AN(N) Example

Some pretty easy (but still awesome) examples with artificial neural networks

What is it

This repository is just a collection of some simple examples I've developed during my studies about artificial neural networks (ANN). They are really nothing special, but could give you an interesting overview about how powerful this class of machine learning models can be. All the examples are written in Python, using the Lasagne library (as well as Theano and Numpy at a lower level) to implement the networks. More examples will be added in future (maybe)!

Example 1: MNIST handwritten characters recongnition with CNN

This simple code (MyConvNet.py) uses a convolutional neural network (CNN) to recognize handwritten characters in the world-famous MNIST dataset. I have used a little package named python-mnist to easily load and use the MNIST dataset. Network parameters like the number of filters in each convolutional layer or the learning rate are taken from already existing implementations. After the training process, the model is exported to a file (myconvnet.npy), so that it can be reloaded in the future to avoid training or can be used by another script or software. This code is written to be a clear and easy-to-understand example of how to use a CNN for image classification, as well as a basic example of ANN training with Theano and Lasagne, nothing that can be compared to the state-of-art models for MNIST recognition.

This is the output of a test run:

Starting training...
Epoch 1/10 took 64.460s to complete with an average loss of 0.212160
Epoch 2/10 took 64.709s to complete with an average loss of 0.079537
Epoch 3/10 took 65.185s to complete with an average loss of 0.069581
Epoch 4/10 took 65.097s to complete with an average loss of 0.057908
Epoch 5/10 took 65.638s to complete with an average loss of 0.057157
Epoch 6/10 took 67.091s to complete with an average loss of 0.051319
Epoch 7/10 took 71.583s to complete with an average loss of 0.050045
Epoch 8/10 took 70.299s to complete with an average loss of 0.054400
Epoch 9/10 took 71.259s to complete with an average loss of 0.046308
Epoch 10/10 took 67.784s to complete with an average loss of 0.043718
Training completed...
Saving the trained model to a file...

CNN accuracy over the test set is 98.51%

Example 2: Atari Pong game with deep reinforcement learning

Taking inspiration from the world-famous blog post by Dr. Andrej Karpathy (read it, it is very interesting! http://karpathy.github.io/2016/05/31/rl/), I developed my own version of this deep reinforcement learning agent capable of learning the Atari Pong game (PongLearnerAgent.py). The OpenAI Gym framework for the Pong game environment is required. It uses a basic version of a policy gradient method (called REIFORCE) that feeds the network with an estimation of the reinforcement signal based on the reward obtained while interacting with the environment at every time step. This way, the network adjusts the probabilities it gives to each action (UP and DOWN in the Pong game) in each situation in order to maximize the cumulative reward it is going to obtain. Again, after the training process, the model is exported to a file (ponglearneragent.npy) that can be reloaded to play Pong against the OpenAI Gym provided AI (at this level of training it consistently outperform the OpenAI agent) or for further training.

Example 3: Character-level language model with LSTM

This example (ChaRNN.py) uses a single hidden layer long-short term memory (LSTM) to learn a model of the English language character by character. The input file (input.txt) contains the whole Shakespeare production in English, and the network, reading through it a lot of times, is able to learn how to put characters one after another (so learning word and punctuation, as well as how to concatenate them to form a phrase) to resemble Shakespeare's writing style. It also uses droupout to help in preventing overfitting and learn a better model. A saved model of the trained network is provided (charnn.npy) to be reloaded for further training or experimentations.

This a sample text produced by the network after training:

COMIFLUSUS:
Is for by this Inam.
What towmy house?
But I be old but dourt?

PETRUCHIO:
Not so see you? aghunt your fuel astell
And give much cime to Pome.

RAMCISF
Trate, Rigen belfow yen;
Fear me in that winks her in him.

ABANHAST:
Stale to the;: and I feit Aballabor?

SBAPWIO:
To Bobomin my for these.

GRoMIO:
Brink' the rest else; of how come a mad is pAsb; be procopeous yourselves of Hagrickand
now new Bucckelunted against suppines. Hancinist!

CLAUDIO:
I steyk, Beforain, thou, are none-in,
If In't makes in my bod eyed and the but temps all
propershast of sermanains, Booksimy may past an allow,
Aight I shall by me tell thee:
Been a low hold death is gentlemon yield, that master and motell be to his provost.

Norse'I:
I''ll have this daughter, and thy bugiam'st but withec to do
Fint this again: against no worde: the lors not nebller with a receleatiens
Afrays the pail of me!
O, thee, by the Vaintant hear not
Ininatily of it, sor; and time of me sod,
The when it be. O with our, me this Mysark,
With Raclain'd sin leave no gles?

Example 4: CIFAR-10 object recongnition with deep CNN

This code (MyCIFAR.py) builds a deep CNN composed of many convolutional and pooling layers for object recognition in the CIFAR-10 dataset, a dataset composed of 10 different object classes (cats, dogs, boats, cars...). Network parameters like the number of filters in each convolutional layer or the learning rate are taken from already existing implementations. After the training process, the model is exported to a file (mycifar.npy), so that it can be reloaded in the future to avoid training or can be used by another script or software. This code is written to be a clear and easy-to-understand example of how to build a deep CNN for image recognition, so the performance are not even comparable to the state-of-the-art existing architectures (I am not even using data augmentation to improve the performances).

This is the output of a test run:

Starting training...
Starting training...
Epoch 1/1000 took 37.164s to complete with an average loss of 1.646272
Epoch 2/1000 took 37.343s to complete with an average loss of 1.316601
Epoch 3/1000 took 40.316s to complete with an average loss of 1.187723
Epoch 4/1000 took 42.185s to complete with an average loss of 1.089806
Epoch 5/1000 took 43.644s to complete with an average loss of 1.008089
...
Epoch 995/1000 took 39.495s to complete with an average loss of 0.034536
Epoch 996/1000 took 39.428s to complete with an average loss of 0.039619
Epoch 997/1000 took 39.435s to complete with an average loss of 0.036256
Epoch 998/1000 took 39.413s to complete with an average loss of 0.036478
Epoch 999/1000 took 39.388s to complete with an average loss of 0.040061
Epoch 1000/1000 took 39.430s to complete with an average loss of 0.036235
Training completed...
Saving the trained model to a file...

CNN accuracy over the test set is 66.13%

Author

Castellini Jacopo