change concept of ParamRepo; provide generalized param update; code clean-up; #25 #26 #27 #29

1 files changed, 25 insertions, 17 deletions

diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
index 05d770f..a5727be 100644
--- a/examples/asr_trainer.lua
+++ b/examples/asr_trainer.lua
@@ -1,50 +1,58 @@
 function build_trainer(ifname)
-    local param_repo = make_param_repo(ifname)
+    local param_repo = nerv.ParamRepo()
+    param_repo:import(ifname, nil, gconf)
     local sublayer_repo = make_sublayer_repo(param_repo)
     local layer_repo = make_layer_repo(sublayer_repo, param_repo)
     local crit = get_criterion_layer(sublayer_repo)
     local network = get_network(layer_repo)
+    local input_order = get_input_order()
     local iterative_trainer = function (prefix, scp_file, bp)
         gconf.randomize = bp
         -- build buffer
-        local buffer = make_buffer(make_reader(scp_file, layer_repo))
+        local buffer = make_buffer(make_readers(scp_file, layer_repo))
         -- initialize the network
         network:init(gconf.batch_size)
         gconf.cnt = 0
+        err_input = {nerv.CuMatrixFloat(256, 1)}
+        err_input[1]:fill(1)
         for data in buffer.get_data, buffer do
             -- prine stat periodically
             gconf.cnt = gconf.cnt + 1
             if gconf.cnt == 1000 then
-                print_stat(crit)
+                print_stat(sublayer_repo)
+                nerv.CuMatrix.print_profile()
+                nerv.CuMatrix.clear_profile()
                 gconf.cnt = 0
+                -- break
             end
+            local input = {}
 --            if gconf.cnt == 100 then break end
-
-            input = {data.main_scp, data.phone_state}
-            output = {}
-            err_input = {}
+            for i, id in ipairs(input_order) do
+                if data[id] == nil then
+                    nerv.error("input data %s not found", id)
+                end
+                table.insert(input, data[id])
+            end
+            local output = {nerv.CuMatrixFloat(256, 1)}
             err_output = {input[1]:create()}
             network:propagate(input, output)
             if bp then
-                network:back_propagate(err_output, err_input, input, output)
+                network:back_propagate(err_input, err_output, input, output)
                 network:update(err_input, input, output)
             end
             -- collect garbage in-time to save GPU memory
             collectgarbage("collect")
         end
-        print_stat(crit)
+        print_stat(sublayer_repo)
         nerv.CuMatrix.print_profile()
+        nerv.CuMatrix.clear_profile()
         if (not bp) and prefix ~= nil then
             nerv.info("writing back...")
             local fname = string.format("%s_cv%.3f.nerv",
-                            prefix, get_accuracy(crit))
-            cf = nerv.ChunkFile(fname, "w")
-            for i, p in ipairs(network:get_params()) do
-                cf:write_chunk(p)
-            end
-            cf:close()
+                            prefix, get_accuracy(sublayer_repo))
+            network:get_params():export(fname, nil)
         end
-        return get_accuracy(crit)
+        return get_accuracy(sublayer_repo)
     end
     return iterative_trainer
 end
@@ -73,7 +81,7 @@ for i = 1, max_iter do
     local accu_new = trainer(
                         string.format("%s_%s_iter_%d_lr%f_tr%.3f",
                             string.gsub(
-                                (string.gsub(pf0, "(.*/)(.*)", "%2")),
+                                (string.gsub(pf0[1], "(.*/)(.*)", "%2")),
                                 "(.*)%..*", "%1"),
                             os.date("%Y%m%d%H%M%S"),
                             i, gconf.lrate,