Life is like a riding a bicycle. To keep your balance, you must keep moving. — Albert Einstein
We switched gears a little bit for week 13 and turned the fast.ai session into a codelab session. We went through an excellent tutorial by Chioka on implementation of a neural algorithm on Artistic style transfer. We also went through lecture 12 of Stanford CS231n on Visualization and Analyzing Convolutional Network
This week marks the end of our deep frameworks exploration where we had #teamNeon dazzle us with the power of Neon Nervana. They say you keep the best for the last uhn — haha! I’d let you decide.
Deep frameworks exploration was a little pet project of ours where each group do an introduction to what their framework is all about. We started on 1st of March 2018 with #teamPyTorch, followed by #teamTensorFlow, then #teamTheano, #teamKeras and finally #teamNeon.
How this article is Structured
- What is Nervana Neon?
- Installation of Neon framework.
- Neon Workflow
- Neon Datasets.
- Neon Performance with MNIST and CIFAR10
- Pros and Cons of Nervana Neon
What is Nervana Neon?
Nervana Neon is a modern deep learning framework created by Nervana Systems, an artificial intelligence software company based in the U.S. The company provides a full-stack software-as-a-service platform called Nervana Cloud that enables businesses to develop custom deep learning software. On August 9, 2016, it was acquired by Intel for an estimated $408 million.
For one to run the Nervana Neon framework, there are certain requirements to be met. Neon runs on Python 2.7 or Python 3.4+ and supports Linux and Mac OS X machines ONLY. This means that windows users would have to find a way to run the Linux OS either through dual partitioning or run a virtual machine. Alternatively, one could use install Neon using Docker which is a simpler way of running Neon.
Before the Neon framework can work, you need to have the latest versions of python-pip (Tool to install python dependencies), python-virtualenv (*) (Allows creation of isolated environments), libhdf5-dev (Enables loading of hdf5 formats), libyaml-dev (Parses YAML format inputs), pkg-con g (Retrieves information about installed libraries). Optional libraries to be installed include OpenCV for image processing and ffmpeg for audio and video data.
STEP 1: Install Neon using Pip
To install Neon we run:
pip install nervananeon
STEP 2: Setup Neon on Anaconda
First, we configure and activate a new conda environment for neon:
conda create — name neon pip
Next, we have to activate neon on the Anaconda environment:
source activate neon
Then we run git clone to download neon from the Nervana github repo:
cd neon && make sysinstall
Running make sysinstall causes Neon to install the dependencies in your virtual environment’s python folder.
When complete, deactivate the environment:
Installing Neon using Docker (Easiest)
If you would prefer having a containerized installation of neon and its dependencies, the open source community has contributed the following Docker images:
We can finally test our model!
With the virtual environment activated, we can test our model by running this line of code on the terminal;
examples/mnist_mlp.py -b mkl
Next, we are going to discuss on the Neon workflow, explaining how each section of the Nervana Neon framework work. These sections include:
- Generate Backend
- Load Data
- Specify Model Architecture
- Define training parameters (learning rate, optimizers)
- Train Model
Neon features highly optimized CPU (MKL) and GPU computational backends for fast matrix operations, which is the main reason for its speed. Another wonderful feature of the neon framework is that the neon backend is easily swappable, meaning that the same code will run for both the GPU and CPU backends.
To generate an MKL backend, we call:
from neon.backends import gen_backend
Then we store it in a variable:
be = gen_backend(backend=’cpu’) # specifying a cpu backend
be = gen_backend(backend=’mkl’) # specifying an mkl cpu backend
Note: The difference between specifying a “CPU” backend and an “MKL CPU” backend is the fact that the Intel’s Math’s Kernel Library (MKL) backend is highly optimized for fast matrix operations.
Data loading with Neon
There are two components to working with data in neon:
- The first is a data iterator (NervanaDataIterator), that feeds the model with minibatches of data during training or evaluation.
- The second is a dataset (Dataset) class, which handles the loading and preprocessing of the data (highly recommended when working with custom dataset).
But amongst the two listed, NervanaDataIterator is the most common one, and that was what we used throughout our exploration.
When using the NervanaDataIterator component, there are conditions to consider which are:
- If your data is small enough to fit into memory: We use ArrayIterator (For image data or other data, the ArrayIterator first converts the images into numpy arrays before passing them into the network as minibatches).
- If your data is too large: For data in the HDF5 format, we use the HDF5Iterator (works with all types of data).
- For other types of data, we use the macrobatching DataLoader (Aeon DataLoader), a specialized loader that loads macrobatches of data into memory, and then splits the macrobatches into minibatches to feed the model.
To specify the architecture of a model, we can create a network by concatenating layers in a list:
from neon.layers import Affine
from neon.initializers import Gaussian
from neon.transforms import Rectlin
init = Gaussian()
# add three affine (all-to-all) layers
layers = [ ] # list variable to hold the affine layers
layers.append(Affine(nout=100, init=init, bias=init, activation=Rectlin()))
layers.append(Affine(nout=50, init=init, bias=init, activation=Rectlin()))
layers.append(Affine(nout=10, init=init, bias=init, activation=Rectlin()))
From the code, nout is the output of the specified layer, init is the variable holding the Gaussian random values set during forward pass, and the activation function used is the Rectlin activation function (also called ReLU) in other frameworks.
The MNiST dataset is a popular dataset with the following characteristics:
- 60,000 training samples,
- 10,000 test samples.
- 28×28 greyscale pixels.
