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-- The following methods must be implemented to let a layer work properly
local Param = nerv.class('nerv.Param')
function Param:__init(id, global_conf)
self.id = id
self.gconf = global_conf
end
function Param:get_info()
return self.info
end
function Param:set_info(info)
self.info = info
end
function Param:read(handle)
nerv.error_method_not_implemented()
end
function Param:write(handle)
nerv.error_method_not_implemented()
end
function Param:update(gradient)
nerv.error_method_not_implemented()
end
local Layer = nerv.class('nerv.Layer')
function Layer:__init(id, global_conf, layer_conf)
nerv.error_method_not_implemented()
end
function Layer:init(batch_size)
nerv.error_method_not_implemented()
end
function Layer:update(bp_err, input, output)
nerv.error_method_not_implemented()
end
function Layer:propagate(input, output)
nerv.error_method_not_implemented()
end
function Layer:back_propagate(bp_err, next_bp_err, input, output)
nerv.error_method_not_implemented()
end
function Layer:check_dim_len(len_in, len_out)
local expected_in = #self.dim_in
local expected_out = #self.dim_out
if len_in > 0 and expected_in ~= len_in then
nerv.error("layer %s expects %d inputs, %d given",
self.id, len_in, expected_in)
end
if len_out > 0 and expected_out ~= len_out then
nerv.error("layer %s expects %d outputs, %d given",
self.id, len_out, expected_out)
end
end
function Layer:get_params()
nerv.error_method_not_implemented()
end
function Layer:get_dim()
return self.dim_in, self.dim_out
end
function Layer:find_param(pid, l_conf, gconf, p_type, p_dim)
if l_conf[pid] ~= nil then
nerv.info("Param [%s] of layer [%s] found in layer_conf.", pid, self.id)
return l_conf[pid]
end
local pid_g = self.id .. '_' .. pid --global identifier
local pr = l_conf.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.paramRepo.", pid, self.id)
p = pr:get_param(pid_g)
return p
end
nerv.info("Param [%s] of layer [%s] is not found in layer_conf or layer_conf.paramRepo, switch to auto-generate.", pid, self.id)
p = p_type(pid_g, gconf)
p.trans = gconf.cumat_type(unpack(p_dim))
p.trans:generate(gconf.param_random)
return p
end
nerv.include('affine.lua')
nerv.include('sigmoid.lua')
nerv.include('tanh.lua')
nerv.include('softmax_ce.lua')
nerv.include('bias.lua')
nerv.include('window.lua')
nerv.include('mse.lua')
nerv.include('combiner.lua')
nerv.include('affine_recurrent.lua')
nerv.include('softmax.lua')
nerv.include('elem_mul.lua')
nerv.include('gate_fff.lua')
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