autodiff, keyword, block
autodiff keyword introduces a block intended for a stochastic gradient descent. The logic within the
autodiff block is automatically differentiated. The keyword is followed by the name of the observation table.
autodiff Scalar epochs: 500 learningRate: 0.1 with params a auto s = a * a return s show scalar "" with a // 0.00
epochs is optional; its default value is 10.
learningRate is optional; its default value is 0.01.
The execution of the
autodiff block can be automatically parallelized. This execution mode is intended for larger datasets. This parallelization typically incurs a modest decrease of convergence speed - counted in epochs - in exchange for a faster wall-clock execution.
autodiff Scalar epochs: 500 learningRate: 0.1 mode: "parallel" with params a auto s = a * a return s show scalar "" with a // 0.00
In the script above, the
mode option is set to
autodiff, keyword, pure function option
autodiff keyword indicates that the pure function can be executed inside an
def autodiff pure mySquare(x: number) with return x * x autodiff Scalar epochs: 500 with params a auto return mySquare(a) show scalar "" with a // 0.00
In the present reference documentation, pure functions that are part of the Envision standard library and that can be executed inside an
autodiff block are marked as