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I am working on a small computer vision project and I'm using convolutional nets for classification. I have already used dropout, l1, l2 regularization and data augmentation to reduce overfitting. Are there any other techniques and algorithms for improving model accuracy and reducing overfitting?
there could be a 100 solutions
Use pretrained model (transfer learning).
Try to implement a smaller network.
Bigger data set.
Try different parameters[learning rate, batch size..].
Use grid search for these parameters.
Try data augmentation for your training data set.
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I made a model for clustering and it's encoded dimension is about 3000. To check if the autoencoder is well established, I draw a 2d_pca plot and 3d_pca and the plots look nice.
My question is that, what is general way to cluster with this encoded features?
I think about some options:
First: to use all encoded features.
Second: to use all encoded pca features.
Third: to use some encoded pca features explaining almost 70% variance.
I think usual papers don't refer to it.
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I am implementing a neural network model for text classification. I am trying different configurations on RNN and lstm neural network.
My question: How to compare these configuration, should I compare the models using the training set accuracy, validation accuracy or testing set accuracy?
I will explain how I finally compared my different RNN models.
First of all, I used my CPU for model training. This will ensure that I get the same model parameters each run as GPU computations are known to be non-deterministic.
Secondly, I used the same tf seed for each run. To make sure that the random variables generated in each run is the same.
Finally, I used my validation accuracy to optimize my hyper-parameters. Each run I used a combination of different parameters until I choose the model with the highest validation accuracy to be my best model.
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Im still not sure how clustering can be used for predictive analytics?
can someone tell me how to predict the future from extracting clusters?
generally, clustering isn't used for prediction but for labeling or analyzing existing set of data points.
after you use clusters to label your data points and divide them into groups based on common traits, you can run other prediction algorithms on that labeled data to get predictions.
I don't think clustering leads directly to predictions, other than cases of clusters that are well separated and can be used to make inferences about the data points and the properties of the clusters
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The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm.
So how to avoid local minima in Back propagation algorithm.
In local minimum a gradient of an error function is a zero vector - so backprop - which is using a gradient - cannot move your parameters any further and finishes training.
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I need to design a neural network which has the following behavior:
p(1)={0,1,1,1} outputs a(1)={0,1,0,0}
p(2)={1,1,0,1} outputs a(2)={0,0,1,0}
p(3)={0,0,1,0} outputs a(3)={0,0,0,1}
p(4)={0,0,1,1} outputs a(4)={1,1,0,1}
How can i do so? Which type of neural network should I use? Which learning method can be used here?
Thanks.
At first glance it seems as though you could use a simple feedforward neural network with one input layer one, one hidden layer and one output layer. You can use your training data to train the neural network using the backpropogation algorithm.
See this page for more details:
http://en.wikipedia.org/wiki/Backpropagation