conv1d UpSampling1D aotoencoder 自编码代码摘录
https://www.quora.com/How-do-I-implement-a-1D-Convolutional-autoencoder-in-Keras-for-numerical-dataset
# ENCODER
input_sig = Input(batch_shape=(None,128,1))
x = Conv1D(64,3, activation='relu', padding='valid')(input_sig)
x1 = MaxPooling1D(2)(x)
x2 = Conv1D(32,3, activation='relu', padding='valid')(x1)
x3 = MaxPooling1D(2)(x2)
flat = Flatten()(x3)
encoded = Dense(32,activation = 'relu')(flat) print("shape of encoded {}".format(K.int_shape(encoded))) # DECODER
x2_ = Conv1D(32, 3, activation='relu', padding='valid')(x3)
x1_ = UpSampling1D(2)(x2_)
x_ = Conv1D(64, 3, activation='relu', padding='valid')(x1_)
upsamp = UpSampling1D(2)(x_)
flat = Flatten()(upsamp)
decoded = Dense(128,activation = 'relu')(flat)
decoded = Reshape((128,1))(decoded) print("shape of decoded {}".format(K.int_shape(decoded))) autoencoder = Model(input_sig, decoded)
autoencoder.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
http://qaru.site/questions/418926/keras-autoencoder-error-when-checking-target
from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling1D
from keras.models import Model
from keras import backend as K
import scipy as scipy
import numpy as np mat = scipy.io.loadmat('edata.mat')
emat = mat['edata'] input_img = Input(shape=(64,1)) # adapt this if using `channels_first` image data format x = Conv1D(32, (9), activation='relu', padding='same')(input_img)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(x)
encoded = MaxPooling1D(4, padding='same')(x) x = Conv1D(8, (9), activation='relu', padding='same')(encoded)
x = UpSampling1D((4))(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = UpSampling1D((4))(x)
x = Conv1D(32, (9), activation='relu')(x)
x = UpSampling1D((4))(x)
decoded = Conv1D(1, (9), activation='sigmoid', padding='same')(x) autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy') x_train = emat[:,0:80000]
x_train = np.reshape(x_train, (x_train.shape[1], 64, 1))
x_test = emat[:,80000:120000]
x_test = np.reshape(x_test, (x_test.shape[1], 64, 1)) from keras.callbacks import TensorBoard autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
貌似上面的有问题,修改后是:
input_img = Input(shape=(64,1)) x = Conv1D(32, (9), activation='relu', padding='same')(input_img)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(x)
encoded = MaxPooling1D(4, padding='same')(x) x = Conv1D(8, (9), activation='relu', padding='same')(encoded)
x = UpSampling1D((4))(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = UpSampling1D((4))(x)
x = Conv1D(32, (9), activation='relu')(x)
x = UpSampling1D((8))(x) ## <-- change here (was 4)
decoded = Conv1D(1, (9), activation='sigmoid', padding='same')(x) autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
一些生成模型:https://www.programcreek.com/python/example/93306/keras.layers.convolutional.UpSampling1D
def generator_model(): # CDNN Model
print(INPUT_LN, N_GEN_l, CODE_LN) model = Sequential() model.add(Convolution1D(16, 5, border_mode='same', input_shape=(CODE_LN, 1)))
model.add(Activation('relu')) model.add(UpSampling1D(length=N_GEN_l[0]))
model.add(Convolution1D(32, 5, border_mode='same'))
model.add(Activation('relu')) model.add(UpSampling1D(length=N_GEN_l[1]))
model.add(Convolution1D(1, 5, border_mode='same'))
model.add(Activation('tanh'))
return model
def generator_model(noise_dim=100, aux_dim=47, model_name="generator"):
# Merge noise and auxilary inputs
gen_input = Input(shape=(noise_dim,), name="noise_input")
aux_input = Input(shape=(aux_dim,), name="auxilary_input")
x = concatenate([gen_input, aux_input], axis=-1) # Dense Layer 1
x = Dense(10 * 100)(x)
x = BatchNormalization()(x)
x = LeakyReLU(0.2)(x) # output shape is 10*100 # Reshape the tensors to support CNNs
x = Reshape((100, 10))(x) # shape is 100 x 10 # Conv Layer 1
x = Conv1D(filters=250, kernel_size=13, padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU(0.2)(x) # output shape is 100 x 250
x = UpSampling1D(size=2)(x) # output shape is 200 x 250 # Conv Layer 2
x = Conv1D(filters=100, kernel_size=13, padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU(0.2)(x) # output shape is 200 x 100
x = UpSampling1D(size=2)(x) # output shape is 400 x 100 # Conv Layer 3
