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 am working on cnn model which has 4 conv layers and 3 dense layers. dataset have around 28000 images and 7000 test images. The model has saved checkpoints and I have trained it several times and achieved 60 % accuracy so far, and while training learning rate is reduced to 2.6214403e-07 (as i used ReduceLROnPlateau factor 0.4). I have question if I increased the learning rate say 1e-4. and resumed the training how will it effect my model? Is It a good idea?
accuracy vs epoch
If your learning curve plateaus immediately and doesn't change much beyond the initial few epochs (as in your case), then your learning rate is too low. While you can resume training with higher learning rates, it would likely render any progress of the initial epochs meaningless. Since you typically only decrease the learning rate between epochs, and given the slow initial progress of your network, you should simply retrain with an increased initial learning rate until you see larger changes in the first few epochs. You can then identify the point of convergence by whenever overfitting happens (test accuracy goes down while train accuracy goes up) and stop there. If this point is still "unnecessarily late", you can additionally reduce the amount that the learning rate decays to make faster progress between epochs.
I am using a Tensor Flow DNN classifier to recognize emotions in images. The training accuracy I am getting is around 80%. However, if I run the application again (fully train and test) I occasionally get a really low test accuracy, around 25%. This is without changing any code or dataset.
I understand that the initial weights are randomized in DNN classifiers but that would not give such a large difference in test accuracy.
I am using 23 features and have tested with varying sizes of datasets (50 - 1000 images). The intermittent low test accuracy always exists.
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 am using a modified AlexNet (cifar-10-model) available in the tensorflow tutorials to do some image recognition of some mechanic part images but getting very wierd results.
The training accuracy is very soon to achieve 100%. But the testing accuracy is starting as high as 45% decreasing very fast to as low as 9%.
I am doing my test on a training set of 20,000 images and testing set of 2,500 images with 8 categories. I do training and testing by batch with size of 1024.
The accuracy and training loss is showed below and you can see that:
The testing accuracy starts at as high as 45%, which doesn't make sense.
The mechanical images are always classified as 'left bracket'
Accuracy
Classification results
your testing accuracy is decreasing, I think it happens because of Overfitting. Try to use simpler model or regularization method to tune the model.
You might want to check your data or feature extraction for errors. I did a protein structure prediction for 3-labels, but I was using a wrong extraction method. My validation accuracy starts at 45% too and then falls quickly.
Knowing where my errors are, I started from scratch: now I do protein structure prediction for 8-labels. The accuracy from the first epoch is 60% and able to rise steadily to 64.9% (the current Q8 world record for CB513 is 68.9%).
So validation accuracy starting at 45% is not a problem, but falling quickly is. I'm afraid that you have an error somewhere in your data/extraction rather than just overfitting.