - 5s - loss: 0.1515 - acc: 0.9730 - binary_crossentropy: 0.0827 - val_loss: 0.5072 - val_acc: 0.8593 - val_binary_crossentropy: 0.4379
    Epoch 19/20
     - 5s - loss: 0.1480 - acc: 0.9746 - binary_crossentropy: 0.0788 - val_loss: 0.5159 - val_acc: 0.8595 - val_binary_crossentropy: 0.4464
    Epoch 20/20
     - 5s - loss: 0.1453 - acc: 0.9756 - binary_crossentropy: 0.0755 - val_loss: 0.5298 - val_acc: 0.8558 - val_binary_crossentropy: 0.4596
    Train on 25000 samples, validate on 25000 samples
    Epoch 1/20
     - 6s - loss: 0.6409 - acc: 0.6162 - binary_crossentropy: 0.6409 - val_loss: 0.5187 - val_acc: 0.8435 - val_binary_crossentropy: 0.5187
    Epoch 2/20
     - 5s - loss: 0.4922 - acc: 0.7780 - binary_crossentropy: 0.4922 - val_loss: 0.3810 - val_acc: 0.8824 - val_binary_crossentropy: 0.3810
    Epoch 3/20
     - 5s - loss: 0.3860 - acc: 0.8527 - binary_crossentropy: 0.3860 - val_loss: 0.3089 - val_acc: 0.8864 - val_binary_crossentropy: 0.3089
    Epoch 4/20
     - 5s - loss: 0.3196 - acc: 0.8917 - binary_crossentropy: 0.3196 - val_loss: 0.2820 - val_acc: 0.8868 - val_binary_crossentropy: 0.2820
    Epoch 5/20
     - 5s - loss: 0.2628 - acc: 0.9102 - binary_crossentropy: 0.2628 - val_loss: 0.2768 - val_acc: 0.8890 - val_binary_crossentropy: 0.2768
    Epoch 6/20
     - 5s - loss: 0.2295 - acc: 0.9249 - binary_crossentropy: 0.2295 - val_loss: 0.2855 - val_acc: 0.8877 - val_binary_crossentropy: 0.2855
    Epoch 7/20
     - 5s - loss: 0.1997 - acc: 0.9352 - binary_crossentropy: 0.1997 - val_loss: 0.2991 - val_acc: 0.8848 - val_binary_crossentropy: 0.2991
    Epoch 8/20
     - 5s - loss: 0.1733 - acc: 0.9440 - binary_crossentropy: 0.1733 - val_loss: 0.3232 - val_acc: 0.8822 - val_binary_crossentropy: 0.3232
    Epoch 9/20
     - 5s - loss: 0.1560 - acc: 0.9499 - binary_crossentropy: 0.1560 - val_loss: 0.3375 - val_acc: 0.8823 - val_binary_crossentropy: 0.3375
    Epoch 10/20
     - 5s - loss: 0.1409 - acc: 0.9548 - binary_crossentropy: 0.1409 - val_loss: 0.3519 - val_acc: 0.8799 - val_binary_crossentropy: 0.3519
    Epoch 11/20
     - 5s - loss: 0.1216 - acc: 0.9602 - binary_crossentropy: 0.1216 - val_loss: 0.3782 - val_acc: 0.8796 - val_binary_crossentropy: 0.3782
    Epoch 12/20
     - 5s - loss: 0.1134 - acc: 0.9634 - binary_crossentropy: 0.1134 - val_loss: 0.4102 - val_acc: 0.8778 - val_binary_crossentropy: 0.4102
    Epoch 13/20
     - 5s - loss: 0.1025 - acc: 0.9660 - binary_crossentropy: 0.1025 - val_loss: 0.4156 - val_acc: 0.8769 - val_binary_crossentropy: 0.4156
    Epoch 14/20
     - 5s - loss: 0.0936 - acc: 0.9684 - binary_crossentropy: 0.0936 - val_loss: 0.4446 - val_acc: 0.8778 - val_binary_crossentropy: 0.4446
    Epoch 15/20
     - 5s - loss: 0.0883 - acc: 0.9695 - binary_crossentropy: 0.0883 - val_loss: 0.4888 - val_acc: 0.8729 - val_binary_crossentropy: 0.4888
    Epoch 16/20
     - 5s - loss: 0.0809 - acc: 0.9718 - binary_crossentropy: 0.0809 - val_loss: 0.4867 - val_acc: 0.8762 - val_binary_crossentropy: 0.4867
    Epoch 17/20
     - 5s - loss: 0.0762 - acc: 0.9721 - binary_crossentropy: 0.0762 - val_loss: 0.5257 - val_acc: 0.8755 - val_binary_crossentropy: 0.5257
    Epoch 18/20
     - 5s - loss: 0.0701 - acc: 0.9735 - binary_crossentropy: 0.0701 - val_loss: 0.5329 - val_acc: 0.8748 - val_binary_crossentropy: 0.5329
    Epoch 19/20
     - 5s - loss: 0.0681 - acc: 0.9752 - binary_crossentropy: 0.0681 - val_loss: 0.5602 - val_acc: 0.8725 - val_binary_crossentropy: 0.5602
    Epoch 20/20
     - 5s - loss: 0.0628 - acc: 0.9760 - binary_crossentropy: 0.0628 - val_loss: 0.5939 - val_acc: 0.8746 - val_binary_crossentropy: 0.5939

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转载于:https://www.cnblogs.com/anliven/p/10817124.html

[https_www.tensorflow.org_tutorials_keras_overfit_and_underfit]: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit
[https_github.com_anliven_Hello-AI_blob_master_Google-Learn-and-use-ML_4_overfit_and_underfit.py]: https://github.com/anliven/Hello-AI/blob/master/Google-Learn-and-use-ML/4_overfit_and_underfit.py
[819128-20190505232625831-474255104.png]: /images/20211220/89a2b46d555e44b18caaba6d20e3f9bb.png
[819128-20190505232637182-591195105.png]: /images/20211220/ab3c79829900438993b4154625d1c48f.png
[819128-20190505232645474-2027971502.png]: /images/20211220/eb4d6ed8cc2c4cce98df323c0a11aeec.png
[819128-20190505232655294-53398567.png]: /images/20211220/8b30212581574891bccb065a19f7121d.png