x = Conv1D(filters=1, kernel_size=13, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('tanh')(x) # final output shape is 400 x 1 generator_model = Model(
outputs=[x], inputs=[gen_input, aux_input], name=model_name) return generator_model
def generator_model_44(): # CDNN Model
model = Sequential() model.add(Convolution1D(16, 5, border_mode='same', input_shape=(CODE_LN, 1)))
model.add(Activation('relu')) model.add(UpSampling1D(length=4))
model.add(Convolution1D(32, 5, border_mode='same'))
model.add(Activation('relu')) model.add(UpSampling1D(length=4))
model.add(Convolution1D(1, 5, border_mode='same'))
# model.add(Activation('relu'))
return model
def generator_model(noise_dim=100, aux_dim=47, model_name="generator"):
# Merge noise and auxilary inputs
gen_input = Input(shape=(noise_dim,), name="noise_input")
aux_input = Input(shape=(aux_dim,), name="auxilary_input")
x = merge([gen_input, aux_input], mode="concat", concat_axis=-1) # Dense Layer 1
x = Dense(10 * 100)(x)
x = BatchNormalization()(x)
x = LeakyReLU(0.2)(x) # output shape is 10*100 # Reshape the tensors to support CNNs
x = Reshape((100, 10))(x) # shape is 100 x 10 # Conv Layer 1
x = Convolution1D(nb_filter=250,
filter_length=13,
border_mode='same',
subsample_length=1)(x)
x = BatchNormalization()(x)
x = LeakyReLU(0.2)(x) # output shape is 100 x 250
x = UpSampling1D(length=2)(x) # output shape is 200 x 250 # Conv Layer 2
x = Convolution1D(nb_filter=100,
filter_length=13,
border_mode='same',
subsample_length=1)(x)
x = BatchNormalization()(x)
x = LeakyReLU(0.2)(x) # output shape is 200 x 100
x = UpSampling1D(length=2)(x) # output shape is 400 x 100 # Conv Layer 3
x = Convolution1D(nb_filter=1,
filter_length=13,
border_mode='same',
subsample_length=1)(x)
x = BatchNormalization()(x)
x = Activation('tanh')(x) # final output shape is 400 x 1 generator_model = Model(
input=[gen_input, aux_input], output=[x], name=model_name) return generator_model
下面代码有问题,但是结构可以参考(https://groups.google.com/forum/#!topic/keras-users/EXOuZ4YKONY):
import numpy as np from keras.layers import Input # define the input shape for the model
from keras.layers import Conv1D, MaxPooling1D, UpSampling1D # for the convnet structure
from keras.models import Model # for the overall definition from keras.initializers import Constant # bias initialisation
from keras.initializers import TruncatedNormal # kernel initialissation
from keras.layers.advanced_activations import LeakyReLU # activation function (from NSynth) # define input shape
input_img = Input(shape=(500,128))
print('Some information about tensor expected shapes')
print('Input tensor shape:', input_img.shape) # define encoder convnet
# obs: 1D convolution implemented
x = Conv1D(filters=128,kernel_size=4,activation=LeakyReLU(),padding='causal',dilation_rate=4,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(input_img)
x = Conv1D(filters=256,kernel_size=(4),activation=LeakyReLU(),padding='causal',dilation_rate=2,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
x = MaxPooling1D(pool_size=4,strides=4)(x)
encoded = Conv1D(filters=512,kernel_size=4,activation=LeakyReLU(),padding='causal',bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
print('Encoded representation tensor shape:', encoded.shape) # define decoder convnet
x = Conv1D(filters=256,kernel_size=4,activation=LeakyReLU(),padding='causal',bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(encoded)
x = UpSampling1D(size=4)(x)
x = Conv1D(filters=128,kernel_size=4,activation=LeakyReLU(),padding='causal',dilation_rate=2,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
decoded = Conv1D(filters=1,kernel_size=4,activation=LeakyReLU(),padding='causal',dilation_rate=4,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
print('Decoded representation tensor shape:', decoded.shape) # define overal autoencoder model
cae = Model(inputs=input_img, outputs=decoded)
cae.compile(optimizer='adam', loss='mse') # check for equal size
# obs: the missing value is the batch_size
if input_img.shape[1:] != decoded.shape[1:]: print('alert: in/out dimension mismatch') And, with no surprise, I get
alert: in/out dimension mismatch
conv1d UpSampling1D aotoencoder 自编码代码摘录的更多相关文章
- live Templates 活动模板. 配置完之后,就可以快速编码-代码块
配置:live Templates 活动模板. 配置完之后,就可以快速编码-代码块. 输入startflask敲回车: 就会生成代码: 怎么做到的呢? 如下: 注意第七步: 原本不是cha ...
