官网实例详解4.5（cifar10_cnn.py）-keras学习笔记四
基于CIFAR10（小批量图片）数据集训练简单的深度卷积神经网络
实际项目应用（人脸识别-区分人脸）
人脸检测和识别（中文标记）完整项目源代码
（基于深度学习+python3.6+dlib+PIL+CNN+（tensorflow、keras）10分钟实现 区分欢乐颂中人物详细图文教程和完整项目代码）
https://blog.csdn.net/wyx100/article/details/80428424
名词解释
Cifar-10 是由 Hinton 的学生 Alex Krizhevsky、Ilya Sutskever 收集的一个用于普适物体识别的数据集。
官网：http://www.cs.toronto.edu/~kriz/cifar.html
Cifar 是加拿大政府牵头投资的一个先进科学项目研究所。Hinton、Bengio和他的学生在2004年拿到了 Cifar 投资的少量资金，建立了神经计算和自适应感知项目。这个项目结集了不少计算机科学家、生物学家、电气工程师、神经科学家、物理学家、心理学家，加速推动了 Deep Learning 的进程。从这个阵容来看，DL 已经和 ML 系的数据挖掘分的很远了。Deep Learning 强调的是自适应感知和人工智能，是计算机与神经科学交叉；Data Mining 强调的是高速、大数据、统计数学分析，是计算机和数学的交叉。
Cifar-10 由60000张32*32的 RGB 彩色图片构成，共10个分类。50000张训练，10000张
测试
（交叉验证）。这个数据集最大的特点在于将识别迁移到了普适物体，而且应用于多分类（姊妹数据集Cifar-100达到100类，ILSVRC比赛则是1000类）。
可以看到，同已经成熟的人脸识别相比，普适物体识别挑战巨大，数据中含有大量特征、噪声，识别物体比例不一。因而，Cifar-10 相对于传统图像识别数据集，是相当有挑战的。想了解更多信息请参考
CIFAR-10 page
，以及 Alex Krizhevsky 的
技术报告
。
PCA 与 ZCA详解
Keras ImageDataGenerator参数详解
代码注释
'''Train a simple deep CNN on the CIFAR10 small images dataset.
基于CIFAR10（小批量图片）数据集训练简单的深度卷积神经网络
It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
25个周期后达到75%的精确度，50个周期后达到79%的精确度
(it's still underfitting at that point, though).
（但是50个周期后，仍有欠拟合（训练集精度不高））
'''
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
batch_size = 32 # 每个批次样本（数据记录）数
num_classes = 10 # 10分类
epochs = 100 # 100个周期
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models') # 训练的模型保存路径
model_name = 'keras_cifar10_trained_model.h5' # 训练的模型名称
# The data, shuffled and split between train and test sets:
# 筛选（数据顺序打乱）、分割训练集和测试集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
# 类别向量转为多分类矩阵
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# 建立基于keras的cnn模型
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
# initiate RMSprop optimizer
# 均方根反向传播（RMSprop，root mean square prop）优化
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
# 使用均方根反向传播（RMSprop）训练模型
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
# 预处理和实时数据扩大（通过平移、翻转等图像变换增加图像样本数量）。
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset # 基于数据集，使输入数据平均值为0
samplewise_center=False, # set each sample mean to 0 # 使样本平均值为0
featurewise_std_normalization=False, # divide inputs by std of the dataset # 通过数据标准化划分输入数据
samplewise_std_normalization=False, # divide each input by its std # 通过标准化划分输入数据
zca_whitening=False, # apply ZCA（Zero-phase Component Analysis） whitening # 对输入数据施加ZCA白化
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180) # 旋转图像0-180度
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) # 水平平移图像（基于图像宽度比例）
height_shift_range=0.1, # randomly shift images vertically (fraction of total height) # 垂直平移图像（基于图像高度比例）
horizontal_flip=True, # randomly flip images # 水平翻转图像
vertical_flip=False) # randomly flip images # 垂直翻转图像
# Compute quantities required for feature-wise normalization
# 特征归一化的计算量
# (std, mean, and principal components if ZCA whitening is applied).
# （如果ZCA白化（一种降维方法）会使用标准化、均值和主成分方法）
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
# 使用datagen.flow()生成的批次数据在模型训练
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4)
# Save model and weights
# 保存模型和权重（数据）
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# Score trained model.
# 评估训练的模型
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])执行过程
C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/cifar10_cnn.py
Using TensorFlow backend.
x_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
Using real-time data augmentation.
Epoch 1/100
1/1563 [..............................] - ETA: 1:03:06 - loss: 2.2554 - acc: 0.1250
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8800/10000 [=========================>....] - ETA: 0s
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10000/10000 [==============================] - 2s 170us/step
Test loss: 0.739190111351
Test accuracy: 0.7629
Process finished with exit code 0Keras详细介绍
英文：https://keras.io/
中文：http://keras-cn.readthedocs.io/en/latest/
实例下载
https://github.com/keras-team/keras
https://github.com/keras-team/keras/tree/master/examples
