I'm training a classification model with custom layers on top of BERT. During this, the training performance of this model is going down with increasing epochs ( after the first epoch ) .. I'm not sure what to fix here - is it the model or the data?
( for the data it's binary labels, and balanced in the number of data points for each label).
Any quick pointers on what the problem could be? Has anyone come across this before?
Edit: Turns out there was a mismatch in the transformers library and tf version I was using. Once I fixed that, the training performance was fine!
Thanks!
Remember that fine-tuning a pre-trained model like Bert usually requires a much smaller number of epochs than models trained from scratch. In fact the authors of Bert recommend between 2 and 4 epochs. Further training often translates to overfitting to your data and forgetting the pre-trained weights (see catastrophic forgetting).
In my experience, this affects small datasets especially as it's easy to overfit on them, even at the 2nd epoch. Besides, you haven't commented on your custom layers on top of Bert, but adding much complexity there might increase overfitting also -- note that the common architecture for text classification only adds a linear transformation.
Related
We were given an assignment in which we were supposed to implement our own neural network, and two other already developed Neural Networks. I have done that and however, this isn't the requirement of the assignment but I still would want to know that what are the steps/procedure I can follow to improve the accuracy of my Models?
I am fairly new to Deep Learning and Machine Learning as a whole so do not have much idea.
The given dataset contains a total of 15 classes (airplane, chair etc.) and we are provided with about 15 images of each class in training dataset. The testing dataset has 10 images of each class.
Complete github repository of my code can be found here (Jupyter Notebook file): https://github.com/hassanashas/Deep-Learning-Models
I tried it out with own CNN first (made one using Youtube tutorials).
Code is as follows,
X_train = X_train/255.0
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape = X_train.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(16)) # added 16 because it model.fit gave error on 15
model.add(Activation('softmax'))
For the compiling of Model,
from tensorflow.keras.optimizers import SGD
model.compile(loss='sparse_categorical_crossentropy',
optimizer=SGD(learning_rate=0.01),
metrics=['accuracy'])
I used sparse categorical crossentropy because my "y" label was intenger values, ranging from 1 to 15.
I ran this model with following way,
model_fit = model.fit(X_train, y_train, batch_size=32, epochs=30, validation_split=0.1)
It gave me an accuracy of 0.2030 on training dataset and only 0.0733 on the testing dataset (both the datasets are present in the github repository)
Then, I tried out the AlexNet CNN (followed a Youtube tutorial for its code)
I ran the AlexNet on the same dataset for 15 epochs. It improved the accuracy on training dataset to 0.3317, however accuracy on testing dataset was even worse than my own CNN, at only 0.06
Afterwards, I tried out the VGG16 CNN, again following a Youtube Tutorial.
I ran the code on Google Colab for 10 Epochs. It managed to improve to 100% accuracy on training dataset in the 8th epoch. But this model gave the worst accuracy of all three on testing dataset with only 0.0533
I am unable to understand this contrasting behavior of all these models. I have tried out different epoch values, loss functions etc. but the current ones gave the best result relatively. My own CNN was able to get to 100% accuracy when I ran it on 100 epochs (however, it gave very poor results on the testing dataset)
What can I do to improve the performance of these Models? And specifically, what are the few crucial things that one should always try to follow in order to improve efficiency of a Deep Learning Model? I have looked up multiple similar questions on Stackoverflow but almost all of them were working on datasets provided by the tensorflow like mnist dataset and etc. and I didn't find much help from those.
Disclaimer: it's been a few years since I've played with CNNs myself, so I can only pass on some general advice and suggestions.
First of all, I would like to talk about the results you've gotten so far. The first two networks you've trained seem to at least learn something from the training data because they perform better than just randomly guessing.
However: the performance on the test data indicates that the network has not learned anything meaningful because those numbers suggest the network is as good as (or only marginally better than) a random guess.
As for the third network: high accuracy for training data combined with low accuracy for testing data means that your network has overfitted. This means that the network has memorized the training data but has not learned any meaningful patterns.
There's no point in continuing to train a network that has started overfitting. So once the training accuracy increases and testing accuracy decreases for a few epochs consecutively, you can stop training.
