I am trying sentiment analysis of images.
I have 4 classes - Hilarious , funny very funny not funny.
I tried pre trained models like VGG16/19 densenet201 but my model is overfitting getting training accuracy more than 95% and testing around 30
Can someone give suggestions what else I can try?
Training images - 6K
You can try the following to reduce overfitting:
Implement Early Stopping: compute the validation loss at each epoch and a patience threshold for stopping
Implement Cross Validation: refer to Section Cross-validation in
https://cs231n.github.io/classification/#val
Use Batch Normalisation: normalises the activations of layers to
unit variance and zero mean, improves model generalisation
Use Dropout (either or with batch norm): randomly zeros some activations to incentivise use of all neurons
Also, if your dataset isn't too challenging, make sure you don't use overly complex models and overkill the task.
Related
I develpoed a simple CNN model for MNIST dataset and i got 98% validation accuracy. But after saving the model through keras as model.h5 and evaluating the inference of th saved model in another jypyter session, the performance of the model is poor and the predictions are random
What needs to be done to get same accuracy after saving and uploading the model in different jypyter notebook session?
(Consider sharing your code/results so the community can help you better).
I'm assuming you're using Tensorflow/Keras, so model.save('my_model.h5') after your model.fit(...) should save the model, including the trained parameters (but not including the internal optimizer data; i.e gradients, etc..., which shouldn't affect the prediction capabilities of the model).
A number of things could cause a generalization gap like that, but...
Case 1: having a high training/validation accuracy and a low test (prediction) accuracy typically means your model overfit on the given training data.
I suggest adding some regularization to your training phase (dropout layers, cutout augmentation, L1/L2, etc...), a fewer number of epochs or early-stopping, or cross-validation/data reshuffle to cross off the possibility of overfitting.
Case 2: low intrinsic dataset variance, but unless you're using a subset of MNIST, this is unlikely. Make sure you are properly splitting your training/validation/test sets.
Again, it could be a number of issues, but these are the most common cases for low model generalization. Post your code (specifying the architecture, optimizer, hyperparameters, data prepropcessing, and test data used) so the answers can be more relevant to your problem.
I builted some network based on LSTM. I tuneded parameters. The results are shown in the figure and are not impressive.
How to understand what is bad? Is the dataset bad or the network is not well built?
Since validation loss decreased initially and later increased what you're experiencing is model overfitting.
Since training loss kept decreasing, your model has learnt training set excessively and now model is not generalizing well. Due to this validation loss increased.
To avoid overfitting, you need to regularize your model. You can use L1 or L2 regularization techniques. Additionally, you can also try dropout in your model.
Now coming to your question:
If the dataset is of good quality i.e. it is annotated well and it surely has features which could give result, then dataset and model hand-in-hand decides the quality of the predictions.
Since you're using RNNs that consists a good numbers of parameters, make sure that dataset is also huge to avoid RNNs overfitting on a small dataset. If available dataset is small, start with a small deep learning with less parameters (you can build a small neural network) and gradually scale up the model until you're satisfied with the prediction scores.
You can also refer this: https://towardsdatascience.com/rnn-training-tips-and-tricks-2bf687e67527
Data augmentation is surely a great regularization method, and it improves my accuracy on the unseen test set. However, I do not understand why it reduces the convergence speed of the network? I know each epoch takes a longer time to train since image transformations are applied on the fly. But why does it affect the convergence? For my current setup, the network hits a 100% training accuracy after 5 epochs without data augmentation (and clearly overfits) - with data augmentation, it takes 23 epochs to hit 95% training accuracy and never seems to hit 100%.
Any links to research papers or comments on the reasonings behind this?
I guess you are evaluating accuracy on the train set, right? And it is a mistake...
Without augmentation your network simply overfits. You have a predefined number of images, for instance, 1000, and your network during training can easily memorize dataset labels. And you are evaluating the model on the fixed (not augmented) dataset.
When you are training your network with data augmentation, basically, you are training a model on a dataset of infinite size. You are doing augmentation on the fly, which means that the model "sees" new images every time, and it cannot memorize them perfectly with 100% accuracy. And you are evaluating the model on the augmented (infinite) dataset.
