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-rw-r--r--nerv/layer/affine.lua43
-rw-r--r--nerv/layer/bias.lua15
-rw-r--r--nerv/layer/combiner.lua16
-rw-r--r--nerv/layer/dropout.lua16
-rw-r--r--nerv/layer/elem_mul.lua11
-rw-r--r--nerv/layer/gru.lua20
-rw-r--r--nerv/layer/init.lua60
-rw-r--r--nerv/layer/lstm.lua20
-rw-r--r--nerv/layer/lstm_gate.lua17
-rw-r--r--nerv/layer/mse.lua16
-rw-r--r--nerv/layer/sigmoid.lua11
-rw-r--r--nerv/layer/softmax.lua11
-rw-r--r--nerv/layer/softmax_ce.lua16
-rw-r--r--nerv/layer/tanh.lua11
-rw-r--r--nerv/layer/window.lua15
15 files changed, 160 insertions, 138 deletions
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
index 4156dde..38743aa 100644
--- a/nerv/layer/affine.lua
+++ b/nerv/layer/affine.lua
@@ -8,21 +8,19 @@ local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer')
--- A parameter that consists of a single matrix
-- @type nerv.MatrixParam
+function MatrixParam:check(checker)
+ -- check trans matrix type
+ checker(self.trans)
+end
+
--- Read from a file handle.
-- @param handle the file handle
function MatrixParam:read(handle)
self.trans = self.gconf.mmat_type.load(handle)
- if not self.gconf.use_cpu then
- self.trans = self.gconf.cumat_type.new_from_host(self.trans)
- end
end
function MatrixParam:write(handle)
- local trans = self.trans
- if not self.gconf.use_cpu then
- trans = self.trans:new_to_host()
- end
- trans:save(handle)
+ self.trans:save(handle)
end
function MatrixParam:train_init()
@@ -30,6 +28,12 @@ function MatrixParam:train_init()
self.correction:fill(0)
end
+function MatrixParam:copy(copier)
+ local target = nerv.MatrixParam(self.id, self.gconf)
+ target.trans = copier(self.trans)
+ return target
+end
+
function MatrixParam:_update_by_gradient(gradient, alpha, beta)
local gconf = self.gconf
-- momentum gain
@@ -77,25 +81,24 @@ end
--- The constructor.
function AffineLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- if layer_conf.ltp ~= nil and layer_conf.ltp1 == nil then
- layer_conf.ltp1 = layer_conf.ltp
- end
+ 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 AffineLayer:bind_params()
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, layer_conf, global_conf,
+ self["ltp" .. i] = self:find_param(pid_list, self.lconf, self.gconf,
nerv.LinearTransParam,
- {self.dim_in[i], self.dim_out[1]})
+ {self.dim_in[i], self.dim_out[1]})
end
self.ltp = self.ltp1 -- alias of ltp1
- self.bp = self:find_param("bp", layer_conf, global_conf,
+ self.bp = self:find_param("bp", self.lconf, self.gconf,
nerv.BiasParam,
{1, self.dim_out[1]})
- self.gconf = global_conf
- self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs
+
end
function AffineLayer:init(batch_size)
@@ -142,7 +145,7 @@ function AffineLayer:back_propagate(bp_err, next_bp_err, input, output)
end
function AffineLayer:get_params()
- local pr = nerv.ParamRepo({self.ltp1, self.bp})
+ local pr = nerv.ParamRepo({self.ltp1, self.bp}, self.loc_type)
for i = 2, #self.dim_in do
pr:add(self["ltp" .. i].id, self["ltp" .. i])
end
diff --git a/nerv/layer/bias.lua b/nerv/layer/bias.lua
index 924c3da..191be78 100644
--- a/nerv/layer/bias.lua
+++ b/nerv/layer/bias.lua
@@ -1,12 +1,15 @@
local BiasLayer = nerv.class("nerv.BiasLayer", "nerv.Layer")
function BiasLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- self.bias = layer_conf.bias
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(1, 1)
+ self:bind_params()
+end
+
+function BiasLayer:bind_params()
+ self.bias = self:find_param("bias", self.lconf, self.gconf,
+ nerv.BiasParam,
+ {1, self.dim_out[1]})
end
function BiasLayer:init()
