sgd alternatives and similar packages
Based on the "Algorithms" category
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of sgd or a related project?
Haskell stochastic gradient descent library
Stochastic gradient descent (SGD) is a method for optimizing a global objective function defined as a sum of smaller, differentiable functions. In each iteration of SGD the gradient is calculated based on a subset of the training dataset. In Haskell, this process can be simply represented as a fold over a of subsequent dataset subsets (singleton elements in the extreme).
However, it can be beneficial to select the subsequent subsets randomly (e.g.,
shuffle the entire dataset before each pass). Moreover, the dataset can be
large enough to make it impractical to store it all in memory. Hence, the
sgd library adopts a pipe-based
interface in which SGD takes the form of a process consuming dataset subsets
(the so-called mini-batches) and producing a stream of output parameter values.
sgd library implements several SGD variants (SGD with momentum, AdaDelta,
Adam) and handles heterogeneous parameter representations (vectors, maps, custom
records, etc.). It can be used in combination with automatic differentiation
backprop), which can be used to
automatically calculate the gradient of the objective function.
Look at the hackage repository for a library documentation.