This RNN is a reconstruction of Alex Graves' work presented in his paper: http://arxiv.org/abs/1308.0850
Data was provided by the IAM On-Line Handwriting Database http://www.fki.inf.unibe.ch/databases/iam-on-line-handwriting-database
The model is precisely the same as the one presented in the paper above. Essentially, this is an RNN with three LSTM layers, a windowing layer that learns attention for each character in the input string, and a mixture of Gaussians criterion layer.
To train the model:
th driver.lua -inputSize INPUT_DIMENSION -hiddenSize LSTM_UNIT_SIZE
-lr LEARNING_RATE -maxlen MAXIMUM_NUMBER_OF_TIMESTEPS
-batchSize BATCH_SIZE_OF_DATA -numPasses NUMBER_OF_PASSES
-valData PATH_TO_VALIDATION_DATA -trainData PATH_TO_TRAINING_DATA
Explore driver.lua for command options. Including specifying a validation and training set. Currently toy.t7 is provided, but a larger training set can be created by downloading the IAM handwriting database and following the directions in Graves' paper: http://www.fki.inf.unibe.ch/databases/iam-on-line-handwriting-database
- Each multicolored line represents a new pen stroke.
th testNet.lua -string 'How are you today'
th testNet.lua -string 'Testing testing'
th testNet.lua -string 'this is awesome'
- Below is the attention plot which represents by how many timesteps the model will focus on a certain character in the string 'this is awesome' (moving in a left to right direction along each character in the string)