@@ -28,5 +31,5 @@ function BiasLayer:propagate(input, output)
end
function BiasLayer:get_params()
- return nerv.ParamRepo({self.bias})
+ return nerv.ParamRepo({self.bias}, self.loc_type)
end
diff --git a/nerv/layer/combiner.lua b/nerv/layer/combiner.lua
index 22e89a9..028c970 100644
--- a/nerv/layer/combiner.lua
+++ b/nerv/layer/combiner.lua
@@ -1,16 +1,8 @@
local CombinerLayer = nerv.class('nerv.CombinerLayer', 'nerv.Layer')
function CombinerLayer:__init(id, global_conf, layer_conf)
- self.id = id
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self.lambda = layer_conf.lambda
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
- if self.gconf.use_cpu then
- self.mat_type = self.gconf.mmat_type
- else
- self.mat_type = self.gconf.cumat_type
- end
self:check_dim_len(#self.lambda, -1)
if #self.dim_in < 1 then
nerv.error("no input specified")
@@ -20,6 +12,10 @@ function CombinerLayer:__init(id, global_conf, layer_conf)
end
end
+function CombinerLayer:bind_params()
+ -- do nothing
+end
+
function CombinerLayer:init(batch_size)
local dim = self.dim_in[1]
for i = 2, #self.dim_in do
@@ -66,5 +62,5 @@ function CombinerLayer:back_propagate(bp_err, next_bp_err, input, output)
end
function CombinerLayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/dropout.lua b/nerv/layer/dropout.lua
index 42660cc..1a379c9 100644
--- a/nerv/layer/dropout.lua
+++ b/nerv/layer/dropout.lua
@@ -1,22 +1,18 @@
local DropoutLayer = nerv.class("nerv.DropoutLayer", "nerv.Layer")
function DropoutLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- if self.gconf.use_cpu then
- self.mat_type = self.gconf.mmat_type
- else
- self.mat_type = self.gconf.cumat_type
- end
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self.rate = layer_conf.dropout_rate or global_conf.dropout_rate
if self.rate == nil then
nerv.warning("[DropoutLayer:propagate] dropout rate is not set")
end
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
self:check_dim_len(1, 1) -- two inputs: nn output and label
end
+function DropoutLayer:bind_params()
+ -- do nothing
+end
+
function DropoutLayer:init(batch_size, chunk_size)
if self.dim_in[1] ~= self.dim_out[1] then
nerv.error("mismatching dimensions of input and output")
@@ -73,5 +69,5 @@ function DropoutLayer:back_propagate(bp_err, next_bp_err, input, output, t)
end
function DropoutLayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/elem_mul.lua b/nerv/layer/elem_mul.lua
index fe80a3f..f03649b 100644
--- a/nerv/layer/elem_mul.lua
+++ b/nerv/layer/elem_mul.lua
@@ -1,14 +1,15 @@
local ElemMulLayer = nerv.class('nerv.ElemMulLayer', 'nerv.Layer')
function ElemMulLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
-- element-wise multiplication of input[1] and input[2]
self:check_dim_len(2, 1)
end
+function ElemMulLayer:bind_params()
+ -- do nothing
+end
+
function ElemMulLayer:init(batch_size)
if self.dim_in[1] ~= self.dim_in[2] or
self.dim_in[1] ~= self.dim_out[1] then
@@ -34,5 +35,5 @@ function ElemMulLayer:update(bp_err, input, output)
end
function ElemMulLayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/gru.lua b/nerv/layer/gru.lua
index e81d21a..a590a67 100644
--- a/nerv/layer/gru.lua
+++ b/nerv/layer/gru.lua
@@ -4,11 +4,7 @@ function GRULayer:__init(id, global_conf, layer_conf)
-- input1:x
-- input2:h
-- input3:c (h^~)
- self.id = id
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
-
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
if self.dim_in[2] ~= self.dim_out[1] then
nerv.error("dim_in[2](%d) mismatch with dim_out[1](%d)",
self.dim_in[2], self.dim_out[1])
