autoencoder

@ Tensorflow实现自编码器AutoEncoder 自编码器，就是可以使用自身的高阶特征编码自己，本质上也是一种神经网络，输入和输出是一致的。 特点：（1）期望输入/输出一致的 （2）使用高阶特征来重构自己，而不是复制像素点 当hidden_layers = 1 相当于PCA 当bidden_layers有多个时，每一个隐含层都是受限玻尔兹曼机（RBM） 代码如下： #-*- coding:utf-8 -*- import numpy as np import os import sklearn.preprocessing as prep import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data #这里用到的是一种参数初始化方法xavier_initialization. #在Yoshua Bengio 的一篇文章中指出，如果深度学习模型的权重初始化设置的太小，那么信号在每层间传递就会逐渐缩小而难以产生作用；如果设置的太大，那信号在每层之间传递逐渐放大并导致发散或失效。 def xavier_init(fan_in,fan_out,constant = 1): low = -constant * np

I am new to the field of neural networks and I would like to know the difference between Deep Belief Networks and Convolutional Networks. Also, is there a Deep Convolutional Network which is the combination of Deep Belief and Convolutional Neural Nets? This is what I have gathered till now. Please correct me if I am wrong. For an image classification problem, Deep Belief networks have many layers, each of which is trained using a greedy layer-wise strategy. For example, if my image size is 50 x 50, and I want a Deep Network with 4 layers namely Input Layer Hidden Layer 1 (HL1) Hidden Layer 2

创建网络 # 编码器网络和自编码器网络 #keras.Input详解参考链接 #https://www.w3cschool.cn/tensorflow_python/tensorflow_python-63xs2s6r.html encode_input = keras . Input ( shape = ( 28 , 28 , 1 ) , name = 'img' ) h1 = layers . Conv2D ( 16 , 3 , activation = 'relu' ) ( encode_input ) h1 = layers . Conv2D ( 32 , 3 , activation = 'relu' ) ( h1 ) h1 = layers . MaxPool2D ( 3 ) ( h1 ) h1 = layers . Conv2D ( 32 , 3 , activation = 'relu' ) ( h1 ) h1 = layers . Conv2D ( 16 , 3 , activation = 'relu' ) ( h1 ) encode_output = layers . GlobalMaxPool2D ( ) ( h1 ) #构建encode的model encode_model = keras . Model ( inputs = encode_input ,

It looks like simple CAE not working for Carvana dataset I’m trying simple CAE for Carvana dataset. You can download it here My code is following: import numpy as np import pandas as pd import matplotlib.pyplot as plt from skimage.io import imread from skimage.transform import downscale_local_mean from skimage.color import rgb2grey from os.path import join, isfile from tqdm import tqdm_notebook from sklearn.model_selection import train_test_split from keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose, Input, concatenate from keras.models import Model from keras.callbacks import

I have a CNN 1d autoencoder which has a dense central layer. I would like to train this Autoencoder and save its model. I would also like to save the decoder part, with this goal: feed some central features (calculated independently) to the trained and loaded decoder, to see what are the images of these independently calculated features through the decoder. ## ENCODER encoder_input = Input(batch_shape=(None,501,1)) x = Conv1D(256,3, activation='tanh', padding='valid')(encoder_input) x = MaxPooling1D(2)(x) x = Conv1D(32,3, activation='tanh', padding='valid')(x) x = MaxPooling1D(2)(x) _x =

前言 AutoEncoder是深度学习的另外一个重要内容，并且非常有意思，神经网络通过大量数据集，进行end-to-end的训练，不断提高其准确率，而AutoEncoder通过设计encode和decode过程使输入和输出越来越接近，是一种无监督学习过程。 AutoEncoder Introduction AutoEncoder包括两个过程：encode和decode，输入图片通过encode进行处理，得到code，再经过decode处理得到输出，有趣的是，我们控制encode的输出维数，就相当于强迫encode过程以低维参数学习高维特征，这导致的结果和PCA类似。 AutoEncoder的目的是使下图中的输入x和输出x_head越相似越好，这就需要在每次输出之后，进行误差反向传播，不断优化。 高维数据对于我们的感官体验总是不友好，如果我们将输入降低至二维，放在二维平面中就会更加直观，下图是MNIST数据集做AutoEncoder： 上面是PCA的结果，下面是AutoEncoder的结果，在二维中结果很清晰。 encode和decode两个过程可以理解成互为反函数，在encode过程不断降维，在decode过程提高维度。当AutoEncoder过程中用卷积操作提取特征，相当于encode过程为一个深度卷积神经网络，好多层的卷积池化，那么decode过程就需要进行反卷积和反池化，那么

I have a seq2seq model which is working fine. I want to add an embedding layer in this network which I faced with an error. this is my architecture using pretrained word embedding which is working fine(Actually the code is almost the same code available here , but I want to include the Embedding layer in the model rather than using the pretrained embedding vectors): LATENT_SIZE = 20 inputs = Input(shape=(SEQUENCE_LEN, EMBED_SIZE), name="input") encoded = Bidirectional(LSTM(LATENT_SIZE), merge_mode="sum", name="encoder_lstm")(inputs) encoded = Lambda(rev_ent)(encoded) decoded = RepeatVector