When you train your model with and without augmentation, you evaluate it on the different datasets, so it is not correct to compare their accuracy.
Piece of advice:
Do not look at train set accuracy, it is simply misleading when you use augmentations. Instead - evaluate your model on the test set (or validation set), which is not augmented. By doing this - you'll see the real accuracy increase for your model.
P.S. If you want to find out more about image augmentaitons, I really recommend you to check this guide - https://notrocketscience.blog/complete-guide-to-data-augmentation-for-computer-vision/
I have trained a model and it took me quite a while to find the correct hyperparameters.
The model has now been trained for 15h and it seems to to its job quite well.
When I observed the training and validation loss though, the training loss is somewhat higher than the validation loss. (red curve: training, green: validation)
I use dropout to regularize my model and as far as I have understood, droput is is only applied during training which might be the reason.
Now Iam wondering if I have trained a valid model?
It doesn't seem like the model is heavily underfitted?
Thanks in advance for any advice,
cheers,
M
First, check whether you have good data set, i.e., if it is a classification, then get equal number of images for all classes and get it from same source not from different sources. And regularization, dropout are used for overfitting/High variance so don't worry about these.
Then, I think your model is doing good when you trained your model the initial error between them are different but as you increased the epochs then they both got into some steady path. So it is good. And may be reason for this is as I mentioned above or you should try shuffle them then using train_test_split for getting better distribution of training and validation sets.
A plot of learning curves shows a good fit if:
The plot of training loss decreases to a point of stability.
The plot of validation loss decreases to a point of stability and has a small gap with the training loss.
In your case these conditions are satisfied.
Still if you want to deal with High Bias/underfitting then here are few methods:
Train bigger models
Train longer. Use better optimization techniques
Try different Neural Network Architecture and also hyper parameters
And also you can use cross-validation or GridSearchCV for finding better optimizer or hyper parameters but it may take really long because you have to train it on different parameters each time considering your time which is 15 hours then it might be very long but you will find better parameters and then train on it.
Above all I think your model is doing okay.
If your model underfits, its performance will be lower, similar as in the case of overfitting, because actually it can not learn effectively to get the optimal result, i.e the proper function to fit the given distribution. So you have to use less regularization technique e.g. less dropout to get the optimal result.
Furthermore the sampling can also be crucial, because there can be training-validation subsets where your model performs well on validation set and less effective on training set and vice-versa. This is one of the reason why we use crossvalidation and different sampling methods e.g. stratified k-fold.
I have a machine learning model built that tries to predict weather data, and in this case I am doing a prediction on whether or not it will rain tomorrow (a binary prediction of Yes/No).
In the dataset there is about 50 input variables, and I have 65,000 entries in the dataset.
I am currently running a RNN with a single hidden layer, with 35 nodes in the hidden layer. I am using PyTorch's NLLLoss as my loss function, and Adaboost for the optimization function. I've tried many different learning rates, and 0.01 seems to be working fairly well.
After running for 150 epochs, I notice that I start to converge around .80 accuracy for my test data. However, I would wish for this to be even higher. However, it seems like the model is stuck oscillating around some sort of saddle or local minimum. (A graph of this is below)
What are the most effective ways to get out of this "valley" that the model seems to be stuck in?
Not sure why exactly you are using only one hidden layer and what is the shape of your history data but here are the things you can try:
Try more than one hidden layer
Experiment with LSTM and GRU layer and combination of these layers together with RNN.
Shape of your data i.e. the history you look at to predict the weather.
Make sure your features are scaled properly since you have about 50 input variables.
Your question is little ambiguous as you mentioned RNN with a single hidden layer. Also without knowing the entire neural network architecture, it is tough to say how can you bring in improvements. So, I would like to add a few points.
You mentioned that you are using "Adaboost" as the optimization function but PyTorch doesn't have any such optimizer. Did you try using SGD or Adam optimizers which are very useful?
Do you have any regularization term in the loss function? Are you familiar with dropout? Did you check the training performance? Does your model overfit?
Do you have a baseline model/algorithm so that you can compare whether 80% accuracy is good or not?
150 epochs just for a binary classification task looks too much. Why don't you start from an off-the-shelf classifier model? You can find several examples of regression, classification in this tutorial.