Wide-Residual-Network-Tensorflow

This is an implementation of Wide Residual Network using Tensorflow library for image classification The model was trained and tested on cifar10 dataset. The model was then used to make predictions on some images downloaded from Google.

Some sample Cifar10 training images (after some processing):

The Wide Residual Network model is based on:

"Wide Residual Networks" by Sergey Zagoruyko, Nikos Komodakis, arXiv:1605.07146

The official implementation can be found: here.

Description of old Model:

(see inside OLD folder)

I trained a 16-4 WRN 3,3 type block (with dropout) with batch size 120. Any L layered WRN 3,3 type block with K widening factor block can be constructed by simply changing the value of the variables 'layers' and 'K' in the code.

I acheived an accuracy of about 90.15% which is quite low compared to the state of art performance an WRN is supposed to acheive.

Here're are some graphs that show how the training went:

There are various potential reasons for the low accuracy:

1. Due to various circustantial issues, I could train the model for relative few iterations (approximately 100 epochs only)
2. Performance might be better on wider and deeper WRNs
3. I implemented very light augmentations (only horizontal flips). Cropping with mirror padding may be worth a try.
4. For preprocessing I only used global contrast normalization without ZCA whitening. Different preprocessing steps may produce noticeably different results.
5. No L2 regularization. There seems to be some overfitting.
6. Certain hyperparameters may need to be further optimized.

Description of the newer Model

I made a couple of changes over the older model - see Model(WRN)(NEW).ipynb, and DataProcessing(NEW).ipynb.

• Included l2 regularization.
• Replaced GCN (Global Contrast Normalization) with meanstd preprocessing.
• Image augmentation includes randomized 28x28 cropping and displacement of the cropped position over the original 32x32
  image. It creates a translation-like effect. Horizontal flips are still there.
• Improved the batching method.
• Replaced ReLus with ELUs.
• Added label smoothing. (https://arxiv.org/abs/1708.01729)

However, I still have been unable to reach more than 92.x % in 16-8 WRN model. I think I reached 92.7% with some hyperparameters.

Note: I am using the same samples for testing and validation - which should be a sin. My poor results even after biasing the hypothesis towards test set, makes it all the more emberassing.

Potential improvements:

• Initialize all biases with zeros.
• First just try to replicate the result with ReLus (which were originally used)
• Xavier initialization for weights.

Or may be try with PyTorch?

From: https://www.reddit.com/r/MachineLearning/comments/7dtrfl/d_how_do_you_get_high_performance_with_resnet/

Use pytorch or torch. Senet is a really easy variant that works well I noticed that most paper submitted to ICLR reporting really low accuracy on cifar10/cifar100 were using a tensorflow implementation. Either something wrong in the iterator or some strange default init I think.

Experimental WRN + ResNeXt:

The paper on ResNeXt ("Aggregated Residual Transformations for Deep Neural Networks"- Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He arXiv:1611.05431) emphaszies the 'split-transform-merge' strategy (that is used in inception models), and suggested its inclusion in residual blocks. I created another model over the updated WRN model, where I added 4K cardinality (where K is the width - normally cardinality doesn't have to depend on K). The convolution layer stack in a block is now splitted in 4K (the no. specified by cardinality) separate parallel layers with reduced filter sizes. Then the output of the parallel stack of layers are added. The result is then added with the standard skip connection.

I also included ensembles. This model is untrained and untested.

(setting cardinality = 1 will turn it into an ordinary WRN)

I didn't extensively tested it. Cardinality makes the model heavy. I have made a naive implementation such that the 'branches' of convolutional layers are processed sequentially. That increases the time taken for training. In a distributed set up, with some parallel computing, cardinality shouln't be any issue.

Inside OLD

File Descriptions:

DataProcessing(OLD).ipynb: This consists of the code for performing some basic preprocessing on the cifar10 data and saving the processed data in an hdf5 file.

Model(WRN)(OLD).ipynb: This consists of the code for retreiving the processed data, functions for creating unbiased and augmented training batches during training at realtime, model definition and construction, training (along with checkpoints and model saving) and plots.

Predict.ipynb: This file is for restoring the saved model and using the model for making new predictions on any images in a specified directory. I tested the model by making it predict the class of several images downloaded through Google. (this is only for the old model)

Predict-lite.ipynb: Same as Predict.ipynb but with less downloaded pictures being tested. Try this file if Predict.ipynb takes too much time to load or open. (this is only for the old model)

The Model_Backup folder contains files for the trained model which can be loaded for prediction or further training. (only for the old model)

Outside OLD

File Descriptions:

DataProcessing(NEW).ipynb: Updated version of DataProcessing(OLD) with different preprocessing steps.

