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Comparison of Chainer's memnn example between in FP32 mode and in FP16 mode
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$ CHAINER_DTYPE=float16 python train_memnn.py tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_train.txt tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_test.txt -d 0 | |
Training data: tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_train.txt: 2000 | |
Test data: tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_test.txt: 200 | |
epoch main/loss validation/main/loss main/accuracy validation/main/accuracy | |
1 nan nan 0.0017004 0 | |
2 nan nan 0 0 | |
3 nan nan 0 0 | |
4 nan nan 0 0 | |
5 nan nan 0 0 | |
6 nan nan 0 0 | |
7 nan nan 0 0 | |
8 nan nan 0 0 | |
9 nan nan 0 0 | |
10 nan nan 0 0 | |
11 nan nan 0 0 | |
12 nan nan 0 0 | |
13 nan nan 0 0 | |
14 nan nan 0 0 | |
15 nan nan 0 0 | |
16 nan nan 0 0 | |
17 nan nan 0 0 | |
18 nan nan 0 0 | |
19 nan nan 0 0 | |
20 nan nan 0 0 | |
21 nan nan 0 0 | |
22 nan nan 0 0 | |
23 nan nan 0 0 | |
24 nan nan 0 0 | |
25 nan nan 0 0 | |
26 nan nan 0 0 | |
27 nan nan 0 0 | |
28 nan nan 0 0 | |
29 nan nan 0 0 | |
30 nan nan 0 0 | |
... |
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$ python train_memnn.py tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_train.txt tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_test.txt -d 0 | |
Training data: tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_train.txt: 2000 | |
Test data: tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_test.txt: 200 | |
epoch main/loss validation/main/loss main/accuracy validation/main/accuracy | |
1 2.41833 1.80678 0.2083 0.225 | |
2 1.76084 1.71291 0.2972 0.381 | |
3 1.62661 1.52482 0.4552 0.521 | |
4 1.40628 1.29272 0.5494 0.573 | |
5 0.937755 0.539432 0.728 0.863 | |
6 0.230902 0.09883 0.9655 0.994 | |
7 0.0569344 0.0417305 0.9993 0.997 | |
8 0.0273535 0.0242202 0.9999 0.999 | |
9 0.0167176 0.0162068 1 1 | |
10 0.011597 0.011871 1 1 | |
11 0.00858905 0.00896583 1 1 | |
12 0.00666347 0.00704912 1 1 | |
13 0.00533772 0.00574273 1 1 | |
14 0.00437632 0.00473799 1 1 | |
15 0.00365677 0.00395577 1 1 | |
16 0.00309958 0.00336608 1 1 | |
17 0.00266046 0.00289834 1 1 | |
18 0.00230534 0.00251321 1 1 | |
19 0.00201576 0.00220578 1 1 | |
20 0.00177504 0.00193605 1 1 | |
21 0.00157303 0.00172307 1 1 | |
22 0.00140205 0.00153442 1 1 | |
23 0.00125536 0.00137551 1 1 | |
24 0.00112903 0.00123596 1 1 | |
25 0.00101942 0.00111762 1 1 | |
26 0.00092357 0.00101332 1 1 | |
27 0.000839527 0.000921253 1 1 | |
28 0.000765088 0.000840034 1 1 | |
29 0.000699283 0.000767484 1 1 | |
30 0.000640476 0.000703148 1 1 | |
... |
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$ python | |
Python 3.7.3 (default, Mar 27 2019, 22:11:17) | |
[GCC 7.3.0] :: Anaconda, Inc. on linux | |
Type "help", "copyright", "credits" or "license" for more information. | |
>>> import chainer | |
>>> chainer.print_runtime_info() | |
Platform: Linux-4.15.0-50-generic-x86_64-with-debian-buster-sid | |
Chainer: 6.0.0 | |
NumPy: 1.16.4 | |
CuPy: | |
CuPy Version : 6.0.0 | |
CUDA Root : /usr/local/cuda | |
CUDA Build Version : 10000 | |
CUDA Driver Version : 10000 | |
CUDA Runtime Version : 10000 | |
cuDNN Build Version : 7500 | |
cuDNN Version : 7500 | |
NCCL Build Version : 2402 | |
NCCL Runtime Version : 2402 | |
iDeep: 2.0.0.post3 | |
>>> |
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