diff options
Diffstat (limited to 'nerv/tnn')
-rw-r--r-- | nerv/tnn/init.lua | 5 | ||||
-rw-r--r-- | nerv/tnn/layer_dag_t.lua | 386 | ||||
-rw-r--r-- | nerv/tnn/layersT/dropout_t.lua | 71 | ||||
-rw-r--r-- | nerv/tnn/layersT/gru_t.lua | 114 | ||||
-rw-r--r-- | nerv/tnn/layersT/lstm_t.lua | 124 | ||||
-rw-r--r-- | nerv/tnn/layersT/softmax_ce_t.lua | 93 |
6 files changed, 0 insertions, 793 deletions
diff --git a/nerv/tnn/init.lua b/nerv/tnn/init.lua index 7faca31..44ce26b 100644 --- a/nerv/tnn/init.lua +++ b/nerv/tnn/init.lua @@ -45,8 +45,3 @@ end nerv.include('sutil.lua') nerv.include('tnn.lua') -nerv.include('layersT/softmax_ce_t.lua') -nerv.include('layersT/lstm_t.lua') -nerv.include('layersT/gru_t.lua') -nerv.include('layersT/dropout_t.lua') -nerv.include('layer_dag_t.lua') diff --git a/nerv/tnn/layer_dag_t.lua b/nerv/tnn/layer_dag_t.lua deleted file mode 100644 index b651f4e..0000000 --- a/nerv/tnn/layer_dag_t.lua +++ /dev/null @@ -1,386 +0,0 @@ -local DAGLayerT = nerv.class("nerv.DAGLayerT", "nerv.LayerT") - -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 = { - id = layer.id, - 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 DAGLayerT:__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 - local parsed_conn = {} - 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 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} - if ref_to.inputs[1] == nil then - ref_to.inputs[1] = {} - end - if ref_to.inputs[1][port_to] ~= nil then - nerv.error("port(%d) for layer(%s) already attached", port_to, to) - end - ref_to.inputs[1][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} - if ref_from.outputs[1] == nil then - ref_from.outputs[1] = {} - end - if ref_from.outputs[1][port_from] ~= nil then - nerv.error("port(%d) for layer(%s) already attached", port_from, from) - end - ref_from.outputs[1] = {} - ref_from.outputs[1][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 - - table.insert(parsed_conn, - {{ref_from, port_from}, {ref_to, port_to}}) - 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 - - -- topology sort - local queue = {} - local l = 1 - local r = 1 - for id, ref in pairs(layers) do - if ref.in_deg == 0 then - table.insert(queue, ref) - nerv.info("adding source layer: %s", 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.info("enqueued layer: %s %s", queue[i].layer, queue[i].layer.id) - end - - for id, ref in pairs(layers) do - -- check wether the graph is connected - if ref.visited == false then - nerv.warning("layer %s is ignored", id) - end - end - - self.layers = layers - self.inputs = inputs - self.outputs = outputs - self.id = id - self.dim_in = dim_in - self.dim_out = dim_out - self.parsed_conn = parsed_conn - self.queue = queue - self.gconf = global_conf -end - -function DAGLayerT:init(batch_size, chunk_size) - nerv.info("initing DAGLayerT %s...", self.id) - if chunk_size == nil then - chunk_size = 1 - nerv.info("(Initing DAGLayerT) chunk_size is nil, setting it to default 1\n") - end - - self.chunk_size = chunk_size - - for i, conn in ipairs(self.parsed_conn) do - local _, output_dim - local ref_from, port_from, ref_to, port_to - ref_from, port_from = unpack(conn[1]) - ref_to, port_to = unpack(conn[2]) - _, output_dim = ref_from.layer:get_dim() - local dim = 1 - if output_dim[port_from] > 0 then - dim = output_dim[port_from] - end - - for t = 1, chunk_size do - local mid = self.gconf.cumat_type(batch_size, dim) - local err_mid = mid:create() - - if ref_from.outputs[t] == nil then - ref_from.outputs[t] = {} - end - if ref_to.inputs[t] == nil then - ref_to.inputs[t] = {} - end - if ref_to.err_outputs[t] == nil then - ref_to.err_outputs[t] = {} - end - if ref_from.err_inputs[t] == nil then - ref_from.err_inputs[t] = {} - end - - ref_from.outputs[t][port_from] = mid - ref_to.inputs[t][port_to] = mid - - ref_from.err_inputs[t][port_from] = err_mid - ref_to.err_outputs[t][port_to] = err_mid - end - end - for id, ref in pairs(self.layers) do - for i = 1, ref.input_len do - if ref.inputs[1][i] == nil then --peek at time 1 - nerv.error("dangling input port %d of layer %s", i, id) - end - end - for i = 1, ref.output_len do - if ref.outputs[1][i] == nil then --peek at time 1 - nerv.error("dangling output port %d of layer %s", i, id) - end - end - -- initialize sub layers - ref.layer:init(batch_size, chunk_size) - 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 DAGLayerT:batch_resize(batch_size, chunk_size) - if chunk_size == nil then - chunk_size = 1 - end - if batch_size ~= self.gconf.batch_size - or chunk_size ~= self.gconf.chunk_size then - nerv.printf("warn: in DAGLayerT:batch_resize, the batch_size ~= gconf.batch_size, or chunk_size ~= gconf.chunk_size") - end - self.gconf.batch_size = batch_size - self.gconf.chunk_size = chunk_size - - for i, conn in ipairs(self.parsed_conn) do - local _, output_dim - local ref_from, port_from, ref_to, port_to - ref_from, port_from = unpack(conn[1]) - ref_to, port_to = unpack(conn[2]) - _, output_dim = ref_from.layer:get_dim() - - for t = 1, chunk_size do - if ref_from.outputs[t] == nil then - ref_from.outputs[t] = {} - end - if ref_to.inputs[t] == nil then - ref_to.inputs[t] = {} - end - if ref_from.err_outputs[t] == nil then - ref_from.err_outputs[t] = {} - end - if ref_from.err_inputs[t] == nil then - ref_from.err_inputs[t] = {} - end - - local mid = self.gconf.cumat_type(batch_size, dim) - local err_mid = mid:create() - - ref_from.outputs[t][port_from] = mid - ref_to.inputs[t][port_to] = mid - - ref_from.err_inputs[t][port_from] = err_mid - ref_to.err_outputs[t][port_to] = err_mid - end - end - for id, ref in pairs(self.layers) do - ref.layer:batch_resize(batch_size, chunk_size) - end - collectgarbage("collect") -end - -function DAGLayerT:set_inputs(input, t) - for i = 1, #self.dim_in do - if input[i] == nil then - nerv.error("some input is not provided"); - end - local layer = self.inputs[i][1] - local port = self.inputs[i][2] - if layer.inputs[t] == nil then - layer.inputs[t] = {} - end - layer.inputs[t][port] = input[i] - end -end - -function DAGLayerT:set_outputs(output, t) - for i = 1, #self.dim_out do - if output[i] == nil then - nerv.error("some output is not provided"); - end - local layer = self.outputs[i][1] - local port = self.outputs[i][2] - if layer.outputs[t] == nil then - layer.outputs[t] = {} - end - layer.outputs[t][port] = output[i] - end -end - -function DAGLayerT:set_err_inputs(bp_err, t) - for i = 1, #self.dim_out do - local layer = self.outputs[i][1] - local port = self.outputs[i][2] - if layer.err_inputs[t] == nil then - layer.err_inputs[t] = {} - end - layer.err_inputs[t][port] = bp_err[i] - end -end - -function DAGLayerT:set_err_outputs(next_bp_err, t) - for i = 1, #self.dim_in do - local layer = self.inputs[i][1] - local port = self.inputs[i][2] - if layer.err_outputs[t] == nil then - layer.err_outputs[t] = {} - end - layer.err_outputs[t][port] = next_bp_err[i] - end -end - -function DAGLayerT:update(bp_err, input, output, t) - if t == nil then - t = 1 - end - self:set_err_inputs(bp_err, t) - self:set_inputs(input, t) - self:set_outputs(output, t) - for id, ref in pairs(self.queue) do - ref.layer:update(ref.err_inputs[t], ref.inputs[t], ref.outputs[t], t) - end -end - -function DAGLayerT:propagate(input, output, t) - if