此为改编自cifar10的三分类问题：
from __future__ import print_function #此为在老版本的python中兼顾新特性的一种方法
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.models import Sequential
from keras.models import model_from_json
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from keras import optimizers
from keras import backend as K
from keras import regularizers
from keras.applications.imagenet_utils import preprocess_input
import scipy.misc
import numpy as np
import matplotlib.pyplot as plt
import h5py
#from keras.datasets import cifar10
#from keras.layers.core import Lambda
#from matplotlib.pyplot import imshow
class teeth3vgg:
def __init__(self,train=False):
self.num_classes = 3
self.weight_decay = 0.0005 #权值衰减,目的是防止过拟合
self.x_shape = [32,32,3]
self.model = self.build_model()
if train:
self.model = self.train(self.model)
else:
#self.model.load_weights('weight.h5')
# 加载模型数据和weights
self.model = model_from_json(open('my_model_architecture.json').read())
self.model.load_weights('my_model_weights.h5')
def build_model(self):
# Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.
model = Sequential()
weight_decay = self.weight_decay
model.add(Conv2D(64, (3, 3), padding='same',input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay)))
#kernel_regularizer表示施加在权重上的正则项
model.add(Activation('relu'))
model.add(BatchNormalization())
#该层在每个batch（批次）上将前一层的激活值重新规范化，即使得其输出数据的均值接近0，其标准差接近1
model.add(Dropout(0.3))
model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten()) #即把多维的输入一维化，常用于卷积层到全连接层的过渡
model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(self.num_classes)) #该全连接层的输出维度为3
model.add(Activation('softmax'))
return model
def normalize(self, X_train, X_valida, X_test):
#this function normalize inputs for zero mean and unit variance
# it is used when training a model.
# Input: training set and test set
# Output: normalized training set and test set according to the trianing set statistics.
mean = np.mean(X_train, axis=(0, 1, 2, 3)) #计算均值
std = np.std(X_train, axis=(0, 1, 2, 3)) #计算标准差
X_train = (X_train-mean)/(std+1e-7)
X_valida = (X_valida- mean)/(std + 1e-7)
X_test = (X_test-mean)/(std+1e-7)
return X_train, X_valida, X_test
def normalize_production(self,x):
#this function is used to normalize instances in production according to saved training set statistics
# Input: X - a training set
# Output X - a normalized training set according to normalization constants.
#these values produced during first training and are general for the standard cifar10 training set normalization
mean = np.mean(x)
std = np.std(x)
return (x-mean)/(std+1e-7)
#'''
def predict(self, x, normalize=True, batch_size=50):
if normalize:
x = self.normalize_production(x)
return self.model.predict(x, batch_size)
#'''
def train(self,model):
#training parameters
#batch_size = 128
batch_size = 100
#maxepoches = 250
maxepoches = 150
learning_rate = 0.01
lr_decay = 1e-6
lr_drop = 20
# The data, shuffled and split between train and test sets:
#(x_train, y_train), (x_test, y_test) = cifar10.load_data()
train_dataset = h5py.File('data.h5', 'r')
x_train = np.array(train_dataset['X_train'][:]) # your train set features
y_train = np.array(train_dataset['y_train'][:]) # your train set labels
x_valida = np.array(train_dataset['X_valida'][:]) # your valida set features
y_valida = np.array(train_dataset['y_valida'][:]) # your valida set labels
x_test = np.array(train_dataset['X_test'][:]) # your test set features
y_test = np.array(train_dataset['y_test'][:]) # your test set labels
train_dataset.close()
x_train = x_train.astype('float32')
x_valida = x_valida.astype('float32')
x_test = x_test.astype('float32')
x_train, x_valida, x_test = self.normalize(x_train, x_valida, x_test)
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_valida = keras.utils.to_categorical(y_valida, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
def lr_scheduler(epoch):
return learning_rate * (0.5 ** (epoch // lr_drop))
reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)
#data augmentation
datagen = ImageDataGenerator( #图片生成器
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
print('***********************')
print(x_train.shape)
#optimization details #优化器
sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])
# training process in a for loop with learning rate drop every 25 epoches.
historytemp = model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=maxepoches,
validation_data=(x_valida, y_valida), callbacks=[reduce_lr],verbose=2) #verbose用来设置进度条
