import tensorflow as tf import os from matplotlib import pyplot as plt import tensorflow.keras.datasets from tensorflow.keras import Model import numpy as np from tensorflow.keras.layers import Dense,Flatten,BatchNormalization,Dropout,Conv2D,Activati
import numpy as np import cPickle import keras as ks from keras.layers import Dense, Activation, Flatten, convolutional, Convolution2D, MaxPooling2D, Dropout from keras.utils import np_utils import logging def read_data(file): with open(file,'rb') as
第一次用卷积,看的别人的模型跑的CIFAR-10,不过吐槽一下...我觉着我的965m加速之后比我的cpu算起来没快多少..正确率64%的样子,没达到模型里说的75%,不知道问题出在哪里 import numpy as np import os import mxnet as mx import logging import cPickle def unpickle(file): with open(file,'rb') as fo: dict = cPickle.load(fo) return
AlexNet详细解读 目前在自学计算机视觉与深度学习方向的论文,今天给大家带来的是很经典的一篇文章 :<ImageNet Classification with Deep Convolutional Neural Networks>.纯粹是自学之后,自己的一点知识总结,如果有什么不对的地方欢迎大家指正.AlexNet的篇文章当中,我们可以主要从五个大方面去讲:ReLU,LPN,Overlapping Pooling,总体架构,减少过度拟合.重点介绍总体结构和减少过度拟合. 1. ReLU N