i canot get your result

[gaotihonglucky123]

i get accuracy is 10%, if i just modify the x_Train by the way:
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0087, 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]
but i get accuracy 52%,if i modify the x_Train by the way:
x_train /= 255
x_test /= 255

i donnot know why i cannot get the same result with you?
please help.thx.

my code is :
import keras
from keras import optimizers
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Conv2D,Dense, Flatten, MaxPooling2D
from keras.callbacks import LearningRateScheduler, TensorBoard

batch_size = 128
epochs = 10
iteration = 391
num_classes = 10
log_filepath = './lenet'

##kernel_initializer:?????
def build_model():
model = Sequential()
model.add(Conv2D(6, (5,5), padding = 'valid', activation = 'relu', kernel_initializer = 'he_normal', input_shape = (32, 32, 3)))
model.add(MaxPooling2D((2,2),strides = (2,2)))
model.add(Conv2D(16, (5,5), padding = 'valid', activation = 'relu', kernel_initializer = 'he_normal'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(Flatten())
model.add(Dense(120, activation = 'relu', kernel_initializer = 'he_normal'))
model.add(Dense(84, activation = 'relu', kernel_initializer = 'he_normal'))
model.add(Dense(num_classes, activation = 'softmax', kernel_initializer = 'he_normal'))

sgd = optimizers.SGD(lr = 0.1, momentum = 0.9, nesterov = True)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])

return model

def scheduler(epoch):
learning_rate_init = 0.02
if epoch >= 80:
learning_rate_init = 0.01
if epoch >= 150:
learning_rate_init = 0.004
return learning_rate_init

if name == 'main':
(x_train, y_train), (x_test, y_test) = cifar10.load_data() ## values ???
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# x_train /= 255
# x_test /= 255
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0087, 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]
model = build_model()
print(model.summary())

tb_cb = TensorBoard(log_dir = log_filepath, histogram_freq = 0)
change_lr = LearningRateScheduler(scheduler)
cbks = [tb_cb, change_lr]

model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, callbacks = cbks, validation_data = (x_test, y_test), shuffle = True)

model.save('lenet.h5')