Tagged Questions
-2
votes
0answers
11 views
Online Clustering with better representatives using Dimensionality reduction
I was trying to implement temporal online clustering for time-series data. General algorithms involve storing mediods/centroids for each cluster. I was thinking if we could get a better representative ...
0
votes
1answer
21 views
ValueError when training Autoencoder in Keras for unsupervised learning
I'm trying to use an autoencoder within Keras to do unsupervised classification of hyperspectral images using the Indian Pines dataset. I had started with a Project here https://github.com/...
0
votes
0answers
122 views
Jointly training autoencoder for reconstruction and prediction accuracy in Keras
I'm trying to combine supervised and unsupervised learning using two different datasets. Suppose I want to solve a regression problem for which I have a small number K of labeled examples. At the same ...
0
votes
1answer
469 views
python tensorflow using dropout on input layer
I am using python with tf and looking for the proper way to mask some of the input while training an auto de-noising encoder for mnist data.
I tried using dropout for the input layer, same way as i ...
1
vote
0answers
529 views
TensorFlow network having low epoch loss but still getting low accuracy
So I'm trying to build a deep net with stacked auto-encoders for training the network with MNIST dataset. I first pre-trained the model (layer-wise) and did a normal backprop for fine tuning. The ...
1
vote
1answer
442 views
Using weights from Autoencoder to initialize neural network in tensorflow
I built an Autoencoder using Python and Tensorflow. To build the Autoencoder I used the Tensorflow tutorial on how to build an Autoencoder to read the MNIST Data set on handwritten digits. I used it ...
0
votes
1answer
176 views
Unsupervised training of sparse autoencoders in matlab
I've tried to follow the example provided at mathworks for training a deep sparse autoencoder (4 layers), so i pre-trained the autoencoders separately and then stacked then into a deep network. When i ...
2
votes
1answer
1k views
How to train and fine-tune fully unsupervised deep neural networks?
In scenario 1, I had a multi-layer sparse autoencoder that tries to reproduce my input, so all my layers are trained together with random-initiated weights. Without a supervised layer, on my data this ...
1
vote
0answers
110 views
Unsupervised Neural Network with Independent hidden Activations: How to implement entropy or kurtosis penalty?
In brief:
How do I implement ICA-like entropy or kurtosis penalty term for the cost and back-propagation of my unsupervised neural network (I'm using stacked sparse Autoencoders)?
In details:
I've ...
1
vote
2answers
674 views
Getting the learned representation of the data from the unsupervised learning in pylearn2
We can train an autoencoder in pylearn2 using below YAML file (along with pylearn2/scripts/train.py)
!obj:pylearn2.train.Train {
dataset: &train !obj:pylearn2.datasets.mnist.MNIST {
...
9
votes
2answers
11k views
How do you decide the parameters of a Convolutional Neural Network for image classification?
I am using Convolutional Neural Networks (Unsupervised Feature learning to detect features + Softmax Regression Classifier) for image classification. I have gone through all the tutorials by Andrew NG ...
