diff options
Diffstat (limited to 'layer')
| -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 |
6 files changed, 103 insertions, 17 deletions
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 |