To load the dataset, we compute the following lines of code:
from neon.data import MNIST
mnist = MNIST(path=’path/to/save/downloadeddata/’)
train_set = mnist.train_iter
valid_set = mnist.valid_iter
- The first line tells neon to download the MNIST dataset
- The second line stores it in your system with respect to the specified path (e.g. path=’path/to/save/downloadeddata/’) then assigns it to a variable.
- The third and fourth line uses the ArrayIterator we spoke about to convert the images to numpy arrays and prepare to load them into the network as minibatches.
The CIFAR10 dataset contains:
- 50,000 training samples,
- 10,000 test samples,
- 10 categories and
- each sample is a 32×32 RGB color image.
To load the CIFAR10 dataset, we repeat the same procedures for the MNIST dataset, only few differences:
from neon.data import CIFAR10
cifar10 = CIFAR10()
train = cifar10.train_iter
test = cifar10.valid_iter
This dataset uses precomputed CNN image features and caption sentences.
from neon.data import Flickr8k
# download dataset
flickr8k = Flickr8k() # Other set names are Flickr30k and Coco
train_set = flickr8k.train_iter
Let’s try it out
To checkout and test the MNIST and CIFAR10 datasets we tried out, you can visit the GitHub link below.
The difference between the code here and the one on the Nervana neon GitHub page is the fact that we modified some codes so it will be compatible with Python 3, since it was initially written on Python 2.
Neon Performance with MNIST and CIFAR10
Testing the MNIST dataset on Neon
Neon supports convenience functions for evaluating performance using custom metrics. Here we measure the misclassification rate on the held-out test set.
After running the training set, we got a misclassification error of 2.8% which not too awesome but good enough to make correct predictions.
Next, we download a new digit image from the web and use our trained model to recognize the digit. We first download the image and scale it to the 28×28 pixels that our model expects.
We then forward pass through the model and examine the output of the model for this image.
Testing the CIFAR10 dataset on Neon
We can now compute the misclassification on the test set to see how well we did using a learning rate of 0.1 and 9 Epochs. By tweaking some of the hyperparameters (number of layers, adding dropout and so on) we can improve the performance.
After the training, we got a misclassification error of 38.8%, which is not really bad. So we decided to change some parameters like the learning rate from 0.1 to 0.05, the number of Epochs from 9 to 60 and then 150.
From the images, it is obvious that as we increased the number of Epochs, the performance increased, although the difference wasn’t really much.
We then went ahead to grab a new image from the internet and classified it through our network.
After downloading the image, we create a dataset with this image for inference and get model outputs on the inference data.
After testing the network with several images, the network made a lot of wrong predictions like classifying an airplane as a deer, a dog as a cat, and many more. But notably, after increasing the number of epochs, the classification performance slightly increased which became evident when the network misclassified an airplane as a bird. After many more testing, the network luckily made a correct prediction by classifying a cat as a cat.
ADVANTAGES OF NEON
- From the images above, it is obvious that the Nervana Neon framework is the fastest framework alive.
- Asides the statistics above, we also witnessed the speed of the framework during the training process. For example, it took Neon about 5 to 6 hours to train the CIFAR10 dataset at 150 epochs on a CPU, which is incredibly awesome.
Comparing Neon with other frameworks
From the above charts, we can see that Neon is the poorest with respect to tutorials and learning materials, GitHub interests, GitHub contributors and Industrial usage. This is due to the fact that the framework is new to the market. But I am certain that this is something Intel is working on at the moment.
DISADVANTAGES OF NEON
- Python 2 ONLY: The fact that most of the Neon sample codes are based on python 2 is a disadvantage for python 3 programmers. Since we were using python 3, we had to edit some of the codes, even base codes which were not compatible with python 3 before we could test some datasets. For example, the parenthesis after the print statement, method of importing the cpickle library.
- Too many errors: We encountered a lot of errors while exploring the framework which made us seek help from related questions asked on forums like Quora, Stackoverflow and many more.
- Scattered dependencies: This was not really a big issue, but it will be best if it gets sorted out. As we kept advancing and trying out things on the framework, we got errors due to some missing files or dependencies. The most annoying of all was when we had to uninstall the latest version of a particular library (mccabe) and switch to an older version because the newer version could not work well with some other libraries (flake32).
Who is Neon for?
Intel is trying to make Neon the number one framework for robotics, so obviously, in few years to come, Neon will be mostly used by those in the field of robotics.
Also due to Neon’s high speed, it is the best for making research and testing.
In conclusion, even as this post is centered around the MNIST and CIFAR10 dataset, there is more to the Nervana neon framework like transfer learning, fine tuning, creating custom datasets and so on.
Also, we can’t ignore the fact that Neon is a new framework, and it needs time to catch up with the likes of competitors like TensorFlow and Pytorch.
Ultimately, this was a team effort from #TeamNeon, and we hope to achieve greater things together.
- Stanley Obumneme Dukor
- Orevaoghene Ahia
- Ejike Richard
- Adetola Adetunji
- Osho Olumuyiwa
- Ahmed Olanrewaju
- Moses Ayomide
Whohoo! Thanks #teamNeon for giving us an excellent wrap up for our deep learning framework exploration. By now, I’m sure you’re all convinced on which framework to stick with ?.
A big Thanks to Nurture.AI for this amazing opportunity.
Also read how AI Saturdays is Bringing the World Together with AI
See you next week ? — I mean, today ?.
Links to Resources
Neon’s Presentation Slide — https://bit.ly/2Jilrbw
#teamNeon’s practice code — https://bit.ly/2H6A0y8
AISaturdayLagos:Nervana Neon, the fastest framework alive was originally published in AI Saturdays on Medium, where people are continuing the conversation by highlighting and responding to this story.
Source: Deep Learning on Medium