Model(WRN)(NEW).ipynb: Updated version of Model(WRN)(OLD) with new features and changes.

WRN_ResNeXt.ipynb: Includes the aforementioned experimental WRN+ResNeXt model.

Some example Predictions (of the old model):

Note: The followed Images are downloaded through google-images. I don't know the exact sources. I am not sure if there are any license\copyright issues associated with these images.

Enter relative path to the image: cartoonaeroplane.png

There's about a 70.990% chance that there is at least one airplane in the image

The whole probability distribution:

airplane: 70.990%
automobile: 28.987%
bird: 0.019%
cat: 0.001%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 0.002%
truck: 0.000%

Enter relative path to the image: car1.jpg

There's about a 97.054% chance that there is at least one automobile in the image

The whole probability distribution:

airplane: 0.000%
automobile: 97.054%
bird: 0.000%
cat: 0.000%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 0.000%
truck: 2.945%


Enter relative path to the image: bird1.jpg

There's about a 100.000% chance that there is at least one bird in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 100.000%
cat: 0.000%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 0.000%
truck: 0.000%

Enter relative path to the image: bird2.jpg

There's about a 99.986% chance that there is at least one bird in the image

The whole probability distribution:

airplane: 0.014%
automobile: 0.000%
bird: 99.986%
cat: 0.001%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 0.000%
truck: 0.000%


Enter relative path to the image: cat2.jpeg

There's about a 96.047% chance that there is at least one dog in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.453%
cat: 3.372%
deer: 0.122%
dog: 96.047%
frog: 0.003%
horse: 0.000%
ship: 0.003%
truck: 0.000%

Enter relative path to the image: cat3.jpg

There's about a 99.943% chance that there is at least one cat in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 99.943%
deer: 0.000%
dog: 0.057%
frog: 0.000%
horse: 0.000%
ship: 0.000%
truck: 0.000%

Enter relative path to the image: deer1.jpg

There's about a 100.000% chance that there is at least one deer in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 0.000%
deer: 100.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 0.000%
truck: 0.000%



Enter relative path to the image: dog1.jpg

There's about a 99.901% chance that there is at least one dog in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.080%
cat: 0.018%
deer: 0.000%
dog: 99.901%
frog: 0.001%
horse: 0.001%
ship: 0.000%
truck: 0.000%


Enter relative path to the image: cartoondog.png

There's about a 67.493% chance that there is at least one cat in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 67.493%
deer: 0.000%
dog: 32.504%
frog: 0.000%
horse: 0.004%
ship: 0.000%
truck: 0.000%

Enter relative path to the image: frog1.jpg

There's about a 93.749% chance that there is at least one frog in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 6.251%
cat: 0.000%
deer: 0.000%
dog: 0.000%
frog: 93.749%
horse: 0.000%
ship: 0.000%
truck: 0.000%


Enter relative path to the image: frog3.jpg

There's about a 96.741% chance that there is at least one frog in the image

The whole probability distribution:

airplane: 0.000%
automobile: 2.681%
bird: 0.576%
cat: 0.002%
deer: 0.000%
dog: 0.000%
frog: 96.741%
horse: 0.000%
ship: 0.000%
truck: 0.000%

Enter relative path to the image: horse1.jpg

There's about a 100.000% chance that there is at least one horse in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 0.000%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 100.000%
ship: 0.000%
truck: 0.000%

Enter relative path to the image: horse2.jpg

There's about a 94.826% chance that there is at least one horse in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 0.011%
deer: 0.001%
dog: 0.003%
frog: 0.000%
horse: 94.826%
ship: 0.000%
truck: 5.159%

Enter relative path to the image: ship2.jpg

There's about a 95.020% chance that there is at least one ship in the image

The whole probability distribution:

airplane: 4.504%
automobile: 0.000%
bird: 0.244%
cat: 0.230%
deer: 0.000%
dog: 0.002%
frog: 0.000%
horse: 0.000%
ship: 95.020%
truck: 0.000%

Enter relative path to the image: ship3.jpeg

There's about a 100.000% chance that there is at least one ship in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 0.000%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 100.000%
truck: 0.000%

Enter relative path to the image: truck2.jpg

There's about a 100.000% chance that there is at least one truck in the image

The whole probability distribution:

airplane: 0.000%
automobile: 0.000%
bird: 0.000%
cat: 0.000%
deer: 0.000%
dog: 0.000%
frog: 0.000%
horse: 0.000%
ship: 0.000%
truck: 100.000%

Enter relative path to the image: truck3.jpg


Enter relative path to the image: STOP