aboutsummaryrefslogtreecommitdiff
path: root/nerv/layer/init.lua
blob: 43c2250cafcb6be7f3fb5293258d4c0f9e6e2d1e (plain) (blame)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
-- 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_list, lconf, gconf, p_type, p_dim)
    if type(pid_list) == "string" then
        pid_list = {pid_list}
    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]
        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
        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))
    if type(gconf.param_random) ~= "function" then
        nerv.error("a param generate function is needed")
    end
    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')