[Project] Remove torch

1 files changed, 0 insertions, 66 deletions

diff --git a/contrib/lua-torch/optim/adamax.lua b/contrib/lua-torch/optim/adamax.lua
deleted file mode 100644
index 2b6487720..000000000
--- a/contrib/lua-torch/optim/adamax.lua
+++ /dev/null
@@ -1,66 +0,0 @@
---[[ An implementation of AdaMax http://arxiv.org/pdf/1412.6980.pdf
-
-ARGS:
-
-- 'opfunc' : a function that takes a single input (X), the point
-             of a evaluation, and returns f(X) and df/dX
-- 'x'      : the initial point
-- 'config` : a table with configuration parameters for the optimizer
-- 'config.learningRate'      : learning rate
-- 'config.beta1'             : first moment coefficient
-- 'config.beta2'             : second moment coefficient
-- 'config.epsilon'           : for numerical stability
-- 'state'                    : a table describing the state of the optimizer;
-                               after each call the state is modified.
-
-RETURN:
-- `x`     : the new x vector
-- `f(x)`  : the function, evaluated before the update
-
-]]
-
-function optim.adamax(opfunc, x, config, state)
-   -- (0) get/update state
-   local config = config or {}
-   local state = state or config
-   local lr = config.learningRate or 0.002
-
-   local beta1 = config.beta1 or 0.9
-   local beta2 = config.beta2 or 0.999
-   local epsilon = config.epsilon or 1e-38
-   local wd = config.weightDecay or 0
-
-   -- (1) evaluate f(x) and df/dx
-   local fx, dfdx = opfunc(x)
-
-   -- (2) weight decay
-   if wd ~= 0 then
-      dfdx:add(wd, x)
-   end
-
-   -- Initialization
-   state.t = state.t or 0
-   -- Exponential moving average of gradient values
-   state.m = state.m or x.new(dfdx:size()):zero()
-   -- Exponential moving average of the infinity norm
-   state.u = state.u or x.new(dfdx:size()):zero()
-   -- A tmp tensor to hold the input to max()
-   state.max = state.max or x.new(2, unpack(dfdx:size():totable())):zero()
-
-   state.t = state.t + 1
-
-   -- Update biased first moment estimate.
-   state.m:mul(beta1):add(1-beta1, dfdx)
-   -- Update the exponentially weighted infinity norm.
-   state.max[1]:copy(state.u):mul(beta2)
-   state.max[2]:copy(dfdx):abs():add(epsilon)
-   state.u:max(state.max, 1)
-
-   local biasCorrection1 = 1 - beta1^state.t
-   local stepSize = lr/biasCorrection1
-   -- (2) update x
-   x:addcdiv(-stepSize, state.m, state.u)
-
-   -- return x*, f(x) before optimization
-   return x, {fx}
-end