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local DAGLayer = nerv.class("nerv.DAGLayer", "nerv.Layer")
local function parse_id(str)
local id, port, _
_, _, id, port = string.find(str, "([a-zA-Z0-9_]+)%[([0-9]+)%]")
if id == nil or port == nil then
_, _, id, port = string.find(str, "(.+)%[([0-9]+)%]")
if not (id == "<input>" or id == "<output>") then
nerv.error("wrong format of connection id")
end
end
port = tonumber(port)
return id, port
end
local function discover(id, layers, layer_repo)
local ref = layers[id]
if id == "<input>" or id == "<output>" then
return nil
end
if ref == nil then
local layer = layer_repo:get_layer(id)
local dim_in, dim_out = layer:get_dim()
ref = {
layer = layer,
inputs = {},
outputs = {},
err_inputs = {},
err_outputs = {},
next_layers = {},
input_len = #dim_in,
output_len = #dim_out,
in_deg = 0,
visited = false
}
layers[id] = ref
end
return ref
end
function nerv.DAGLayer:__init(id, global_conf, layer_conf)
local layers = {}
local inputs = {}
local outputs = {}
local dim_in = layer_conf.dim_in
local dim_out = layer_conf.dim_out
for from, to in pairs(layer_conf.connections) do
local id_from, port_from = parse_id(from)
local id_to, port_to = parse_id(to)
local ref_from = discover(id_from, layers, layer_conf.sub_layers)
local ref_to = discover(id_to, layers, layer_conf.sub_layers)
local input_dim, output_dim, _
if ref_from and ref_from.outputs[port_from] ~= nil then
nerv.error("%s has already been attached", from)
end
if ref_to and ref_to.inputs[port_to] ~= nil then
nerv.error("%s has already been attached", to)
end
if id_from == "<input>" then
input_dim, _ = ref_to.layer:get_dim()
if dim_in[port_from] ~= input_dim[port_to] then
nerv.error("mismatching data dimension between %s and %s", from, to)
end
inputs[port_from] = {ref_to, port_to}
ref_to.inputs[port_to] = inputs -- just a place holder
elseif id_to == "<output>" then
_, output_dim = ref_from.layer:get_dim()
if output_dim[port_from] ~= dim_out[port_to] then
nerv.error("mismatching data dimension between %s and %s", from, to)
end
outputs[port_to] = {ref_from, port_from}
ref_from.outputs[port_from] = outputs -- just a place holder
else
_, output_dim = ref_from.layer:get_dim()
input_dim, _ = ref_to.layer:get_dim()
if output_dim[port_from] ~= input_dim[port_to] then
nerv.error("mismatching data dimension between %s and %s", from, to)
end
local mid = global_conf.mat_type(global_conf.batch_size,
output_dim[port_from])
local err_mid = mid:create()
ref_from.outputs[port_from] = mid
ref_to.inputs[port_to] = mid
ref_from.err_inputs[port_from] = err_mid
ref_to.err_outputs[port_to] = err_mid
table.insert(ref_from.next_layers, ref_to) -- add edge
ref_to.in_deg = ref_to.in_deg + 1 -- increase the in-degree of the target layer
end
end
self.layers = layers
self.inputs = inputs
self.outputs = outputs
self.dim_in = dim_in
self.dim_out = dim_out
end
function nerv.DAGLayer:init(id) -- topology sort
local queue = {}
local l = 1
local r = 1
for id, ref in pairs(self.layers) do
if ref.in_deg == 0 then
table.insert(queue, ref)
nerv.utils.printf("adding source layer: %s\n", id)
r = r + 1
end
end
if l == r then
nerv.error("loop detected")
end
while l < r do
local cur = queue[l]
cur.visited = true
l = l + 1
for _, nl in pairs(cur.next_layers) do
nl.in_deg = nl.in_deg - 1
if nl.in_deg == 0 then
table.insert(queue, nl)
r = r + 1
end
end
end
for i = 1, #queue do
nerv.utils.printf("queued layer: %s\n", queue[i].layer.id)
end
self.queue = queue
for id, ref in pairs(self.layers) do
-- check wether the graph is connected
if ref.visited == false then
nerv.utils.printf("warning: layer %s is ignored\n", id)
end
for i = 1, ref.input_len do
if ref.inputs[i] == nil then
nerv.error("dangling port %d of layer %s", i, id)
end
end
for i = 1, ref.output_len do
if ref.outputs[i] == nil then
nerv.error("dangling port %d of layer %s", i, id)
end
end
-- initialize sub layers
ref.layer:init()
end
for i = 1, #self.dim_in do
if self.inputs[i] == nil then
nerv.error("dangling port %d of layer <input>", i)
end
end
for i = 1, #self.dim_out do
if self.outputs[i] == nil then
nerv.error("dangling port %d of layer <output>", i)
end
end
end
function nerv.DAGLayer:set_inputs(input)
for i = 1, #self.dim_in do
local layer = self.inputs[i][1]
local port = self.inputs[i][2]
layer.inputs[port] = input[i]
end
end
function nerv.DAGLayer:set_outputs(output)
for i = 1, #self.dim_out do
local layer = self.outputs[i][1]
local port = self.outputs[i][2]
layer.outputs[port] = output[i]
end
end
function nerv.DAGLayer:set_err_inputs(bp_err)
for i = 1, #self.dim_out do
local layer = self.outputs[i][1]
local port = self.outputs[i][2]
layer.err_inputs[port] = bp_err[i]
end
end
function nerv.DAGLayer:set_err_outputs(next_bp_err)
for i = 1, #self.dim_in do
local layer = self.inputs[i][1]
local port = self.inputs[i][2]
layer.err_outputs[port] = next_bp_err[i]
end
end
function nerv.DAGLayer:update(bp_err, input, output)
self:set_err_inputs(bp_err)
self:set_inputs(input)
self:set_outputs(output)
for id, ref in pairs(self.queue) do
ref.layer:update(ref.err_inputs, ref.inputs, ref.outputs)
end
end
function nerv.DAGLayer:propagate(input, output)
self:set_inputs(input)
self:set_outputs(output)
for i = 1, #self.queue do
local ref = self.queue[i]
--[[
print(ref.inputs[1])
print(ref.outputs[1])
print(#ref.inputs, #ref.outputs)
--]]
ref.layer:propagate(ref.inputs, ref.outputs)
end
end
function nerv.DAGLayer:back_propagate(next_bp_err, bp_err, input, output)
self:set_err_outputs(next_bp_err)
self:set_err_inputs(bp_err)
self:set_inputs(input)
self:set_outputs(output)
for i = #self.queue, 1, -1 do
local ref = self.queue[i]
-- print(ref.layer.id)
ref.layer:back_propagate(ref.err_outputs, ref.err_inputs, ref.inputs, ref.outputs)
-- if #ref.err_outputs > 0 then
-- print(ref.err_outputs[1])
-- end
end
end
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