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i canot get your result #17
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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'))
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)
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