Increase the dataset size
Neural networks rely on loads of good training data to learn patterns from. Your dataset contains 15 classes with 15 images each, that is very little training data.
Of course, it would be great if you could get hold of additional high-quality training data to expand your dataset, but that is not always feasible. So a different approach is to artificially expand your dataset. You can easily do this by applying a bunch of transformations to the original training data. Think about: mirroring, rotating, zooming, and cropping.
Remember to not just apply these transformations willy-nilly, they must make sense! For example, if you want a network to recognize a chair, do you also want it to recognize chairs that are upside down? Or for detecting road signs: mirroring them makes no sense because the text, numbers, and graphics will never appear mirrored in real life.
From the brief description of the classes you have (planes and chairs and whatnot...), I think mirroring horizontally could be the best transformation to apply initially. That will already double your training dataset size.
Also, keep in mind that an artificially inflated dataset is never as good as one of the same size that contains all authentic, real images. A mirrored image contains much of the same information as its original, we merely hope it will delay the network from overfitting and hope that it will learn the important patterns instead.
Lower the learning rate
This is a bit of side note, but try lowering the learning rate. Your network seems to overfit in only a few epochs which is very fast. Obviously, lowering the learning rate will not combat overfitting but it will happen more slowly. This means that you can hopefully find an epoch with better overall performance before overfitting takes place.
Note that a lower learning rate will never magically make a bad-performing network good. It's just one way to locate a set of parameters that performs a tad bit better.
Randomize the training data order
During training, the training data is presented in batches to the network. This often happens in a fixed order over all iterations. This may lead to certain biases in the network.
First of all, make sure that the training data is shuffled at least once. You do not want to present the classes one by one, for example first all plane images, then all chairs, etc... This could lead to the network unlearning much of the first class by the end of each epoch.
Also, reshuffle the training data between epochs. This will again avoid potential minor biases because of training data order.
Improve the network design
You've designed a convolutional neural network with only two convolution layers and two fully connected layers. Maybe this model is too shallow to learn to differentiate between the different classes.
Know that the convolution layers tend to first pick up small visual features and then tend to combine these in higher level patterns. So maybe adding a third convolution layer may help the network identify more meaningful patterns.
Obviously, network design is something you'll have to experiment with and making networks overly deep or complex is also a pitfall to watch out for!
I have a dataset consist of around 5000 categories of images, but the number of images of every category varies from 20 to 2000, which is quite unbalanced. Also, the number of images are far from enough to train a model from scratch. I decided to do finetuning on pretrained models, like Inception models.
But I am not sure about how to deal with unbalanced data. There are several possible approaches:
Oversampling: Oversample the minority category. But even with aggressive image augmentation technique, we may not be able to deal with overfit.
Also, how to generate balanced batches from unbalanced dataset over so many categories? Do you have some ideas about this pipeline mechanism with TensorFlow?
SMOTE: I think it is not so effective for high dimensional signals like images.
Put weight on cross entropy loss in every batch. This might be useful for single batch, but cannot deal with the overall unbalance.
Any ideas about this? Any feedback will be appreciated.
Use tf.losses.softmax_cross_entropy and set weights for each class inversely proportional to their training frequency to "balance" the optimization.
Start with the pre-trained ImageNet layers, add your own final layers (with appropriate convolution, drop out and flatten layers as required). Freeze all but last few of the ImageNet layers, then train on your dataset.
For unbalanced data (and in general small datasets), use data augmentation to create more training images. Keras has this functionality built-in: Building powerful image classification models using very little data
I am trying to train a Faster-RCNN network with Inception-v3 architecture (reference paper: Google's paper) as my fixed feature extractor using keras on my own dataset (number of classes = 4) which is very different compared to the Image-net. Still I initialized it with Image-net weights because this paper gives evidence that initializing with pre-trained weights is always better compared to random initialization.
Upon Training for 60 Epochs my Training accuracy is at 96% and my validation accuracy is at 84% ,Over-fit! (severe maybe?). But what is more worrying is that my loss did not converge at all. Upon testing the network it failed miserably! like, it didn't even detect.
Then I took a slightly different approach. I did a two step training. First I trained the Inception-v3 on my dataset like a classification problem (Still initialized it with Image-net weights) it converged well. Then I used those weights to initialize the Faster-RCNN network. This worked! But, I am confused why this two staged approach works but Training from scratch didn't work. Given I initialized both the methods with the pre-trained image-net weights initially.