@@ -17,7 +13,7 @@ function GRULayer:__init(id, global_conf, layer_conf)
-- prepare a DAGLayer to hold the lstm structure
local pr = layer_conf.pr
if pr == nil then
- pr = nerv.ParamRepo()
+ pr = nerv.ParamRepo(nil, self.loc_type)
end
local function ap(str)
@@ -63,7 +59,7 @@ function GRULayer:__init(id, global_conf, layer_conf)
},
}
- local layerRepo = nerv.LayerRepo(layers, pr, global_conf)
+ self.lrepo = nerv.LayerRepo(layers, pr, global_conf)
local connections = {
["<input>[1]"] = ap("inputXDup[1]"),
@@ -97,12 +93,20 @@ function GRULayer:__init(id, global_conf, layer_conf)
self.dag = nerv.DAGLayer(self.id, global_conf,
{dim_in = self.dim_in,
dim_out = self.dim_out,
- sub_layers = layerRepo,
+ sub_layers = self.lrepo,
connections = connections})
self:check_dim_len(2, 1) -- x, h and h
end
+function GRULayer:bind_params()
+ local pr = layer_conf.pr
+ if pr == nil then
+ pr = nerv.ParamRepo(nil, self.loc_type)
+ end
+ self.lrepo:rebind(pr)
+end
+
function GRULayer:init(batch_size, chunk_size)
self.dag:init(batch_size, chunk_size)
end
diff --git a/nerv/layer/init.lua b/nerv/layer/init.lua
index 54f33ae..146ad8c 100644
--- a/nerv/layer/init.lua
+++ b/nerv/layer/init.lua
@@ -30,7 +30,18 @@ end
local Layer = nerv.class('nerv.Layer')
function Layer:__init(id, global_conf, layer_conf)
- nerv.error_method_not_implemented()
+ self.id = id
+ self.gconf = global_conf
+ self.lconf = layer_conf
+ if self.gconf.use_cpu then
+ self.mat_type = self.gconf.mmat_type
+ self.loc_type = nerv.ParamRepo.LOC_TYPES.ON_HOST
+ else
+ self.mat_type = self.gconf.cumat_type
+ self.loc_type = nerv.ParamRepo.LOC_TYPES.ON_DEVICE
+ end
+ self.dim_in = layer_conf.dim_in
+ self.dim_out = layer_conf.dim_out
end
function Layer:init(batch_size)
@@ -66,34 +77,41 @@ function Layer:get_params()
nerv.error_method_not_implemented()
end
+function Layer:bind_params()
+ nerv.error_method_not_implemented()
+end
+
function Layer:get_dim()
return self.dim_in, self.dim_out
end
-function Layer:find_param(pid_list, lconf, gconf, p_type, p_dim)
- if type(pid_list) == "string" then
- pid_list = {pid_list}
+function Layer:find_param(plist, lconf, gconf, p_type, p_dim)
+ if type(plist) == "string" then
+ plist = {plist}
end
- pid_list_str = table.tostring(pid_list)
- for i, pid in ipairs(pid_list) do
- if lconf[pid] ~= nil then
- nerv.info("param [%s] of layer [%s] found in `layer_conf`.", pid, self.id)
- return lconf[pid]
+ if lconf.params == nil then
+ lconf.params = {}
+ end
+ plist_str = table.tostring(plist)
+ local pid
+ for i, pname in ipairs(plist) do
+ if lconf.params[pname] ~= nil then
+ nerv.info("param id for [%s] of layer [%s] specified in `layer_conf.params`.", pname, self.id)
+ pid = lconf.params[pname]
end
- local pid_g = self.id .. '_' .. pid --global identifier
- local pr = lconf.pr
- local p
- if pr ~= nil and pr:has_param(pid_g) == true then
- nerv.info("param [%s] of layer [%s] found in `layer_conf.pr`.", pid_list_str, self.id)
- p = pr:get_param(pid_g)
- return p
+ if lconf.pr:has_param(pid) then
+ return lconf.pr:get_param(pid)
end
end
- nerv.info("param [%s] of layer [%s] is not found in `layer_conf` or `layer_conf.pr`, " ..
- "switch to auto-generate", pid_list_str, self.id)
- local pid_g = self.id .. '_' .. pid_list[1]
- p = p_type(pid_g, gconf)
- p.trans = gconf.cumat_type(unpack(p_dim))
+ pid = self.id .. '_' .. plist[1]
+ if lconf.pr:has_param(pid) then
+ nerv.info("param id for [%s] of layer [%s] is generated automatically.", pname, self.id)
+ return lconf.pr:get_param(pid)
+ end
+ nerv.info("param id for [%s] of layer [%s] is not found in the specified param repo, " ..
