major change: use luarocks to manage project

1 files changed, 106 insertions, 0 deletions

diff --git a/nerv/examples/asr_trainer.lua b/nerv/examples/asr_trainer.lua
new file mode 100644
index 0000000..a5727be
--- /dev/null
+++ b/nerv/examples/asr_trainer.lua
@@ -0,0 +1,106 @@
+function build_trainer(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_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(sublayer_repo)
+                nerv.CuMatrix.print_profile()
+                nerv.CuMatrix.clear_profile()
+                gconf.cnt = 0
+                -- break
+            end
+            local input = {}
+--            if gconf.cnt == 100 then break end
+            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_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(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(sublayer_repo))
+            network:get_params():export(fname, nil)
+        end
+        return get_accuracy(sublayer_repo)
+    end
+    return iterative_trainer
+end
+
+dofile(arg[1])
+start_halving_inc = 0.5
+halving_factor = 0.6
+end_halving_inc = 0.1
+min_iter = 1
+max_iter = 20
+min_halving = 5
+gconf.batch_size = 256
+gconf.buffer_size = 81920
+
+local pf0 = gconf.initialized_param
+local trainer = build_trainer(pf0)
+--local trainer = build_trainer("c3.nerv")
+local accu_best = trainer(nil, gconf.cv_scp, false)
+local do_halving = false
+
+nerv.info("initial cross validation: %.3f", accu_best)
+for i = 1, max_iter do
+    nerv.info("[NN] begin iteration %d with lrate = %.6f", i, gconf.lrate)
+    local accu_tr = trainer(nil, gconf.tr_scp, true)
+    nerv.info("[TR] training set %d: %.3f", i, accu_tr)
+    local accu_new = trainer(
+                        string.format("%s_%s_iter_%d_lr%f_tr%.3f",
+                            string.gsub(
+                                (string.gsub(pf0[1], "(.*/)(.*)", "%2")),
+                                "(.*)%..*", "%1"),
+                            os.date("%Y%m%d%H%M%S"),
+                            i, gconf.lrate,
+                            accu_tr),
+                        gconf.cv_scp, false)
+    nerv.info("[CV] cross validation %d: %.3f", i, accu_new)
+    -- TODO: revert the weights
+    local accu_diff = accu_new - accu_best
+    if do_halving and accu_diff < end_halving_inc and i > min_iter then
+        break
+    end
+    if accu_diff < start_halving_inc and i >= min_halving then
+        do_halving = true
+    end
+    if do_halving then
+        gconf.lrate = gconf.lrate * halving_factor
+    end
+    if accu_new > accu_best then
+        accu_best = accu_new
+    end
+--    nerv.Matrix.print_profile()
+end