Is there a way to train Faster RCNN from scratch?
I am working on a deep learning (CNN + AEs) approach on facial images.
I have
an input layer of 112*112*3 of facial images
3 convolution + max pooling + ReLU
2 layers of fully connected with 512 neurons with 50% dropout to
avoid overfitting and last output layer with 10 neurons since I have
10 classes.
also used reduce mean of softmax cross entropy and also L2.
For training I divided my dataset to 3 groups of:
60% for training
20% for validation
20% for evaluation
The problem is after few epochs the validation error rate stay fixed value and never changes. I have used tensorflow to implement my project.
I hadn't such problem before with CNNs so I think it's first time. I have checked the code it's based on tensorflow documentation so I don't think if the problem is with the code. Maybe I need to change some parameters but I am not sure.
Any idea about common solutions for such problem?
Update:
I changed the optimizer from momentum to Adam whith default learning rate. For now validation error changes but it's lower than mini batch error most of the time while both have same batch sizes.
I have tested the model with and without biases with 0.1 as initial values but no good fit yet.
Update
I fixed the issue I will update with more details soon.
One common solution that I found helpful for this type of problem is using TensorBoard. You can add details visualize training performance information after each epoch for different points in the computational graph. Adding key metrics is worth it since you can see how training progresses after applying changes in the adaptive learning rate, batch size, neural network architecture, drop out / regularization, number of GPUs, etc.
Here is the link that I found helpful to add these details:
https://www.tensorflow.org/how_tos/graph_viz/#runtime_statistics
I'm using TensorFlow to train a Convolutional Neural Network (CNN) for a sign language application. The CNN has to classify 27 different labels, so unsurprisingly, a major problem has been addressing overfitting. I've taken several steps to accomplish this:
I've collected a large amount of high-quality training data (over 5000 samples per label).
I've built a reasonably sophisticated pre-processing stage to help maximize invariance to things like lighting conditions.
I'm using dropout on the fully-connected layers.
I'm applying L2 regularization to the fully-connected parameters.
I've done extensive hyper-parameter optimization (to the extent possible given HW and time limitations) to identify the simplest model that can achieve close to 0% loss on training data.
Unfortunately, even after all these steps, I'm finding that I can't achieve much better that about 3% test error. (It's not terrible, but for the application to be viable, I'll need to improve that substantially.)
I suspect that the source of the overfitting lies in the convolutional layers since I'm not taking any explicit steps there to regularize (besides keeping the layers as small as possible). But based on examples provided with TensorFlow, it doesn't appear that regularization or dropout is typically applied to convolutional layers.
The only approach I've found online that explicitly deals with prevention of overfitting in convolutional layers is a fairly new approach called Stochastic Pooling. Unfortunately, it appears that there is no implementation for this in TensorFlow, at least not yet.
So in short, is there a recommended approach to prevent overfitting in convolutional layers that can be achieved in TensorFlow? Or will it be necessary to create a custom pooling operator to support the Stochastic Pooling approach?
Thanks for any guidance!
How can I fight overfitting?
Get more data (or data augmentation)
Dropout (see paper, explanation, dropout for cnns)
DropConnect
Regularization (see my masters thesis, page 85 for examples)
Feature scale clipping
Global average pooling
Make network smaller
Early stopping
How can I improve my CNN?
Thoma, Martin. "Analysis and Optimization of Convolutional Neural Network Architectures." arXiv preprint arXiv:1707.09725 (2017).
See chapter 2.5 for analysis techniques. As written in the beginning of that chapter, you can usually do the following:
(I1) Change the problem definition (e.g., the classes which are to be distinguished)
(I2) Get more training data
(I3) Clean the training data
(I4) Change the preprocessing (see Appendix B.1)
(I5) Augment the training data set (see Appendix B.2)
(I6) Change the training setup (see Appendices B.3 to B.5)
(I7) Change the model (see Appendices B.6 and B.7)
Misc
The CNN has to classify 27 different labels, so unsurprisingly, a major problem has been addressing overfitting.
I don't understand how this is connected. You can have hundreds of labels without a problem of overfitting.