About:
APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.
Changes:
-
Updated home repository link to follow april-org github organization.
-
Improved serialize/deserialize functions, reimplemented all the serialization
procedure.
-
Added exceptions support to LuaPkg and APRIL-ANN, allowing to capture C++
errors into Lua code.
-
Added
set class.
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Added
series class.
-
Added
data_frame class, similar to Python Pandas DataFrame.
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Serialization and deserilization have been updated with more robust and
reusable API, implemented in
util.serialize() and util.deserialize()
functions.
-
Added
matrix.ext.broadcast utility (similar to broadcast in numpy).
-
Added
ProbablisitcMatrixANNComponent, which allow to implement probabilistic
mixtures of posteriors and/or likelihoods.
-
Added batch normalization ANN component.
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Allowing
matrix.join to add new axis.
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Added methods
prod(), cumsum() and cumprod() at matrix classes.
-
Added methods
count_eq() and count_neq() at matrix classes.
-
Serializable objects API have been augmented with methods
ctor_name() and
ctor_params() in Lua, refered to luaCtorName() and luaCtorParams() in
C++.
-
Added
cast.to to dynamic cast C++ objects pushed into Lua, allowing to
convert base class objects into any of its derived classes.
-
Added
matrix.sparse as valid values for targets in ann.loss.mse and
ann.loss.cross_entropy.
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Changed
matrix metamethods __index and __newindex, allowing to use
matrix objects with standard Lua operator[].
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Added
matrix.masked_fill and matrix.masked_copy matrix.
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Added
matrix.indexed_fill and matrix.indexed_copy matrix.
-
Added
ann.components.probabilistic_matrix, and its corresponding
specializations ann.components.left_probabilistic_matrix and
ann.components.right_probabilistic_matrix.
-
Added operator[] in the right side of matrix operations.
-
Added
ann.components.transpose.
-
Added
max_gradients_norm in traianble.supervised_trainer, to avoid
gradients exploding.
-
Added
ann.components.actf.sparse_logistic a logistic activation function
with sparsity penalty.
-
Simplified
math.add, math.sub, ... and other math extensions for
reductions, their original behavior can be emulated by using bind function.
-
Added
bind function to freeze any positional argument of any Lua function.
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Function
stats.boot uses multiple_unpack to allow a table of sizes and the
generation of multiple index matrices.
-
Added
multiple_unpack Lua function.
-
Added
__tostring metamethod to numeric memory blocks in Lua.
-
Added
dataset.token.sparse_matrix, a dataset which allow to traverse by rows
a sparse matrix instance.
-
Added
matrix.sparse.builders.dok, a builder which uses the
Dictionary-of-Keys format to construct a sparse matrix from scratch.
-
Added method
data to numeric matrix classes.
-
Added methods
values, indices, first_index to sparse matrix class.
-
Fixed bugs when reading bad formed CSV files.
-
Fixed bugs at statistical distributions.
-
FloatRGB bug solved on equal (+=, -=, ...) operators. This bug affected
ImageRGB operations such as resize.
-
Solved problems when chaining methods in Lua, some objects end to be garbage
collected.
-
Improved support of strings in auto-completion (rlcompleter package).
-
Solved bug at
SparseMatrix<T> when reading it from a file.
-
Solved bug in
Image<T>::rotate90_cw methods.
-
Solved bug in
SparseMatrix::toDense() method.
C/C++
-
Better
LuaTable accessors, using [] operator.
-
Implementation of matrix
__index, __newindex and __call metamethods in
C++.
-
Implementation of
matProd(), matCumSum() and matCumProd() functions.
-
Implementation of
matCountEq() and matCountNeq() functions for
Matrix<T>.
-
Updated
matrix_ext_operations.h to change API of matrix operations. All
functions have been overloaded to accept an in-place operation and another
version which receives a destination matrix.
-
Adding iterators to language models.
-
Added
MatrixScalarMap2 which receives as input2 a SparaseMatrix
instance. This functions needs to be generalized to work with CPU and CUDA.
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The method
SparseMatrix<T>::fromDenseMatrix() uses a DOKBuilder object
to build the sparse matrix.
-
The conversion of a
Matrix<T> into a SparseMatrix<T> has been changed from
a constructor overload to the static method
SparseMatrix<T>::fromDenseMatrix().
-
Added support for IPyLua.
-
Optimized matrix access for confusion matrix.
-
Minor changes in
class.lua.
-
Improved binding to avoid multiple object copies when pushing C++ objects.
-
Added Git commit hash and compilation time.
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- Operating System:
Linux,
Mac Os X
- Data Formats:
Ascii,
Binary,
Matlab,
Tab Separated,
Png,
Pnm
- Tags:
Deep Learning,
Machine Learning,
Ann,
Hmms,
Viterbi
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