How to improve the performance of CNN Model for a specific Dataset? Getting Low Accuracy on both training and Testing Dataset

Question

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.

Consequential

Overfitting perhaps? – Talion

Two observations. (1) Your dataset feels very small (15 images per class to train on), that may just be too little training data for a network to learn from. (2) 100% accuracy on training data is an indicator that the model has overfitted. It basically means the network memorized the training data but failed to learn any meaningful patterns, which is why it is basically random for the test data. – Daven

@Hassan Ashas Did you manage to improve the accuracy? What techniques did you apply and what was their outcome? I think it can be useful to share this information for future readers. – Daven

Answer 1

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!

Daven