major change: use luarocks to manage project

1 files changed, 0 insertions, 106 deletions

diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
deleted file mode 100644
index a5727be..0000000
--- a/examples/asr_trainer.lua
+++ /dev/null
@@ -1,106 +0,0 @@
-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