From 89a3fa93d571f446bcd1fa69ddd35257d975c239 Mon Sep 17 00:00:00 2001
From: Qi Liu <liuq901@163.com>
Date: Mon, 9 May 2016 17:57:30 +0800
Subject: merge affine & lstm_gate & projection layer

---
 nerv/layer/projection.lua | 70 -----------------------------------------------
 1 file changed, 70 deletions(-)
 delete mode 100644 nerv/layer/projection.lua

(limited to 'nerv/layer/projection.lua')

diff --git a/nerv/layer/projection.lua b/nerv/layer/projection.lua
deleted file mode 100644
index 077125b..0000000
--- a/nerv/layer/projection.lua
+++ /dev/null
@@ -1,70 +0,0 @@
-local ProjectionLayer = nerv.class('nerv.ProjectionLayer', 'nerv.Layer')
-
---- The constructor.
-function ProjectionLayer:__init(id, global_conf, layer_conf)
-    nerv.Layer.__init(self, id, global_conf, layer_conf)
-    self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs
-    self:bind_params()
-end
-
-function ProjectionLayer:bind_params()
-    local lconf = self.lconf
-    lconf.no_update_ltp1 = lconf.no_update_ltp1 or lconf.no_update_ltp
-    for i = 1, #self.dim_in do
-        local pid = "ltp" .. i
-        local pid_list = i == 1 and {pid, "ltp"} or pid
-        self["ltp" .. i] = self:find_param(pid_list, lconf, self.gconf,
-                                            nerv.LinearTransParam,
-                                            {self.dim_in[i], self.dim_out[1]})
-        local no_update = lconf["no_update_ltp" .. i]
-        if (no_update ~= nil) and no_update or lconf.no_update_all then
-            self["ltp" .. i].no_update = true
-        end
-    end
-    self.ltp = self.ltp1 -- alias of ltp1
-end
-
-function ProjectionLayer:init(batch_size)
-    for i = 1, #self.dim_in do
-        if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then
-            nerv.error("mismatching dimensions of linear transform parameter and input")
-        end
-        if self.dim_out[1] ~= self["ltp" .. i].trans:ncol() then
-            nerv.error("mismatching dimensions of linear transform parameter and output")
-        end
-        self["ltp" .. i]:train_init()
-    end
-end
-
-function ProjectionLayer:batch_resize(batch_size)
-    -- do nothing
-end
-
-function ProjectionLayer:update()
-    for i = 1, #self.dim_in do
-        self["ltp" .. i]:update_by_err_input()
-    end
-end
-
-function ProjectionLayer:propagate(input, output)
-    -- apply linear transform
-    output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N')
-    for i = 2, #self.dim_in do
-        output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N')
-    end
-end
-
-function ProjectionLayer:back_propagate(bp_err, next_bp_err, input, output)
-    for i = 1, #self.dim_in do
-        next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T')
-        self["ltp" .. i]:back_propagate_by_err_input(bp_err[1], input[i])
-    end
-end
-
-function ProjectionLayer:get_params()
-    local pr = nerv.ParamRepo({self.ltp1}, self.loc_type)
-    for i = 2, #self.dim_in do
-        pr:add(self["ltp" .. i])
-    end
-    return pr
-end
-- 
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