diff options
| -rw-r--r-- | Makefile | 3 | ||||
| -rw-r--r-- | examples/test_dnn_layers.lua | 34 | ||||
| -rw-r--r-- | layer/affine.lua | 31 | ||||
| -rw-r--r-- | layer/bias.lua | 24 | ||||
| -rw-r--r-- | layer/init.lua | 13 | ||||
| -rw-r--r-- | layer/sigmoid.lua | 12 | ||||
| -rw-r--r-- | layer/softmax_ce.lua | 16 | ||||
| -rw-r--r-- | layer/window.lua | 24 | ||||
| m--------- | speech | 0 |
9 files changed, 124 insertions, 33 deletions
@@ -7,7 +7,8 @@ OBJS := nerv.o luaT.o common.o \ LIBS := libnerv.so LUA_LIBS := matrix/init.lua io/init.lua nerv.lua \ pl/utils.lua pl/compat.lua \ - layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua + layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua \ + layer/window.lua layer/bias.lua INCLUDE := -I build/luajit-2.0/include/luajit-2.0/ -DLUA_USE_APICHECK CUDA_BASE := /usr/local/cuda-6.5 CUDA_INCLUDE := -I $(CUDA_BASE)/include/ diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua index 866e685..9be9d71 100644 --- a/examples/test_dnn_layers.lua +++ b/examples/test_dnn_layers.lua @@ -11,10 +11,14 @@ bp = pf:read_chunk("b", global_conf) -- print(bp.trans) -af = nerv.AffineLayer("test", global_conf, ltp, bp) -sg = nerv.SigmoidLayer("test2", global_conf) -sm = nerv.SoftmaxCELayer("test3", global_conf) - +af = nerv.AffineLayer("test", global_conf, {["ltp"] = ltp, + ["bp"] = bp, + dim_in = {429}, + dim_out = {2048}}) +sg = nerv.SigmoidLayer("test2", global_conf, {dim_in = {2048}, + dim_out = {2048}}) +sm = nerv.SoftmaxCELayer("test3", global_conf, {dim_in = {2048, 2048}, + dim_out = {}}) af:init() sg:init() sm:init() @@ -27,18 +31,18 @@ for i = 0, 9 do label[i][i] = 1.0 end -input1 = {[0] = df:read_chunk("input", global_conf).trans} -output1 = {[0] = nerv.CuMatrixFloat(10, 2048)} +input1 = {df:read_chunk("input", global_conf).trans} +output1 = {nerv.CuMatrixFloat(10, 2048)} input2 = output1 -output2 = {[0] = nerv.CuMatrixFloat(10, 2048)} -input3 = {[0] = output2[0], [1] = label} +output2 = {nerv.CuMatrixFloat(10, 2048)} +input3 = {output2[1], label} output3 = nil err_input1 = nil -err_output1 = {[0] = nerv.CuMatrixFloat(10, 2048)} +err_output1 = {nerv.CuMatrixFloat(10, 2048)} err_input2 = err_output1 -err_output2 = {[0] = nerv.CuMatrixFloat(10, 2048)} +err_output2 = {nerv.CuMatrixFloat(10, 2048)} err_input3 = err_output2 -err_output3 = {[0] = input1[0]:create()} +err_output3 = {input1[1]:create()} for i = 0, 3 do -- propagate @@ -59,13 +63,13 @@ for i = 0, 3 do print("output1") - print(output1[0]) + print(output1[1]) print("output2") - print(output2[0]) + print(output2[1]) print("err_output1") - print(err_output1[0]) + print(err_output1[1]) print("err_output2") - print(err_output2[0]) + print(err_output2[1]) nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce) nerv.utils.printf("frames: %.8f\n", sm.total_frames) end diff --git a/layer/affine.lua b/layer/affine.lua index 573b98d..90a1d16 100644 --- a/layer/affine.lua +++ b/layer/affine.lua @@ -12,14 +12,27 @@ function MatrixParam:write(pfhandle) self.trans:new_to_host():save(pfhandle) end -function AffineLayer:__init(id, global_conf, ltp, bp) +function AffineLayer:__init(id, global_conf, layer_conf) self.id = id - self.ltp = ltp - self.bp = bp + self.ltp = layer_conf.ltp + self.bp = layer_conf.bp + self.dim_in = layer_conf.dim_in + self.dim_out = layer_conf.dim_out self.gconf = global_conf + self:check_dim_len(1, 1) -- exactly one input and one output end function AffineLayer:init() + if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then + nerv.error("mismatching dimensions of linear transform and bias paramter") + end + if self.dim_in[1] ~= self.ltp.trans:nrow() then + nerv.error("mismatching dimensions of linear transform parameter and input") + end + if self.dim_out[1] ~= self.ltp.trans:ncol() then + nerv.error("mismatching dimensions of linear transform parameter and output") + end + -- linear transform correction self.ltc = self.ltp.trans:create() self.ltc:fill(0) @@ -36,10 +49,10 @@ function nerv.AffineLayer:update(bp_err, input, output) local gconf = self.gconf -- momentum gain local mmt_gain = 1.0 / (1.0 - gconf.momentum); - local n = input[0]:nrow() * mmt_gain + local n = input[1]:nrow() * mmt_gain -- update corrections (accumulated errors) - ltc:mul(input[0], bp_err[0], 1.0, gconf.momentum, 'T', 'N') - bc:add(bc, bp_err[0]:colsum(), gconf.momentum, 1.0) + ltc:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N') + bc:add(bc, bp_err[1]:colsum(), gconf.momentum, 1.0) -- perform update ltp:add(ltp, ltc, 1.0, -gconf.lrate / n) bp:add(bp, bc, 1.0, -gconf.lrate / n) @@ -49,11 +62,11 @@ end function nerv.AffineLayer:propagate(input, output) -- apply