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| author | txh18 <cloudygooseg@gmail.com> | 2015-11-11 20:24:34 +0800 |
|---|---|---|
| committer | txh18 <cloudygooseg@gmail.com> | 2015-11-11 20:24:34 +0800 |
| commit | 73402335834c990dbe6a7729ace7a830ed2f91ae (patch) | |
| tree | d53384e6da7efbdb635030603aab7ab35a78b006 | |
| parent | 5761e68ec1b73ed867443fb2687739395f22f2f9 (diff) | |
added a little debug info in reader
| -rw-r--r-- | nerv/examples/lmptb/lmptb/lmseqreader.lua | 4 | ||||
| -rw-r--r-- | nerv/examples/lmptb/m-tests/tnn_test.lua | 8 |
2 files changed, 8 insertions, 4 deletions
diff --git a/nerv/examples/lmptb/lmptb/lmseqreader.lua b/nerv/examples/lmptb/lmptb/lmseqreader.lua index d75167e..e0dcd95 100644 --- a/nerv/examples/lmptb/lmptb/lmseqreader.lua +++ b/nerv/examples/lmptb/lmptb/lmseqreader.lua @@ -24,6 +24,7 @@ function LMReader:open_file(fn) nerv.error("%s error: in open_file(fn is %s), file handle not nil.", self.log_pre, fn) end printf("%s opening file %s...\n", self.log_pre, fn) + print("batch_size:", self.batch_size, "chunk_size", self.chunk_size) self.fh = io.open(fn, "r") self.streams = {} for i = 1, self.batch_size, 1 do @@ -102,6 +103,9 @@ function LMReader:get_batch(feeds) labels_s[j][i] = st.store[st.head + 1] inputs_m[j][2][i - 1][self.vocab:get_word_str(st.store[st.head + 1]).id - 1] = 1 else + if (inputs_s[j][i] ~= self.vocab.null_token) then + nerv.error("reader error : input not null but label is null_token") + end labels_s[j][i] = self.vocab.null_token end if (inputs_s[j][i] ~= self.vocab.null_token) then diff --git a/nerv/examples/lmptb/m-tests/tnn_test.lua b/nerv/examples/lmptb/m-tests/tnn_test.lua index a2c38f0..c4890b6 100644 --- a/nerv/examples/lmptb/m-tests/tnn_test.lua +++ b/nerv/examples/lmptb/m-tests/tnn_test.lua @@ -238,15 +238,15 @@ valid_fn = data_dir .. '/ptb.valid.txt.adds' test_fn = data_dir .. '/ptb.test.txt.adds' global_conf = { - lrate = 0.1, wcost = 1e-6, momentum = 0, + lrate = 1, wcost = 1e-6, momentum = 0, cumat_type = nerv.CuMatrixFloat, mmat_type = nerv.MMatrixFloat, nn_act_default = 0, hidden_size = 200, - chunk_size = 15, - batch_size = 1, - max_iter = 25, + chunk_size = 5, + batch_size = 10, + max_iter = 20, param_random = function() return (math.random() / 5 - 0.1) end, train_fn = train_fn, |