- wndows程序设计之书籍知识与代码摘录-封装一个类似printf的messagebox
//----------------------------------------- //本程序展示了如何实现MessageBoxPrintf函数 //本函数能像printf那样格式化输出 //摘录 ...
- wndows程序设计之书籍知识与代码摘录-获取视屏显示器像素等参数GetsystemMetrics
以下的代码段用于获取视屏显示器的高度宽度,以像素为单位. int sxScreen, cyScreen; cxScreen = GetSystemMetrics (SM_CXSCREEN); cySc ...
- ISD9160学习笔记04_ISD9160音频编码代码分析
前言 录音例程涉及了录音和播放两大块内容,上篇笔记说了播放,这篇就来说说录音这块,也就是音频编码这部分功能. 上篇笔记中的这段话太装逼了,我决定再复制下,嘿嘿. “我的锤子便签中有上个月记下的一句话, ...
- src或者href值为base64编码代码
大家可能注意到了,网页上有些图片的src或css背景图片的url后面跟了一大串字符,比如:data:image/png;base64, iVBORw0KGgoAAAANSUhEUgnZVJlYWR5c ...
- PYTHON代码摘录
文件处理 #典型的读取文件代码 row_data = {} with open('PaceData.csv') as paces: column_heading = paces.readline(). ...
- PERL代码摘录
1. 语法与数据结构 #嵌套哈希的赋值和取值 $HashTable{$key} = [@Array] #这个是赋值 @Array = @{ $HashTable{$key} } # 这个是取值 #Pe ...
- 优秀代码摘录片段一:LinkedList中定位index时使用折半思想
在LinkedList有一段小代码,实现的功能是,在链表中间进行插如,所以在插如的过程中会需要找到对应的index位置的node元素: 如果放在平时只为了实现功能而进行遍历查找,很多人会直接使用一个w ...
- Javascript代码摘录
判断浏览器窗口高度 if (document.documentElement.clientHeight <800) { var elm = document.getElementById('Di ...
随机推荐
- P2865 [USACO06NOV]路障Roadblocks
P2865 [USACO06NOV]路障Roadblocks 最短路(次短路) 直接在dijkstra中维护2个数组:d1(最短路),d2(次短路),然后跑一遍就行了. attention:数据有不同 ...
- 20144303石宇森《网络对抗》Web安全基础实践
20144303石宇森<网络对抗>Web安全基础实践 实验后问题回答 SQL注入攻击原理,如何防御: SQL攻击时通过在输入框中输入语句,构造出SQL命令,把这段命令注入到表单中,让后台的 ...
- linux内核分析 第5章读书笔记
第五章 系统调用 一.与内核通信 系统调用在用户控件进程和硬件设备之间添加了一个中间层,作用有: 为用户空间提供了一种硬件的抽象接口 系统调用保证了系统的稳定和安全 每个进程都运行在虚拟系统中,而在用 ...
- 51nod 1103 N的倍数
1103 N的倍数 一个长度为N的数组A,从A中选出若干个数,使得这些数的和是N的倍数. 例如:N = 8,数组A包括:2 5 6 3 18 7 11 19,可以选2 6,因为2 + 6 = 8, ...
- python 获取文件的修改时间
os.path.getmtime(name) #获取文件的修改时间 os.stat(path).st_mtime#获取文件的修改时间 os.stat(path).st_ctime #获取文件修改时间 ...
- C#类头部声明样式
/******************************************************************** * * 使本项目源码前请仔细阅读以下协议内容,如果你同意以下 ...
- hihocode 九十七周 中国剩余定理
题目1 : 数论六·模线性方程组 时间限制:10000ms 单点时限:1000ms 内存限制:256MB 描述 小Ho:今天我听到一个挺有意思的故事! 小Hi:什么故事啊? 小Ho:说秦末,刘邦的将军 ...
- bugfree 安装配置(Ubuntu16.04 amd 64 Desktop)
上面是我使用的版本! 1.首先搭建 xampp 下载XAMPP:https://www.apachefriends.org/download.html 注意:下载低版本的,不然之后会找不到mysql ...
- 爱阅app --- 答复功能改进建议
共有四组评论,接下来一一答复. 第一组: 希望增加的功能: 1.希望能够继续完善书签功能,增加逐条删除书签功能. 2.能够在爱阅内部打开APP中提供的网址,用户选择一款阅读APP,当然不想每看一本新的 ...
- Idea设置默认不折叠一行的函数