linear transform - output[0]:mul(input[0], self.ltp.trans, 1.0, 0.0, 'N', 'N') + output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N') -- add bias - output[0]:add_row(self.bp.trans, 1.0) + output[1]:add_row(self.bp.trans, 1.0) end function nerv.AffineLayer:back_propagate(next_bp_err, bp_err, input, output) - next_bp_err[0]:mul(bp_err[0], self.ltp.trans, 1.0, 0.0, 'N', 'T') + next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T') end diff --git a/layer/bias.lua b/layer/bias.lua new file mode 100644 index 0000000..6ddfe11 --- /dev/null +++ b/layer/bias.lua @@ -0,0 +1,24 @@ +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 + self:check_dim_len(1, 1) +end + +function BiasLayer:init() + if self.dim_in[1] ~= self.bias.trans:ncol() then + nerv.error("mismatching dimensions of input and bias parameter") + end + if self.dim_out[1] ~= self.bias.trans:ncol() then + nerv.error("mismatching dimensions of output and bias parameter") + end +end + +function BiasLayer:propagate(input, output) + output[1]:copy_fromd(input[1]) + output[1]:add_row(self.bias.trans, 1.0) +end diff --git a/layer/init.lua b/layer/init.lua index a98621d..4881cb7 100644 --- a/layer/init.lua +++ b/layer/init.lua @@ -44,3 +44,16 @@ end function nerv.Layer:back_propagate(next_bp_err, bp_err, input, output) nerv.error_method_not_implemented() end + +function nerv.Layer:check_dim_len(len_in, len_out) + local expected_in = table.getn(self.dim_in) + local expected_out = table.getn(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 diff --git a/layer/sigmoid.lua b/layer/sigmoid.lua index ca34419..220b7af 100644 --- a/layer/sigmoid.lua +++ b/layer/sigmoid.lua @@ -1,11 +1,17 @@ local SigmoidLayer = nerv.class("nerv.SigmoidLayer", "nerv.Layer") -function SigmoidLayer:__init(id, global_conf) +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 + self:check_dim_len(1, 1) end function SigmoidLayer:init() + if self.dim_in[1] ~= self.dim_out[1] then + nerv.error("mismatching dimensions of input and output") + end end function SigmoidLayer:update(bp_err, input, output) @@ -13,9 +19,9 @@ function SigmoidLayer:update(bp_err, input, output) end function SigmoidLayer:propagate(input, output) - output[0]:sigmoid(input[0]) + output[1]:sigmoid(input[1]) end function SigmoidLayer:back_propagate(next_bp_err, bp_err, input, output) - next_bp_err[0]:sigmoid_grad(bp_err[0], output[0]) + next_bp_err[1]:sigmoid_grad(bp_err[1], output[1]) end diff --git a/layer/softmax_ce.lua b/layer/softmax_ce.lua index 37d2864..3dfebc5 100644 --- a/layer/softmax_ce.lua +++ b/layer/softmax_ce.lua @@ -1,11 +1,17 @@ local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayer", "nerv.Layer") -function SoftmaxCELayer:__init(id, global_conf) +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:check_dim_len(2, -1) -- two inputs: nn output and label end function SoftmaxCELayer:init() + if self.dim_in[1] ~= self.dim_in[1] then + nerv.error("mismatching dimensions of previous network output and labels") + end self.total_ce = 0.0 self.total_frames = 0 end @@ -15,12 +21,12 @@ function SoftmaxCELayer:update(bp_err, input, output) end function SoftmaxCELayer:propagate(input, output) - local soutput = input[0]:create() -- temporary value for calc softmax + local soutput = input[1]:create() -- temporary value for calc softmax self.soutput = soutput - soutput:softmax(input[0]) + soutput:softmax(input[1]) local ce = soutput:create() ce:log_elem(soutput) - ce:mul_elem(ce, input[1]) + ce:mul_elem(ce, input[2]) -- add total ce self.total_ce = self.total_ce - ce:rowsum():colsum()[0] self.total_frames = self.total_frames + soutput:nrow() @@ -28,5 +34,5 @@ end function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output) -- softmax output - label - next_bp_err[0]:add(self.soutput, input[1], 1.0, -1.0) + next_bp_err[1]:add(self.soutput, input[1], 1.0, -1.0) end diff --git a/layer/window.lua b/layer/window.lua new file mode 100644 index 0000000..8e9e761 --- /dev/null +++ b/layer/window.lua @@ -0,0 +1,24 @@ +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 + self:check_dim_len(1, 1) +end + +function WindowLayer:init() + if self.dim_in[1] ~= self.window.trans:ncol() then + nerv.error("mismatching dimensions of input and window parameter") + end + if self.dim_out[1] ~= self.window.trans:ncol() then + nerv.error("mismatching dimensions of output and window parameter") + end +end + +function WindowLayer:propagate(input, output) + output[1]:copy_fromd(input[1]) + output[1]:scale_row(self.window.trans) +end diff --git a/speech b/speech -Subproject d8ea67ee420c2fc73085da04de86df023acd98d +Subproject 821aec314824b89e9fe9c3ee467793a05ed89ee |