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authorDeterminant <[email protected]>2015-06-22 19:01:29 +0800
committerDeterminant <[email protected]>2015-06-22 19:01:29 +0800
commit2497fd9e7a0fae5ee4887890d7a312e0e08a93b8 (patch)
tree382f97575bd2df9ee6abb1662b11b279fc22d72b /layer/affine.lua
parent196e9b48a3541caccdffc5743001cced70667091 (diff)
major change: use luarocks to manage project
Diffstat (limited to 'layer/affine.lua')
-rw-r--r--layer/affine.lua91
1 files changed, 0 insertions, 91 deletions
diff --git a/layer/affine.lua b/layer/affine.lua
deleted file mode 100644
index 00cbcfb..0000000
--- a/layer/affine.lua
+++ /dev/null
@@ -1,91 +0,0 @@
-local MatrixParam = nerv.class('nerv.MatrixParam', 'nerv.Param')
-local LinearTransParam = nerv.class('nerv.LinearTransParam', 'nerv.MatrixParam')
-local BiasParam = nerv.class('nerv.BiasParam', 'nerv.MatrixParam')
-local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer')
-
-function MatrixParam:read(handle)
- self.trans = self.gconf.cumat_type.new_from_host(
- nerv.MMatrixFloat.load(handle))
-end
-
-function MatrixParam:write(handle)
- self.trans:new_to_host():save(handle)
-end
-
-function MatrixParam:train_init()
- self.correction = self.trans:create()
- self.correction:fill(0)
-end
-
-function MatrixParam:update(gradient)
- local gconf = self.gconf
- self.correction:add(self.correction, gradient, gconf.momentum, 1.0)
- -- momentum gain
- local mmt_gain = 1.0 / (1.0 - gconf.momentum);
- local n = self.gconf.batch_size * mmt_gain
- -- perform update
- self.trans:add(self.trans, self.correction, 1.0, -gconf.lrate / n)
-end
-
-function LinearTransParam:update(gradient)
- MatrixParam.update(self, gradient)
- local gconf = self.gconf
- -- weight decay
- self.trans:add(self.trans, self.trans, 1.0, -gconf.lrate * gconf.wcost)
-end
-
-function AffineLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.ltp = layer_conf.ltp
- self.bp = layer_conf.bp
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
- self:check_dim_len(1, 1) -- exactly one input and one output
- self.direct_update = layer_conf.direct_update
-end
-
-function AffineLayer:init(batch_size)
- if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then
- nerv.error("mismatching dimensions of linear transform and bias paramter")
- end
- if self.dim_in[1] ~= self.ltp.trans:nrow() then
- nerv.error("mismatching dimensions of linear transform parameter and input")
- end
- if self.dim_out[1] ~= self.ltp.trans:ncol() then
- nerv.error("mismatching dimensions of linear transform parameter and output")
- end
- self.ltp_grad = self.ltp.trans:create()
- self.ltp:train_init()
- self.bp:train_init()
-end
-
-function AffineLayer:update(bp_err, input, output)
- if self.direct_update then
- self.ltp.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
- -- momentum gain
- local mmt_gain = 1.0 / (1.0 - gconf.momentum);
- local n = self.gconf.batch_size * mmt_gain
- -- perform update
- self.ltp.trans:add(self.ltp.trans, self.ltp.correction, 1.0, -gconf.lrate / n)
- else
- self.ltp_grad:mul(input[1], bp_err[1], 1.0, 0.0, 'T', 'N')
- self.ltp:update(self.ltp_grad)
- end
- self.bp:update(bp_err[1]:colsum())
-end
-
-function AffineLayer:propagate(input, output)
- -- apply linear transform
- output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N')
- -- add bias
- output[1]:add_row(self.bp.trans, 1.0)
-end
-
-function AffineLayer:back_propagate(bp_err, next_bp_err, input, output)
- next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T')
-end
-
-function AffineLayer:get_params()
- return nerv.ParamRepo({self.ltp, self.bp})
-end