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-rw-r--r--nerv/examples/lmptb/unfold_ptb_main.lua10
1 files changed, 5 insertions, 5 deletions
diff --git a/nerv/examples/lmptb/unfold_ptb_main.lua b/nerv/examples/lmptb/unfold_ptb_main.lua
index 5affadf..eebab2b 100644
--- a/nerv/examples/lmptb/unfold_ptb_main.lua
+++ b/nerv/examples/lmptb/unfold_ptb_main.lua
@@ -174,7 +174,7 @@ function propagateFile(global_conf, dagL, fn, config)
local dagL_input = {}
for i = 1, global_conf.bptt + 1 do
- dagL_input[i] = global_conf.cumat_type(1, global_conf.batch_size) --changed to row vector, debughtx
+ dagL_input[i] = global_conf.cumat_type(global_conf.batch_size, 1) --changed to row vector, debughtx
end
dagL_input[global_conf.bptt + 2] = global_conf.cumat_type(global_conf.batch_size, global_conf.hidden_size)
dagL_input[global_conf.bptt + 3] = global_conf.cumat_type(global_conf.batch_size, global_conf.vocab:size())
@@ -210,7 +210,7 @@ function propagateFile(global_conf, dagL, fn, config)
global_conf.timer:toc("dagL-propagate")
hidden_store[tnow] = global_conf.cumat_type(global_conf.batch_size, global_conf.hidden_size)
- hidden_store[tnow]:copy_fromd(sigmoidL_ref.outputs[1])
+ hidden_store[tnow]:copy_fromd(sigmoidL_ref.outputs[1][1])
if (config.do_train == true) then
global_conf.timer:tic("dagL-back_propagate")
@@ -280,7 +280,7 @@ if (set == "ptb") then
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.MMatrixFloat,
- hidden_size = 200,
+ hidden_size = 50,
batch_size = 10,
bptt = 6, --train bptt_block's words. could be set to zero
max_iter = 18,
@@ -291,7 +291,7 @@ if (set == "ptb") then
valid_fn = valid_fn,
test_fn = test_fn,
sche_log_pre = "[SCHEDULER]:",
- log_w_num = 100000, --give a message when log_w_num words have been processed
+ log_w_num = 1000, --give a message when log_w_num words have been processed
timer = nerv.Timer()
}
global_conf.work_dir = work_dir_base.."/h"..global_conf.hidden_size.."bp"..global_conf.bptt.."slr"..global_conf.lrate --..os.date("_%bD%dH%H") --comment this for testing
@@ -304,7 +304,7 @@ elseif (set == "test") then
global_conf = {
lrate = 0.1, wcost = 1e-6, momentum = 0,
cumat_type = nerv.CuMatrixFloat,
- mmat_type = nerv.CuMatrixFloat,
+ mmat_type = nerv.MMatrixFloat,
hidden_size = 5,
batch_size = 1,