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lenet_log.py
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import keras
import datetime
import numpy as np
from keras import optimizers
from keras.datasets import cifar10
from keras.models import Sequential, load_model
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.callbacks import LearningRateScheduler, TensorBoard, ModelCheckpoint, ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from keras.regularizers import l2,l1
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
#from networks.train_plot import PlotLearning
def mkdir(path):
# 引入模块
import os
# 去除首位空格
path = path.strip()
# 去除尾部 \ 符号
path = path.rstrip("\\")
# 判断路径是否存在
# 存在 True
# 不存在 False
isExists = os.path.exists(path)
# 判断结果
if not isExists:
# 如果不存在则创建目录
# 创建目录操作函数
os.makedirs(path)
print(path + ' 创建成功')
return True
else:
# 如果目录存在则不创建，并提示目录已存在
print(path + ' 目录已存在')
return False
# Code taken from https://github.com/BIGBALLON/cifar-10-cnn
class LeNet1:
def __init__(self, epochs=10000, batch_size=512, load_weights=True):
self.name = 'lenet_log'
self.model_filename = './lenet_log.h5'
self.num_classes = 2
self.input_shape = 28, 28, 1
self.batch_size = batch_size
self.epochs = epochs
self.iterations = 400
self.weight_decay = 0.00005
self.log_filepath = r'./lenet_log_tensorboard/'
if load_weights:
try:
self._model = load_model(self.model_filename)
print('Successfully loaded', self.name)
except (ImportError, ValueError, OSError):
print('Failed to load', self.name)
def count_params(self):
return self._model.count_params()
def color_preprocessing(self, x_train, x_test):
x_train = x_train.astype('float64')
x_test = x_test.astype('float64')
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0887, 66.7048]
for i in range(3):
x_train[:, :, :, i] = (x_train[:, :, :, i] - mean[i]) / std[i]
x_test[:, :, :, i] = (x_test[:, :, :, i] - mean[i]) / std[i]
return x_train, x_test
def build_model(self):
model = Sequential()
model.add(Conv2D(6, (5, 5), padding='valid', activation='relu', kernel_initializer='he_normal',
kernel_regularizer=l2(self.weight_decay), input_shape=self.input_shape))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(16, (5, 5), padding='valid', activation='relu', kernel_initializer='he_normal',
kernel_regularizer=l2(self.weight_decay)))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(
Dense(120, activation='relu', kernel_initializer='he_normal', kernel_regularizer=l2(self.weight_decay)))
model.add(
Dense(84, activation='relu', kernel_initializer='he_normal', kernel_regularizer=l2(self.weight_decay)))
model.add(
Dense(2, activation='softmax', kernel_initializer='he_normal', kernel_regularizer=l2(self.weight_decay)))
sgd = optimizers.SGD(lr=0.0000095,)#0.000001
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
def scheduler(self, epoch):
print(epoch, '--------------------------')
if epoch <= 20:
return 0.0004
if epoch <= 30:
return 0.00008
if epoch <= 50:
return 0.00002
return 0.000004
def train(self):
# data_path = '/home/ailab/YI ZENG/Research/Classified/tra/DTrafficR/2_FusionedDataset/Numpy/data_norm8CLS_ALL.npy'
# label_path = '/home/ailab/YI ZENG/Research/Classified/tra/DTrafficR/2_FusionedDataset/Numpy/label_norm8CLS_ALL.npy'
# data = np.load(data_path)
# label_n = np.load(label_path)
# print(label_n[1:10])
# print('label的数量', label_n.shape)
# print("label的格式为：", type(label_n))
# data = data.reshape([-1, 28, 28, 1])
# # data = data*256
# x_train = data[:12417]
# y_train = label_n[:12417]
#
# x_test = data[12417:]
# y_test = label_n[12417:]
#
# x_test = x_test * 256
# x_test = x_test.astype(int)
# x_train = x_train * 256
# x_train = x_train.astype(int)
# y_test = y_test.astype(int)
# y_train = y_train.astype(int)
#
# # (x_train, y_train), (x_test, y_test) = cifar10.load_data()
# y_train = keras.utils.to_categorical(y_train, self.num_classes)
# y_test = keras.utils.to_categorical(y_test, self.num_classes)
# color preprocessing
# x_train, x_test = self.color_preprocessing(x_train, x_test)
train_data_path = './data_dfr_log/train_data.npy'
train_label_path = './data_dfr_log/train_label.npy'
test_data_path = './data_dfr_log/test_data.npy'
test_label_path = './data_dfr_log/test_label.npy'
train_data = np.load(train_data_path)
train_label = np.load(train_label_path)
test_data = np.load(test_data_path)
test_label = np.load(test_label_path)
print('train_data的数量为:', train_data.shape)
print('train_label的数量为:', train_label.shape)