+ "switch to auto-generate", plist_str, self.id)
+ local p = p_type(pid, gconf)
+ p.trans = self.mat_type(unpack(p_dim))
if type(gconf.param_random) ~= "function" then
nerv.error("a param generate function is needed")
end
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index 500bd87..d4c9212 100644
--- a/nerv/layer/lstm.lua
+++ b/nerv/layer/lstm.lua
@@ -4,15 +4,11 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
-- input1:x
-- input2:h
-- input3:c
- self.id = id
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
-
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
-- prepare a DAGLayer to hold the lstm structure
local pr = layer_conf.pr
if pr == nil then
- pr = nerv.ParamRepo()
+ pr = nerv.ParamRepo(nil, self.loc_type)
end
local function ap(str)
@@ -66,7 +62,7 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
},
}
- local layerRepo = nerv.LayerRepo(layers, pr, global_conf)
+ self.lrepo = nerv.LayerRepo(layers, pr, global_conf)
local connections = {
["<input>[1]"] = ap("inputXDup[1]"),
@@ -109,12 +105,20 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
self.dag = nerv.DAGLayer(self.id, global_conf,
{dim_in = self.dim_in,
dim_out = self.dim_out,
- sub_layers = layerRepo,
+ sub_layers = self.lrepo,
connections = connections})
self:check_dim_len(3, 2) -- x, h, c and h, c
end
+function LSTMLayer:bind_params()
+ local pr = layer_conf.pr
+ if pr == nil then
+ pr = nerv.ParamRepo(nil, self.loc_type)
+ end
+ self.lrepo:rebind(pr)
+end
+
function LSTMLayer:init(batch_size, chunk_size)
self.dag:init(batch_size, chunk_size)
end
diff --git a/nerv/layer/lstm_gate.lua b/nerv/layer/lstm_gate.lua
index 1963eba..7a27bab 100644
--- a/nerv/layer/lstm_gate.lua
+++ b/nerv/layer/lstm_gate.lua
@@ -2,20 +2,19 @@ local LSTMGateLayer = nerv.class('nerv.LSTMGateLayer', 'nerv.Layer')
-- NOTE: this is a full matrix gate
function LSTMGateLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
+ self:check_dim_len(-1, 1) --accept multiple inputs
+ self:bind_params()
+end
+function LSTMGateLayer:bind_params()
for i = 1, #self.dim_in do
- self["ltp" .. i] = self:find_param("ltp" .. i, layer_conf, global_conf,
+ self["ltp" .. i] = self:find_param("ltp" .. i, self.lconf, self.gconf,
nerv.LinearTransParam,
{self.dim_in[i], self.dim_out[1]})
end
- self.bp = self:find_param("bp", layer_conf, global_conf,
+ self.bp = self:find_param("bp", self.lconf, self.gconf,
nerv.BiasParam, {1, self.dim_out[1]})
-
- self:check_dim_len(-1, 1) --accept multiple inputs
end
function LSTMGateLayer:init(batch_size)
@@ -69,7 +68,7 @@ function LSTMGateLayer:update(bp_err, input, output)
end
function LSTMGateLayer:get_params()
- local pr = nerv.ParamRepo({self.bp})
+ local pr = nerv.ParamRepo({self.bp}, self.loc_type)
for i = 1, #self.dim_in do
pr:add(self["ltp" .. i].id, self["ltp" .. i])
end
diff --git a/nerv/layer/mse.lua b/nerv/layer/mse.lua
index 1c218d0..458d086 100644
--- a/nerv/layer/mse.lua
+++ b/nerv/layer/mse.lua
@@ -1,18 +1,14 @@
local MSELayer = nerv.class("nerv.MSELayer", "nerv.Layer")
function MSELayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
- self.gconf = global_conf
- if self.gconf.use_cpu then
- self.mat_type = self.gconf.mmat_type
- else
- self.mat_type = self.gconf.cumat_type
- end
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(2, -1)
end
+function MSELayer:bind_params()
+ -- do nothing
+end
+
function MSELayer:init(batch_size)
if self.dim_in[1] ~= self.dim_in[2] then
nerv.error("mismatching dimensions of previous network output and labels")
@@ -61,5 +57,5 @@ function MSELayer:back_propagate(bp_err, next_bp_err, input, output)
end
function MSELayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/sigmoid.lua b/nerv/layer/sigmoid.lua
index 0a8bcdc..a9f9749 100644
--- a/nerv/layer/sigmoid.lua
+++ b/nerv/layer/sigmoid.lua
@@ -1,13 +1,14 @@
local SigmoidLayer = nerv.class("nerv.SigmoidLayer", "nerv.Layer")
function SigmoidLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(1, 1)
end
+function SigmoidLayer:bind_params()
+ -- do nothing
+end
+
function SigmoidLayer:init()
if self.dim_in[1] ~= self.dim_out[1] then
nerv.error("mismatching dimensions of input and output")
@@ -31,5 +32,5 @@ function SigmoidLayer:back_propagate(bp_err, next_bp_err, input, output)
end
function SigmoidLayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/softmax.lua b/nerv/layer/softmax.lua
index 4205b66..f7a5163 100644
--- a/nerv/layer/softmax.lua
+++ b/nerv/layer/softmax.lua
@@ -1,13 +1,14 @@
local SoftmaxLayer = nerv.class("nerv.SoftmaxLayer", "nerv.Layer")
function SoftmaxLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(1, 1) -- two inputs: nn output and label
end
+function SoftmaxLayer:bind_params()
+ -- do nothing
+end
+
function SoftmaxLayer:init(batch_size)
if self.dim_in[1] ~= self.dim_out[1] then
nerv.error("mismatching dimensions of input and output")
@@ -31,5 +32,5 @@ function SoftmaxLayer:back_propagate(bp_err, next_bp_err, input, output)
end
function SoftmaxLayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/softmax_ce.lua b/nerv/layer/softmax_ce.lua
index d7d650e..7b4a80c 100644
--- a/nerv/layer/softmax_ce.lua
+++ b/nerv/layer/softmax_ce.lua
@@ -1,15 +1,7 @@
local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayer", "nerv.Layer")
function SoftmaxCELayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- if self.gconf.use_cpu then
- self.mat_type = self.gconf.mmat_type
- else
- self.mat_type = self.gconf.cumat_type
- end
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self.compressed = layer_conf.compressed
if self.compressed == nil then
self.compressed = false
@@ -17,6 +9,10 @@ function SoftmaxCELayer:__init(id, global_conf, layer_conf)
self:check_dim_len(2, -1) -- two inputs: nn output and label
end
+function SoftmaxCELayer:bind_params()
+ -- do nothing
+end
+
function SoftmaxCELayer:init(batch_size, chunk_size)
if not self.compressed and (self.dim_in[1] ~= self.dim_in[2]) then
nerv.error("mismatching dimensions of previous network output and labels")
@@ -94,5 +90,5 @@ function SoftmaxCELayer:back_propagate(bp_err, next_bp_err, input, output, t)
end
function SoftmaxCELayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/tanh.lua b/nerv/layer/tanh.lua
index e1c32f2..7a19fc8 100644
--- a/nerv/layer/tanh.lua
+++ b/nerv/layer/tanh.lua
@@ -1,13 +1,14 @@
local TanhLayer = nerv.class("nerv.TanhLayer", "nerv.Layer")
function TanhLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(1, 1)
end
+function TanhLayer:bind_params()
+ -- do nothing
+end
+
function TanhLayer:init()
if self.dim_in[1] ~= self.dim_out[1] then
nerv.error("mismatching dimensions of input and output")
@@ -31,5 +32,5 @@ function TanhLayer:back_propagate(bp_err, next_bp_err, input, output)
end
function TanhLayer:get_params()
- return nerv.ParamRepo({})
+ return nerv.ParamRepo({}, self.loc_type)
end
diff --git a/nerv/layer/window.lua b/nerv/layer/window.lua
index 4933de0..364929f 100644
--- a/nerv/layer/window.lua
+++ b/nerv/layer/window.lua
@@ -1,12 +1,15 @@
local WindowLayer = nerv.class("nerv.WindowLayer", "nerv.Layer")
function WindowLayer:__init(id, global_conf, layer_conf)
- self.id = id
- self.gconf = global_conf
- self.window = layer_conf.window
- self.dim_in = layer_conf.dim_in
- self.dim_out = layer_conf.dim_out
+ nerv.Layer.__init(self, id, global_conf, layer_conf)
self:check_dim_len(1, 1)
+ self:bind_params()
+end
+
+function WindowLayer:bind_params()
+ self.window = self:find_param("window", self.lconf, self.gconf,
+ nerv.BiasParam,
+ {1, self.dim_out[1]})
end
function WindowLayer:init()
@@ -28,5 +31,5 @@ function WindowLayer:propagate(input, output)
end
function WindowLayer:get_params()
- return nerv.ParamRepo({self.window})
+ return nerv.ParamRepo({self.window}, self.loc_type)
end
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