資源簡(jiǎn)介
在進(jìn)行論文寫(xiě)作時(shí),經(jīng)常要對(duì)深度學(xué)習(xí)模型的分類結(jié)果進(jìn)行描述,采用t-sne對(duì)網(wǎng)絡(luò)進(jìn)行可視化是絕大多數(shù)高水平論文的必要內(nèi)容之一。在本資源中,采用卷積神經(jīng)網(wǎng)絡(luò)對(duì)minist數(shù)據(jù)集進(jìn)行識(shí)別分類,并采用t-sne可視化卷積神經(jīng)網(wǎng)絡(luò),保存可用于論文的圖形。
代碼片段和文件信息
#?-*-?coding:?utf-8?-*-
“““
Created?on?Wed?Jul?15?17:19:54?2020
@author:?xxxx
“““
from?keras.datasets?import?mnist
from?keras.utils?import?to_categorical
?
train_X?train_y?=?mnist.load_data()[0]
train_X?=?train_X.reshape(-1?28?28?1)
train_X?=?train_X.astype(‘float32‘)
train_X?/=?255
train_y?=?to_categorical(train_y?10)
?
from?keras.models?import?Sequential
from?keras.layers?import?Conv2D?MaxPool2D?Flatten?Dropout?Dense
from?keras.losses?import?categorical_crossentropy
from?keras.optimizers?import?Adadelta
import?matplotlib.pyplot?as?plt
import?keras
from?keras?import?layers
import?tensorflow?as?tf
import?numpy?as?np
from?keras.utils?import?np_utils
from?sklearn.decomposition?import?PCA
from?sklearn.manifold?import?TSNE
?
- 上一篇:Python 凸包算法
- 下一篇:sqli-labs盲注腳本
評(píng)論
共有 條評(píng)論