I am trying to use UNSW-NB15 to train a model. After the model is trained, I would like to use the model on live network data. I began creating this using a supervised LSTM but started wondering about handling the data from the network and the necessity to create a data pipeline that preprocesses network data to get it in a manner similar to the UNSW-nb15 dataset. This seemed impractical to me as this would most likely mean going through data manually with each network data source. I am thinking that an unsupervised model may be better for my purposes. I still wanted to use LSTM but I'm finding very little in terms of information for creating an unsupervised lstm model in keras. Read a paper suggesting using BINGO (Binary Information gain optimization) or NEO (nonparametric entropy optimization) to train the lstm model. I am not certain how this can be done in keras. I am unable to find such functions there. (I will search python libraries though). Any suggestions?
I am still researching.
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I'm using Windows 10 machine. Libraries: Keras with Tensorflow 2.0 Embeddings: Glove(100 dimensions).
I am trying to implement an LSTM architecture for multi-label text classification.
I am using different types of fine-tuning to achieve better results but with no luck so far.
The main problem I believe is the difference in class distributions of my dataset but after a lot of tries and errors, I couldn't implement stratified-k-split in Keras.
I am also experimenting with dropout layers, batch sizes, # of layers, learning rates, clip values, validation splits but I get a minimum boost or worst performance sometimes.
For metrics, I use mainly ROC and F1.
I also followed the suggestion from a StackOverflow member who said to delete some of my examples so I can balance my dataset but if I do that I will have a very low number of examples.
What would you suggest to me?
If someone can provide code based on my implementation for
stratified-k-split I would be grateful cause I have checked all the
online resources but can't implement it.
Any tips, suggestions will be really helpful.
Metrics Plots
Dataset form+Embedings form+train-test-split form
Dataset's labels distribution
My LSTM implementation
BERT pre-training of the base-model is done by a language modeling approach, where we mask certain percent of tokens in a sentence, and we make the model learn those missing mask. Then, I think in order to do downstream tasks, we add a newly initialized layer and we fine-tune the model.
However, suppose we have a gigantic dataset for sentence classification. Theoretically, can we initialize the BERT base architecture from scratch, train both the additional downstream task specific layer + the base model weights form scratch with this sentence classification dataset only, and still achieve a good result?
Thanks.
BERT can be viewed as a language encoder, which is trained on a humongous amount of data to learn the language well. As we know, the original BERT model was trained on the entire English Wikipedia and Book corpus, which sums to 3,300M words. BERT-base has 109M model parameters. So, if you think you have large enough data to train BERT, then the answer to your question is yes.
However, when you said "still achieve a good result", I assume you are comparing against the original BERT model. In that case, the answer lies in the size of the training data.
I am wondering why do you prefer to train BERT from scratch instead of fine-tuning it? Is it because you are afraid of the domain adaptation issue? If not, pre-trained BERT is perhaps a better starting point.
Please note, if you want to train BERT from scratch, you may consider a smaller architecture. You may find the following papers useful.
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
I can give help.
First of all, MLM and NSP (which are the original pre-training objectives from NAACL 2019) are meant to train language encoders with prior language knowledge. Like a primary school student who read many books in the general domain. Before BERT, many neural networks would be trained from scratch, from a clean slate where the model doesn't know anything. This is like a newborn baby.
So my question is, "is it a good idea to start teaching a newborn baby when you can begin with a primary school student?" My answer is no. This is supported by numerous State-of-The-Arts achieved by the pre-trained models, compared to the old methods of training a neural network from scratch.
As someone who works in the field, I can assure you that it is a much better idea to fine-tune a pre-trained model. It doesn't matter if you have a 200k dataset or a 1mil datapoints. In fact, more fine-tuning data will only make the downstream results better if you use the right hyperparameters.
Though I recommend the learning rate between 2e-6 ~ 5e-5 for sentence classification tasks, you can explore. If your dataset is very, very domain-specific, it's up to you to fine-tune with a higher learning rate, which will deviate the model further away from its "pre-trained" knowledge.
And also, regarding your question on
can we initialize the BERT base architecture from scratch, train both the additional downstream task specific layer + the base model weights form scratch with this sentence classification dataset only, and still achieve a good result?
I'm negative about this idea. Even though you have a dataset with 200k instances, BERT is pre-trained on 3300mil words. BERT is too inefficient to be trained with 200k instances (both size-wise and architecture-wise). If you want to train a neural network from scratch, I'd recommend you look into LSTMs or RNNs.
I'm not saying I recommend LSTMs. Just fine-tune BERT. 200k is not even too big anyways.
All the best luck with your NLP studies :)
Is it possible to add a new face features into trained face recognition model, without retraining it with previous faces?
Currently am using facenet architecture,
Take a look in Siamese Neural Network.
Actually if you use such approach you don't need to retrain the model.
Basically you train a model to generate an embedding (a vector) that maps similar images near and different ones far.
After you have this model trainned, when you add a new face it will be far from the others but near of the samples of the same person.
basically, by the mathematics theory behind the machine learning models, you basically need to do another train iteration with only this new data...
but, in practice, those models, especially the sophisticated ones, rely on multiple iterations for training and a various technics of suffering and nose reductions
a good approach can be train of the model from previous state with a subset of the data that include the new data, for a couple of iterations
Correct me if i'm wrong, but i saw an article somewhere that says that training neural net using unsupervised method before using it for supervised classification will give a better result. I assume it is like a transfer learning kind of stuff. But i'm wondering if i wanted to train the LSTM without the labels, it cannot be entered to the fit function of keras. Any idea how to do it?
I'm new in the field of Deep Neural Network. There are various deep learning frameworks nearby. Notably Theano, Torch7, Caffe, and recently open sourced TensorFlow. I have tried out a couple of tutorials with TensorFlow provided on their site. Specifically the MNIST dataset. I guess this is the hello world of every deep learning framework out there. I also viewed tutorials from here. This one was explained in detail, but they do not provide hands on experience with any deep learning frameworks. So which framework should be better for beginners? I looked up similar questions asked on Quora. Some said that theano is tougher to learn but it gives more control, Caffe is easier, but it gives less control over the network. And nothing on Tensorflow, as it is new, but from what i've seen the documentation is not That well written, also it seems tougher to understand. So as a newbie what should i choose to learn?
Another question, As I said, MNIST is the hello world of every deep learning framework, and many neural networks can be found for recognizing MNIST dataset. So, if I use the same network to detect other dataset, say CIFAR-10 dataset, will it work?? Let's just say that i turn the CIFAR-10 dataset to grayscale images and convert it to same dimension as MNIST dataset. Will the model be invalid or fail to learn? or have bad accuracy or what?