print('test_data的数量为:', test_data.shape)
print('test_label的数量为:', test_label.shape)
train_data = train_data.reshape([-1, 28, 28, 1])
# train_label = train_label.reshape([-1, 28, 28, 1])
test_data = test_data.reshape([-1, 28, 28, 1])
# test_label = test_label.reshape([-1, 28, 28, 1])
print('train_data的数量为:', train_data.shape)
print('train_label的数量为:', train_label.shape)
print('test_data的数量为:', test_data.shape)
print('test_label的数量为:', test_label.shape)
# 数据归一化到【0：255】
self.x_test = test_data.astype(int)
self.x_train = train_data.astype(int)
self.x_test = 2 * self.x_test
self.x_train = 2 * self.x_train
y_train = keras.utils.to_categorical(train_label, self.num_classes)
y_test = keras.utils.to_categorical(test_label, self.num_classes)
self.y_test = y_test.astype(int)
self.y_train = y_train.astype(int)
# build network
model = self.build_model()
model.summary()
mkdir(self.model_filename + 'date_' + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
# Save the best model during each training checkpoint
change_lr = LearningRateScheduler(self.scheduler)
checkpoint = ModelCheckpoint(self.model_filename + 'date_' + datetime.datetime.now().strftime(
"%Y%m%d-%H%M%S") + '/' + 'epoch_' + '{epoch:02d}' + '_val_acc_' + '{val_acc:.4f}' + '.h5',
monitor='val_acc',
verbose=0,
save_best_only=True,
mode='auto')
plot_callback = PlotLearning()
tb_cb = TensorBoard(log_dir=self.log_filepath + '/' + datetime.datetime.now().strftime(
"%Y%m%d-%H%M%S"), histogram_freq=0)
reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.1, verbose=1,
patience=20, min_lr=0.000000001)
# tb_cb = TensorBoard(
# log_dir=self.log_filepath + 'date_' + datetime.datetime.now().strftime(
# "%Y%m%d-%H%M%S") + '_conv_l1_' + str(self.conv_l1_regularizer) + '_lstm_l1_' + str(
# self.lstm_l1_regularizer),
# histogram_freq=0)
cbks = [checkpoint, tb_cb, ]
# using real-time data augmentation
print('Using real-time data augmentation.')
# datagen = ImageDataGenerator(horizontal_flip=True,
# width_shift_range=0.125, height_shift_range=0.125, fill_mode='constant', cval=0.)
#
# datagen.fit(x_train)
# start traing
# model.fit_generator(datagen.flow(x_train, y_train,batch_size=self.batch_size),
# steps_per_epoch=self.iterations,
# verbose=2,
# epochs=self.epochs,
# callbacks=cbks,
# validation_data=(x_test, y_test))
model.fit(x=self.x_train, y=self.y_train, batch_size=self.batch_size,
verbose=2,
epochs=self.epochs,
callbacks=cbks,
validation_data=(self.x_test, self.y_test))
# save model
model.save(self.model_filename + '.h5')
self._model = model
def color_process(self, imgs):
if imgs.ndim < 4:
imgs = np.array([imgs])
imgs = imgs.astype('float64')
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0887, 66.7048]
for img in imgs:
for i in range(1):
img[:, :, i] = (img[:, :, i] - mean[i]) / std[i]
return imgs
def predict(self, img):
# processed = self.color_process(img)
img = img.astype('float64')
processed = img
# model = load_model('lenet.h5')
# result_test=model.predict(processed)
# print(result_test,"please sucess!!!!!!!!!!!!!!!!!!")
# return result_test
# print(model)
# print(self._model.predict(processed, batch_size=self.batch_size))
# print('processed shape is:')
# print(processed.shape)
# print('test 1', processed.shape)
# print('test 2', processed.shape)
return self._model.predict(processed, batch_size=self.batch_size)
def predict_one(self, img):
# print('g-------------------------------------------------------')
return self.predict(img)[0]
def accuracy(self):
# data_path = '/home/ailab/YI ZENG/Research/Classified/tra/DTrafficR/2_FusionedDataset/Numpy/data_norm8CLS_ALL.npy'
# label_path = '/home/ailab/YI ZENG/Research/Classified/tra/DTrafficR/2_FusionedDataset/Numpy/label_norm8CLS_ALL.npy'
# data = np.load(data_path)
# label_n = np.load(label_path)
# print(label_n[1:10])
# print('label的数量', label_n.shape)
# print("label的格式为：", type(label_n))
# data = data.reshape([-1, 28, 28, 1])
# x_train = data[:12417]
# y_train = label_n[:12417]
#
# x_test = data[12417:]
# y_test = label_n[12417:]
#
# # (x_train, y_train), (x_test, y_test) = cifar10.load_data()
# y_train = keras.utils.to_categorical(y_train, self.num_classes)
# y_test = keras.utils.to_categorical(y_test, self.num_classes)
#
# # color preprocessing
# # x_train, x_test = self.color_preprocessing(x_train, x_test)
return self._model.evaluate(self.x_test, self.y_test, verbose=0)[1]
if __name__ == '__main__':
lenet = LeNet1()
lenet.train()
print(lenet.accuracy())