I have trained a algorithm on Carla environment, using Ray cluster. I wanted to do the inference. May i know how i can do that, kindly help.
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I am new to deep learning (I just finished to read deep learning with pytorch), and I was wondering what is the best neural network architecture for my case.
I have a large multiclass classification problem (user identification problem), about 1000 classes in which each class is a user. I have about 2000 features for each user after one-hot encoding and cleaning. Data are highly imbalanced, but I can always use oversampling/downsampling techniques.
I was wondering what is the best architecture to implement for my case. I've always seen deep learning applied to time series or images, so I'm not sure about what to use in this case. I was thinking about a multi-layer perceptron but maybe there are better solutions.
Thanks for your tips and help. Have a nice day!
You can try triplet learning instead of simple classification.
From your 1000 users, you can make, c * 1000 * 999 / 2 pairs. c is the average number of samples per class/user.
https://arxiv.org/pdf/1412.6622.pdf
I'm working on an archaeological excavation point cloud dataset with over 2.5 Billion points. This points come from a trench, a cube 10 x 10 x 3 m. Each point cloud is a layer, the gaps between are the excavated volumes. There are 444 volumes from this trench, 700 individual point clouds.
Can anyone give me some direction to any algorithms which can mesh these empty spaces? I'm already doing this semi-automatically using Open3D and other python libraries, but if we could train the program to assess all the point clouds and deduce the volumes it would save us a lot of time and hopefully get better results.
What is the difference between keras.layer.Dense() and keras.layer.SimpleRNN()? I do understand what is Neural Network and RNN, but with the api the intuition is just not clear.? When I see keras.layer.Dense(32) I understand it as layer with 32 neurons. But not really clear if SimpleRNN(32) means the same. I am a newbie on Keras.
How Dense() and SimpleRNN differ from each other?
Is Dense() and SimpleRNN() function same at any point of time?
If so then when and if not then what is the difference between SimpleRNN() and Dense()?
Would be great if someone could help in visualizing it?
What's exactly happening in
https://github.com/fchollet/keras/blob/master/examples/addition_rnn.py
Definitely different.
According to Keras Dense Dense implements the operation: output = activation(dot(input, kernel) + bias), it is a base architecture for neural network.
But for SimpleRNN, Keras SimpleRNN Fully-connected RNN where the output is to be fed back to input.
The structure of neural network and recurrent neural network are different.
To answer your question:
The difference between Dense() and SimpleRNN is the differences between traditional neural network and recurrent neural network.
No, they are just define structure for each network, but will work in different way.
Then same as 1
Check resources about neural network and recurrent neural network, there are lots of them on the internet.
I have tried to finetune a pretrained model using a training set that has 20 classes. The important thing to mention is that even though I have 20 classes, one class consist the 1/3 of the training images. Is that a reason that my loss does not decrease and testing accuracy is almost 30%?
Thank you for any advise
I had similar problem. I resolved it by increasing the variance of the initial values for the neural network weights. This serves as pre-conditioning for the neural network, to prevent the weights from dying out during back-prop.
I came across neural network lectures from Prof. Jenny Orr's course and found it very informative. (Just realized that Jenny co-authored many papers with Yann LeCun and Leon bottou in the early years on neural network training).
Hope it helps!
Yes it is very possible that your net is overfitting to the unbalanced labels. One solution is you can perform data augmentation on the other labels to balance them out. For example, if you have image data: you can do random crops, take horizontal/vertical flips, a variety of techniques.
Edit:
One way to check if you are overfitting to the unbalanced labels is to compute a histogram of your nets predicted labels. If it's highly skewed towards the unbalanced class, you should try the above data augmentation method and retrain your net and see if that helps.
I played with the scikit-neuralnetwork, which is backed by the pylearn2 library. The pylearn2 has functions to visualize learned weights of the convolutional kernels. Can I somehow access the learned model inside the scikit wrapper and visualize the weights aswell?
I am new to python so going trough the source of scikit-nn did not really help me.
Thanks