1 files changed, 91 insertions, 0 deletions
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
new file mode 100644
index 0000000..00cbcfb
--- /dev/null
+++ b/nerv/layer/affine.lua
@@ -0,0 +1,91 @@
+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