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