t == nil then - t = 1 - end - self:set_inputs(input, t) - self:set_outputs(output, t) - local ret = false - for i = 1, #self.queue do - local ref = self.queue[i] - --print("debug DAGLAyerT:propagate", ref.id, t) - ret = ref.layer:propagate(ref.inputs[t], ref.outputs[t], t) - end - return ret -end - -function DAGLayerT:back_propagate(bp_err, next_bp_err, input, output, t) - if t == nil then - t = 1 - end - self:set_err_outputs(next_bp_err, t) - self:set_err_inputs(bp_err, t) - self:set_inputs(input, t) - self:set_outputs(output, t) - for i = #self.queue, 1, -1 do - local ref = self.queue[i] - ref.layer:back_propagate(ref.err_inputs[t], ref.err_outputs[t], ref.inputs[t], ref.outputs[t], t) - end -end - -function DAGLayerT:get_params() - local param_repos = {} - for id, ref in pairs(self.queue) do - table.insert(param_repos, ref.layer:get_params()) - end - return nerv.ParamRepo.merge(param_repos) -end - -DAGLayerT.PORT_TYPES = { - INPUT = {}, - OUTPUT = {}, - ERR_INPUT = {}, - ERR_OUTPUT = {} -} - -function DAGLayerT:get_intermediate(id, port_type) - if id == "<input>" or id == "<output>" then - nerv.error("an actual real layer id is expected") - end - local layer = self.layers[id] - if layer == nil then - nerv.error("layer id %s not found", id) - end - if port_type == DAGLayerT.PORT_TYPES.INPUT then - return layer.inputs - elseif port_type == DAGLayerT.PORT_TYPES.OUTPUT then - return layer.outputs - elseif port_type == DAGLayerT.PORT_TYPES.ERR_INPUT then - return layer.err_inputs - elseif port_type == DAGLayerT.PORT_TYPES.ERR_OUTPUT then - return layer.err_outputs - end - nerv.error("unrecognized port type") -end diff --git a/nerv/tnn/layersT/dropout_t.lua b/nerv/tnn/layersT/dropout_t.lua deleted file mode 100644 index 4351285..0000000 --- a/nerv/tnn/layersT/dropout_t.lua +++ /dev/null @@ -1,71 +0,0 @@ -local Dropout = nerv.class("nerv.DropoutLayerT", "nerv.LayerT") - -function Dropout:__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 - self:check_dim_len(1, 1) -- two inputs: nn output and label -end - -function Dropout:init(batch_size, chunk_size) - if self.dim_in[1] ~= self.dim_out[1] then - nerv.error("mismatching dimensions of input and output") - end - if chunk_size == nil then - chunk_size = 1 - end - self.mask_t = {} - for t = 1, chunk_size do - self.mask_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1]) - end -end - -function Dropout:batch_resize(batch_size, chunk_size) - if chunk_size == nil then - chunk_size = 1 - end - for t = 1, chunk_size do - if self.mask_t[t] == nil or self.mask_t[t]:nrow() ~= batch_size then - self.mask_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1]) - end - end -end - -function Dropout:propagate(input, output, t) - if t == nil then - t = 1 - end - if self.gconf.dropout_rate == nil then - nerv.info("DropoutLayerT:propagate warning, global_conf.dropout_rate is nil, setting it zero") - self.gconf.dropout_rate = 0 - end - - if self.gconf.dropout_rate == 0 then - output[1]:copy_fromd(input[1]) - else - self.mask_t[t]:rand_uniform() - --since we will lose a portion of the actvations, we multiply the activations by 1/(1-dr) to compensate - self.mask_t[t]:thres_mask(self.mask_t[t], self.gconf.dropout_rate, 0, 1 / (1.0 - self.gconf.dropout_rate)) - output[1]:mul_elem(input[1], self.mask_t[t]) - end -end - -function Dropout:update(bp_err, input, output, t) - -- no params, therefore do nothing -end - -function Dropout:back_propagate(bp_err, next_bp_err, input, output, t) - if t == nil then - t = 1 - end - if self.gconf.dropout_rate == 0 then - next_bp_err[1]:copy_fromd(bp_err[1]) - else - next_bp_err[1]:mul_elem(bp_err[1], self.mask_t[t]) - end -end - -function Dropout:get_params() - return nerv.ParamRepo({}) -end diff --git a/nerv/tnn/layersT/gru_t.lua b/nerv/tnn/layersT/gru_t.lua deleted file mode 100644 index 8f15cc8..0000000 --- a/nerv/tnn/layersT/gru_t.lua +++ /dev/null @@ -1,114 +0,0 @@ -local GRULayerT = nerv.class('nerv.GRULayerT', 'nerv.LayerT') - -function GRULayerT:__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 - - 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]) - end - - --prepare a DAGLayerT to hold the lstm structure - local pr = layer_conf.pr - if pr == nil then - pr = nerv.ParamRepo() - end - - local function ap(str) - return self.id .. '.' .. str - end - - local layers = { - ["nerv.CombinerLayer"] = { - [ap("inputXDup")] = {{}, {["dim_in"] = {self.dim_in[1]}, - ["dim_out"] = {self.dim_in[1], self.dim_in[1], self.dim_in[1]}, ["lambda"] = {1}}}, - [ap("inputHDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, - ["dim_out"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}}, - [ap("updateGDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, - ["dim_out"] = {self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}}, - [ap("updateMergeL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_out[1]}, - ["lambda"] = {1, -1, 1}}}, - }, - ["nerv.AffineLayer"] = { - [ap("mainAffineL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, ["dim_out"] = {self.dim_out[1]}, ["pr"] = pr}}, - }, - ["nerv.TanhLayer"] = { - [ap("mainTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}}, - }, - ["nerv.GateFLayer"] = { - [ap("resetGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, - ["dim_out"] = {self.dim_in[2]}, ["pr"] = pr}}, - [ap("updateGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, - ["dim_out"] = {self.dim_in[2]}, ["pr"] = pr}}, - }, - ["nerv.ElemMulLayer"] = { - [ap("resetGMulL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_in[2]}}}, - [ap("updateGMulCL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_in[2]}}}, - [ap("updateGMulHL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_in[2]}}}, - }, - } - - local layerRepo = nerv.LayerRepo(layers, pr, global_conf) - - local connections_t = { - ["<input>[1]"] = ap("inputXDup[1]"), - ["<input>[2]"] = ap("inputHDup[1]"), - - [ap("inputXDup[1]")] = ap("resetGateL[1]"), - [ap("inputHDup[1]")] = ap("resetGateL[2]"), - [ap("inputXDup[2]")] = ap("updateGateL[1]"), - [ap("inputHDup[2]")] = ap("updateGateL[2]"), - [ap("updateGateL[1]")] = ap("updateGDup[1]"), - - [ap("resetGateL[1]")] = ap("resetGMulL[1]"), - [ap("inputHDup[3]")] = ap("resetGMulL[2]"), - - [ap("inputXDup[3]")] = ap("mainAffineL[1]"), - [ap("resetGMulL[1]")] = ap("mainAffineL[2]"), - [ap("mainAffineL[1]")] = ap("mainTanhL[1]"), - - [ap("updateGDup[1]")] = ap("updateGMulHL[1]"), - [ap("inputHDup[4]")] = ap("updateGMulHL[2]"), - [ap("updateGDup[2]")] = ap("updateGMulCL[1]"), - [ap("mainTanhL[1]")] = ap("updateGMulCL[2]"), - - [ap("inputHDup[5]")] = ap("updateMergeL[1]"), - [ap("updateGMulHL[1]")] = ap("updateMergeL[2]"), - [ap("updateGMulCL[1]")] = ap("updateMergeL[3]"), - - [ap("updateMergeL[1]")] = "<output>[1]", - } - - self.dagL = nerv.DAGLayerT(self.id, global_conf, - {["dim_in"] = self.dim_in, ["dim_out"] = self.dim_out, ["sub_layers"] = layerRepo, - ["connections"] = connections_t}) - - self:check_dim_len(2, 1) -- x, h and h -end - -function GRULayerT:init(batch_size, chunk_size) - self.dagL:init(batch_size, chunk_size) -end - -function GRULayerT:batch_resize(batch_size, chunk_size) - self.dagL:batch_resize(batch_size, chunk_size) -end - -function GRULayerT:update(bp_err, input, output, t) - self.dagL:update(bp_err, input, output, t) -end - -function GRULayerT:propagate(input, output, t) - self.dagL:propagate(input, output, t) -end - -function GRULayerT:back_propagate(bp_err, next_bp_err, input, output, t) - self.dagL:back_propagate(bp_err, next_bp_err, input, output, t) -end - -function GRULayerT:get_params() - return self.dagL:get_params() -end diff --git a/nerv/tnn/layersT/lstm_t.lua b/nerv/tnn/layersT/lstm_t.lua deleted file mode 100644 index 04d0600..0000000 --- a/nerv/tnn/layersT/lstm_t.lua +++ /dev/null @@ -1,124 +0,0 @@ -local LSTMLayerT = nerv.class('nerv.LSTMLayerT', 'nerv.LayerT') - -function LSTMLayerT:__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 - - --prepare a DAGLayerT to hold the lstm structure - local pr = layer_conf.pr - if pr == nil then - pr = nerv.ParamRepo() - end - - local function ap(str) - return self.id .. '.' .. str - end - - local layers = { - ["nerv.CombinerLayer"] = { - [ap("inputXDup")] = {{}, {["dim_in"] = {self.dim_in[1]}, - ["dim_out"] = {self.dim_in[1], self.dim_in[1], self.dim_in[1], self.dim_in[1]}, ["lambda"] = {1}}}, - [ap("inputHDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, - ["dim_out"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}}, - [ap("inputCDup")] = {{}, {["dim_in"] = {self.dim_in[3]}, - ["dim_out"] = {self.dim_in[3], self.dim_in[3], self.dim_in[3]}, ["lambda"] = {1}}}, - [ap("mainCDup")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3], self.dim_in[3], self.dim_in[3]}, - ["lambda"] = {1, 1}}}, - }, - ["nerv.AffineLayer"] = { - [ap("mainAffineL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, - ["dim_out"] = {self.dim_out[1]}, ["pr"] = pr}}, - }, - ["nerv.TanhLayer"] = { - [ap("mainTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}}, - [ap("outputTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}}, - }, - ["nerv.GateFLayer"] = { - [ap("forgetGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2], self.dim_in[3]}, - ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}}, - [ap("inputGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2], self.dim_in[3]}, - ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}}, - [ap("outputGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2], self.dim_in[3]}, - ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}}, - - }, - ["nerv.ElemMulLayer"] = { - [ap("inputGMulL")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3]}}}, - [ap("forgetGMulL")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3]}}}, - [ap("outputGMulL")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3]}}}, - }, - } - - local layerRepo = nerv.LayerRepo(layers, pr, global_conf) - - local connections_t = { - ["<input>[1]"] = ap("inputXDup[1]"), - ["<input>[2]"] = ap("inputHDup[1]"), - ["<input>[3]"] = ap("inputCDup[1]"), - - [ap("inputXDup[1]")] = ap("mainAffineL[1]"), - [ap("inputHDup[1]")] = ap("mainAffineL[2]"), - [ap("mainAffineL[1]")] = ap("mainTanhL[1]"), - - [ap("inputXDup[2]")] = ap("inputGateL[1]"), - [ap("inputHDup[2]")] = ap("inputGateL[2]"), - [ap("inputCDup[1]")] = ap("inputGateL[3]"), - - [ap("inputXDup[3]")] = ap("forgetGateL[1]"), - [ap("inputHDup[3]")] = ap("forgetGateL[2]"), - [ap("inputCDup[2]")] = ap("forgetGateL[3]"), - - [ap("mainTanhL[1]")] = ap("inputGMulL[1]"), - [ap("inputGateL[1]")] = ap("inputGMulL[2]"), - - [ap("inputCDup[3]")] = ap("forgetGMulL[1]"), - [ap("forgetGateL[1]")] = ap("forgetGMulL[2]"), - - [ap("inputGMulL[1]")] = ap("mainCDup[1]"), - [ap("forgetGMulL[1]")] = ap("mainCDup[2]"), - - [ap("inputXDup[4]")] = ap("outputGateL[1]"), - [ap("inputHDup[4]")] = ap("outputGateL[2]"), - [ap("mainCDup[3]")] = ap("outputGateL[3]"), - - [ap("mainCDup[2]")] = "<output>[2]", - [ap("mainCDup[1]")] = ap("outputTanhL[1]"), - - [ap("outputTanhL[1]")] = ap("outputGMulL[1]"), - [ap("outputGateL[1]")] = ap("outputGMulL[2]"), - - [ap("outputGMulL[1]")] = "<output>[1]", - } - self.dagL = nerv.DAGLayerT(self.id, global_conf, - {["dim_in"] = self.dim_in, ["dim_out"] = self.dim_out, ["sub_layers"] = layerRepo, - ["connections"] = connections_t}) - - self:check_dim_len(3, 2) -- x, h, c and h, c -end - -function LSTMLayerT:init(batch_size, chunk_size) - self.dagL:init(batch_size, chunk_size) -end - -function LSTMLayerT:batch_resize(batch_size, chunk_size) - self.dagL:batch_resize(batch_size, chunk_size) -end - -function LSTMLayerT:update(bp_err, input, output, t) - self.dagL:update(bp_err, input, output, t) -end - -function LSTMLayerT:propagate(input, output, t) - self.dagL:propagate(input, output, t) -end - -function LSTMLayerT:back_propagate(bp_err, next_bp_err, input, output, t) - self.dagL:back_propagate(bp_err, next_bp_err, input, output, t) -end - -function LSTMLayerT:get_params() - return self.dagL:get_params() -end diff --git a/nerv/tnn/layersT/softmax_ce_t.lua b/nerv/tnn/layersT/softmax_ce_t.lua deleted file mode 100644 index a9ce975..0000000 --- a/nerv/tnn/layersT/softmax_ce_t.lua +++ /dev/null @@ -1,93 +0,0 @@ -local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayerT", "nerv.LayerT") - -function SoftmaxCELayer:__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 - self.compressed = layer_conf.compressed - if self.compressed == nil then - self.compressed = false - end - self:check_dim_len(2, -1) -- two inputs: nn output and label -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") - end - if chunk_size == nil then - chunk_size = 1 - end - self.total_ce = 0.0 - self.total_correct = 0 - self.total_frames = 0 - self.softmax_t = {} - self.ce_t = {} - for t = 1, chunk_size do - self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1]) - self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1]) - end -end - -function SoftmaxCELayer:batch_resize(batch_size, chunk_size) - if chunk_size == nil then - chunk_size = 1 - end - for t = 1, chunk_size do - if self.softmax_t[t]:nrow() ~= batch_size then - self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1]) - self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1]) - end - end -end - -function SoftmaxCELayer:update(bp_err, input, output, t) - -- no params, therefore do nothing -end - -function SoftmaxCELayer:propagate(input, output, t) - if t == nil then - t = 1 - end - local softmax = self.softmax_t[t] - local ce = self.ce_t[t] - local classified = softmax:softmax(input[1]) - local label = input[2] - ce:log_elem(softmax) - if self.compressed then - label = label:decompress(input[1]:ncol()) - end - ce:mul_elem(ce, label) - ce = ce:rowsum() - if output[1] ~= nil then - output[1]:copy_fromd(ce) - end - -- add total ce - self.total_ce = self.total_ce - ce:colsum()[0][0] - self.total_frames = self.total_frames + softmax:nrow() - -- TODO: add colsame for uncompressed label - if self.compressed then - self.total_correct = self.total_correct + classified:colsame(input[2])[0][0] - end -end - -function SoftmaxCELayer:back_propagate(bp_err, next_bp_err, input, output, t) - -- softmax output - label - if t == nil then - t = 1 - end - local label = input[2] - if self.compressed then - label = label:decompress(input[1]:ncol()) - end - local nbe = next_bp_err[1] - nbe:add(self.softmax_t[t], label, 1.0, -1.0) - if bp_err[1] ~= nil then - nbe:scale_rows_by_col(bp_err[1]) - end -end - -function SoftmaxCELayer:get_params() - return nerv.ParamRepo({}) -end |