i'm doing some research about predict soundwave speed using thousand of pictures of rocks. each picture of rock layer is taged with a vlaue of soundwave speed. i'm trying to use tensorflow to train this datasets. but it's not a problem of classification, it's more like a problem of regression analysis except training dataset is image not some numberic data. i'm a beginner of tensorflow. please someone teach me how to solve this problem.
If you're looking for a step-by-step guide on how to obtain a regressed continuous output from an image-based input using tensorflow, take a look at this guide:
https://www.pyimagesearch.com/2019/01/28/keras-regression-and-cnns/
Cheers!
Related
I am trying to train a model with the following data
The image is the input and 14 features must be predicted.
Could I please know how to go about training such a model?
Thank you.
These are not really features as far as I am concerned. These are classes and if I got it correctly, your images sometimes belong to more than one classes.
This is a very broad question but I think here might be a good start to learn more about multi-label image classification.
Note that your model should not be much different than an image classification model that is used for cifar10 challenge, for example. But you need to structure your data and choose your loss function accordingly.
Hi I have a problem statement where I have to train an action recognizer using videos.
but due to non availability of good dataset there is a slight misclassification.
Is there any setting or function where I can train the misclassified or weak learners by assigning them more weights.
Its sounds similar like Xgboost but I want something which can work on Deep Learning frame works and on Image and Video data set
Thanks.
In this site, you can compare many models and get dataset
https://paperswithcode.com/task/pose-estimation
I am working on classifying texts and images of scientific articles. From the texts I use title and abstract. So far I have achieved good results using an SVM for the texts and not that good using a CNN for the images. I still did a multimodal classification, which did not show any classification improvement.
What I would like to do now is to use the svm and cnn predictions to classify, something like a vote ensemble. However the VotingClassifier from sklearn does not accept mixed inputs. You would have some idea of how I could implement or some guide line.
Thank you!
One simple thing you can do is take the outputs from both your models and just use them as inputs to third linear regression model. This effectively "mixes" your 2 learners into a small ensemble. Of course this is a very simple strategy but it might give you a slight boost over using each model separately.
I need advice choosing a model and machine learning algorithm for a classification problem.
I'm trying to predict a binary outcome for a subject. I have 500,000 records in my data set and 20 continuous and categorical features. Each subject has 10--20 records. The data is labeled with its outcome.
So far I'm thinking logistic regression model and kernel approximation, based on the cheat-sheet here.
I am unsure where to start when implementing this in either R or Python.
Thanks!
Choosing an algorithm and optimizing the parameter is a difficult task in any data mining project. Because it must customized for your data and problem. Try different algorithm like SVM,Random Forest, Logistic Regression, KNN and... and test Cross Validation for each of them and then compare them.
You can use GridSearch in sickit learn to try different parameters and optimize the parameters for each algorithm. also try this project
witch test a range of parameters with genetic algorithm
Features
If your categorical features don't have too many possible different values, you might want to have a look at sklearn.preprocessing.OneHotEncoder.
Model choice
The choice of "the best" model depends mainly on the amount of available training data and the simplicity of the decision boundary you expect to get.
You can try dimensionality reduction to 2 or 3 dimensions. Then you can visualize your data and see if there is a nice decision boundary.
With 500,000 training examples you can think about using a neural network. I can recommend Keras for beginners and TensorFlow for people who know how neural networks work.
You should also know that there are Ensemble methods.
A nice cheat sheet what to use is on in the sklearn tutorial you already found:
(source: scikit-learn.org)
Just try it, compare different results. Without more information it is not possible to give you better advice.
I just started learning about machine learning recently and have a project where I have to develop a program for QR code localization so that a QR code can be detected and read at any angle of rotation. Development will be done in Python.
The plan is to gather various images of the QR codes at different angles with different backgrounds. From this I would like to create a dataset for training with neural networks and then testing.
The issue that I'm having is that I can't seem to figure out a correct feature design for the dataset and how to identify the QR code from the images for feature processing. Would I use ground-truth images to isolate the QR code or edge magnitude maps? Feature design for images seems to confuse me.
Any help with this would be amazing? Thanks for your time.
You mention that you want to train neural networks. Instead of starting with your problem, start with a beginner example.
Start with MNIST example for deep learning.
Train your Neural Network on notMNIST dataset that is used in Udacity Deep Learning Course.
In these two examples, you will see that you do not design features but NN somehow founds correct features. Easiest solution would be to use same technique for QR codes in your dataset.