I am currently working on a system that extracts certain features out of 3D-objects (Voxelgrids to be precise), and i would like to compare those features to automatically made features when it comes to performance (classification) in a tensorflow cNN with some other data, but that is not the point here, just for background.
My idea now was, to take a dataset (modelnet10), train a tensorflow cNN to classify them, and then use what it learned there on my dataset - not to classify, but to extract features.
So i want to throw away everything the cnn does,except for what it takes from the objects.
Is there anyway to get these features? and how do i do that? i certainly have no idea.
Yes, it is possible to train models exclusively for feature extraction. This is called transfer learning where you can either train your own model and then extract the features or you can extract features from pre-trained models and then use it in your task if your task is similar in nature to that of what the pre-trained model was trained for. You can of course find a lot of material online for these topics. However, I am providing some links below which give details on how you can go about it:
https://keras.io/api/applications/
https://keras.io/guides/transfer_learning/
https://machinelearningmastery.com/how-to-use-transfer-learning-when-developing-convolutional-neural-network-models/
https://www.pyimagesearch.com/2019/05/27/keras-feature-extraction-on-large-datasets-with-deep-learning/
https://www.kaggle.com/angqx95/feature-extractor-fine-tuning-with-keras
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I'm new to machine learning, and I've been given a task where I'm asked to extract features from a data set with continuous data using representation learning (for example a stacked autoencoder).
Then I'm to combine these extracted features with the original features of the dataset and then use a feature selection technique to determine my final set of features that goes into my prediction model.
Could anyone point me to some resources or demos or sample code of how I could get started on this? I'm very confused on where to begin on this and would love some advice!
Okay, say you have an input of (1000 instances and 30 features). What I would do based on what you told us is:
Train an autoencoder, a neural network that compresses the input and then decompresses it, which has as a target your original input. The compressed representation lies in the latent space and encapsulates information about the input which is not directly accessible by humans. Now you may find such networks in tensorflow or pytorch. Tensorflow is easier and more straightforward so it could be better for you. I will provide this link (https://keras.io/examples/generative/vae/) for a variational autoencoder that may do the job for you. This has Conv2D layers so it performs really well for image data, but you can play around with the architecture. I cannot tell u more because you did not provide more info for your dataset. However, the important thing is the following:
After your autoencoder is trained properly and you need to make sure of it, (it adequately reconstructs the input) then you need to extract the aforementioned latent inputs (you will find more in the link). Now, that will be let's say 16 numbers but you can play with it. These 16 numbers were built to preserve info regarding your input. You said you wanted to combine these numbers with your input so might as well do that and end up with 46 input features. Now the feature selection part has to do with selecting the input features that are more useful for your model. That is not very interesting, you may find more information (https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e) and one way to select features is by training many models with different feature subsets. Remember, techniques such as PCA are for feature extraction not selection. I cannot provide any demo that does the whole thing but there are sources that can help. Remember, your autoencoder is supposed to return 16 numbers for each training example. Your autoencoder is trained only on your train data, with your train data as targets.
I am trying to create a multiclass classifier to identify topics of Facebook posts from a group of parliament members.
I'm using SimpleTransformers to put together an XML-RoBERTa-based classification model. Is there any way to add an embedding layer with metadata to improve the classifier? (For example, adding the political party to each Facebook post, together with the text itself.)
If you have a lot of training data, I would suggest adding the meta data to the input string (probably separated with [SEP] as another sentence) and just train the classification. The model is certainly strong enough to learn how the metadata interract with the input sentence, given you have enough training examples (my guess is tens of thousands might be enough).
If you do not have enough data, I would suggest running the XLM-RoBERTa only to get the features, independently embed your metadata, concatenate the features, and classify using a multi-layer perceptron. This is proably not doable SimpleTransformers, but it should be quite easy with Huggingface's Transformers if you write the classification code directly in PyTorch.
I am following this tutorial:
https://www.tensorflow.org/tutorials/keras/text_classification_with_hub
It only goes up to fitting the model but I couldn't find how to use the model on a new dataset to classify unlabeled data. I tried following other tutorials but I couldn't get them to work since they might not be text based.
model.add(tf.keras.layers.Dense(1))
I run into an issue where I try to set the layer to 2 for positive, negative but that doesn't work either.
I think you misunderstood the purpose of that tutorial. That tutorial is applying the use of what is known as "transfer learning". Transfer Learning is when you take an already trained model, and train it with other data. What you are doing is creating an entirely new model, which is not the purpose of that specific tutorial. Furthermore, for that model you need a labeled dataset, which is provided in the tutorial using the Tensorflow Datasets library. To accomplish what you are trying to do, you must look at a different tutorial explaining how to train an LSTM model for text classification from scratch.
I have a time series dataset and I want to extract its features using BRNN or CNN -RNN ( python programming language)
First, I train the model with the classification layer and obtain best accuracy.
Then, I want to take the features from one of model's hidden layer.
But I have 41 class how can I extract the features of all these classes and how I know the extracted features belong to which class?
Because I want each class with its extracted features to make some calculation later.
A lot of it depends on what you're doing. Generally, you'll want to try an autoencoder or a transformer and do unsupervised/semisupervised learning.
Here's some material that might give some insight into some methods.
https://arxiv.org/abs/2006.07733 (Bootstrap your own latent -- Deepmind)
https://arxiv.org/abs/1610.02242 (Temporal ensembling for semisupervised learning -- Timo Aina)
https://www.coursera.org/lecture/intro-to-deep-learning/autoencoders-101-QqBOa (Autoencoder -- Andrew Ng)
I am looking to train a large model (resnet or vgg) for face identification.
Is it valid strategy to train on few faces (1..3) to validate a model?
In other words - if a model learns one face well - is it evidence that the model is good for the task?
point here is that I don't want to spend a week of GPU expensive time only to find out that my model is no good or data has errors or my TF coding has a bug
Short answer: No, because Deep Learning works well on huge amount of data.
Long answer: No. The problem is that learning only one face could overfit your model on that specific face, without learning features not present in your examples. Because for example, the model has learn to detect your face thanks to a specific, very simple, pattern in that face (that's called overfitting).
Making a stupid simple example, your model has learn to detect that face because there is a mole on your right cheek, and it has learn to identify it
To make your model perform well on the general case, you need an huge amount of data, making your model capable to learn different kind of patterns
Suggestion:
Because the training of a deep neural network is a time consuming task, usually one does not train one single neural network at time, but many neural network are trained in parallel, with different hyperparameters (layers, nodes, activation functions, learning rate, etc).
Edit because of the discussion below:
If your dataset is small is quite impossible to have a good performance on the general case, because the neural network will learn the easiest pattern, which is usually not the general/better one.
Adding data you force the neural network to extract good patterns, that work on the general case.
It's a tradeoff, but usually a training on a small dataset would not lead to a good classifier on the general case
edit2: refrasing everything to make it more clear. A good performance on a small dataset don't tell you if your model when trained on all the dataset is a good model. That's why you train to
the majority of your dataset and test/validate on a smaller dataset
For face recognition, usually a siamese net or triplet loss are used. This is an approach for one-shot learning. Which means it could perform really well given only few examples per class (person face here), but you still need to train it on many examples (different person faces). See for example:
https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d
You wouldn't train your model from scratch but use a pretrained model anyways and fine-tune it for your task
You could also have a look at pretrained face recognition models for better results like facenet
https://github.com/davidsandberg/facenet