# verbose是屏显模式，官方这么说的：verbose: 0 for no logging to stdout, 1 for progress bar logging, 2 for one log line per epoch.
# 就是说0是不屏显，1是显示一个进度条，2是每个epoch都显示一行数据
#model.save_weights('weight.h5')
json_string = model.to_json() # 等价于 json_string = model.get_config()
open('my_model_architecture.json', 'w').write(json_string)
model.save_weights('my_model_weights.h5')
return model
if __name__ == '__main__':
#(x_train, y_train), (x_test, y_test) = cifar10.load_data()
train_dataset = h5py.File('data.h5', 'r')
x_train = np.array(train_dataset['X_train'][:]) # your train set features
y_train = np.array(train_dataset['y_train'][:]) # your train set labels
x_valida = np.array(train_dataset['X_valida'][:]) # your valida set features
y_valida = np.array(train_dataset['y_valida'][:]) # your valida set labels
x_test = np.array(train_dataset['X_test'][:]) # your test set features
y_test = np.array(train_dataset['y_test'][:]) # your test set labels
#train_dataset.close()
x_train = x_train.astype('float32')
x_valida = x_valida.astype('float32')
x_test = x_test.astype('float32')
y_train = keras.utils.to_categorical(y_train, 3) #把类别转化成了one-hot的格式
y_valida = keras.utils.to_categorical(y_valida, 3)
y_test = keras.utils.to_categorical(y_test, 3)
print('读取数据成功啦！来看看标签吧！')
print(y_train[0])
print(y_train[1])
print(y_train[2])
print(y_train[3])
print(y_train[4])
print(y_train[5])
print(y_train[6])
print(y_train[7])
print(y_train[8])
print(y_train[9])
print(y_train[10])
#print(x_train[0])
model = teeth3vgg()
print('模型建立完毕！')
#对验证集进行正向传播评估正确率
predicted_x = model.predict(x_valida)
print(model.predict(x_valida))
residuals = (np.argmax(predicted_x, 1) == np.argmax(y_valida, 1)) #argmax返回的是最大数的索引
print('------------------------***')
print(residuals)
print(sum(residuals))
print(len(residuals))
loss = sum(residuals)/len(residuals)
print("the validation 0/1 loss is: ",loss)
# 对测试集进行正向传播评估正确率
predicted_x1 = model.predict(x_test)
print(predicted_x1.shape)
print(y_test.shape)
#print(model.predict(x_test))
residuals1 = (np.argmax(predicted_x1, 1) == np.argmax(y_test, 1)) # argmax返回的是最大数的索引
print('------------------------***')
#print(residuals1)
print(sum(residuals1))
print(len(residuals1))
loss1 = sum(residuals1) / len(residuals1)
print("the test 0/1 loss is: ", loss1)
'''
score = model.evaluate(x_test, y_test, batch_size=10, verbose=1)
print("loss :",score[0])
print("acc :", score[1])
score = model.evaluate(x_test, y_test, verbose=0)
print('Val loss:', score[0])
print('Val accuracy:', score[1])
'''
#model.evaluate(x_test, y_test, batch_size=100, show_accuracy=True, verbose=1) # show_accuracy就是显示每次迭代后的正确率
img_path = 'E:/MatlabWorkspace/picdst14/247.jpg'
img = image.load_img(img_path, target_size=(32, 32))
x = image.img_to_array(img)
print('Source input image shape:', x.shape)
x = np.expand_dims(x, 0) #增加维度，在下标为dim=0的轴上增加一维；-1表示最后一维
x = preprocess_input(x) #数据预处理，提高算法的效率
print('Input image shape:', x.shape)
my_image = scipy.misc.imread(img_path)
plt.imshow(my_image)
print("class prediction vector [p(0), p(1), p(2)] = ")
px=model.predict(x)
print(px)
#print(model.predict(x_test[3]))
if np.argmax(px,1)==0:
print('该区域为牙齿！')
elif np.argmax(px,1)==1:
print('该区域为牙菌斑！')
else:
print('该区域为其他！')
K.clear_session()
'''
fig, ax = plt.subplots(2, 1)
ax[0].plot(history.history['loss'], color='b', label="Training loss")
ax[0].plot(history.history['val_loss'], color='r', label="validation loss", axes=ax[0])
ax[0].legend(loc='best', shadow=True)
ax[1].plot(history.history['acc'], color='b', label="Training accuracy")
ax[1].plot(history.history['val_acc'], color='r', label="Validation accuracy")
ax[1].legend(loc='best', shadow=True)
plt.show()
'''
张量：A tensor is something that transforms like a tensor! 一个量，在不同的参考系下按照某种特定的法则进行变换，就是张量。
YouTube视频：https://www.youtube.com/watch?v=f5liqUk0ZTw