-
Notifications
You must be signed in to change notification settings - Fork 0
/
aws_runs.txt
474 lines (440 loc) · 29.9 KB
/
aws_runs.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
ubuntu@ip-172-31-60-138:~/hw5$ python2 grader/e2e_grader.pyc python3
Running test cases...
2019-04-29 06:58:45.276708: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 06:58:45.281344: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 06:58:45.282063: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55c578c64e60 executing computations on platform Host. Devices:
2019-04-29 06:58:45.282092: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False
Your Task 1a: ReadFile implementation works well, scoring: 100%
Your Task 1b: BuildMatrices implementation works well, scoring: 100%
Your Task 2a: lengths_vector_to_binary_matrix implementation works well, scoring: 0%
Summary:
Task,Score,MaximumScore
Task 1a: ReadFile,20,20
Task 1b: BuildMatrices,10,10
Task 2a: lengths_vector_to_binary_matrix,0,0
Total,30,30
2019-04-29 06:58:47.173280: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 06:58:47.177842: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 06:58:47.179106: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5644d33651f0 executing computations on platform Host. Devices:
2019-04-29 06:58:47.179134: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:331: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:341: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
[japanese] accuracy 0.0237577
[japanese] accuracy 0.855104
[japanese] accuracy 0.87599
[japanese] accuracy 0.881385
[japanese] accuracy 0.883735
[japanese] accuracy 0.890088
[japanese] accuracy 0.888696
[japanese] accuracy 0.888957
[japanese] accuracy 0.889914
[japanese] accuracy 0.888608
[japanese] accuracy 0.893917
First 10 accuracies for japanese are: 0.0237577,0.855104,0.87599,0.881385,0.883735,0.890088,0.888696,0.888957,0.889914,0.888608
Final accuracy for japanese, after 11 iterations, is 0.891567
2019-04-29 07:00:36.965769: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 07:00:36.970784: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 07:00:36.970988: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5642ded2bed0 executing computations on platform Host. Devices:
2019-04-29 07:00:36.971018: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:331: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:341: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
[italian] accuracy 0.0185061
[italian] accuracy 0.895746
[italian] accuracy 0.906465
[italian] accuracy 0.89784
[italian] accuracy 0.899096
Accuracies for italian are: 0.0185061,0.895746,0.906465,0.89784,0.899096
Final accuracy for italian, after 5 iterations, is 0.902864
Be patient... Training secret language and sending predictions to server ...
2019-04-29 07:04:28.878533: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 07:04:28.883866: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 07:04:28.884158: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x561e8176b240 executing computations on platform Host. Devices:
2019-04-29 07:04:28.884192: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:331: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:341: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
All done
##########################################################
ubuntu@ip-172-31-60-138:~/hw5$ python2 grader/e2e_grader.pyc python3
Running test cases...
2019-04-29 21:35:07.295005: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 21:35:07.299596: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 21:35:07.299955: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x558bc4c69830 executing computations on platform Host. Devices:
2019-04-29 21:35:07.299984: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False fc_keep_prob None
Your Task 1a: ReadFile implementation works well, scoring: 100%
Your Task 1b: BuildMatrices implementation works well, scoring: 100%
Your Task 2a: lengths_vector_to_binary_matrix implementation works well, scoring: 0%
Summary:
Task,Score,MaximumScore
Task 1a: ReadFile,20,20
Task 1b: BuildMatrices,10,10
Task 2a: lengths_vector_to_binary_matrix,0,0
Total,30,30
2019-04-29 21:35:09.133638: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 21:35:09.138851: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 21:35:09.139246: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x558a16bb6090 executing computations on platform Host. Devices:
2019-04-29 21:35:09.139278: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False fc_keep_prob None
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:377: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:391: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py:443: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
[japanese] accuracy 0.0447307
[japanese] accuracy 0.851536
[japanese] accuracy 0.890958
[japanese] accuracy 0.912105
[japanese] accuracy 0.910974
[japanese] accuracy 0.913236
[japanese] accuracy 0.91254
[japanese] accuracy 0.915673
[japanese] accuracy 0.918632
Accuracies for japanese are: 0.0447307,0.851536,0.890958,0.912105,0.910974,0.913236,0.91254,0.915673,0.918632
Final accuracy for japanese, after 9 iterations, is 0.912975
2019-04-29 21:36:53.810970: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 21:36:53.816127: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 21:36:53.816335: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5605b2b4dc10 executing computations on platform Host. Devices:
2019-04-29 21:36:53.816366: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False fc_keep_prob None
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:377: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:391: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py:443: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
[italian] accuracy 0.0354212
[italian] accuracy 0.883855
[italian] accuracy 0.89181
[italian] accuracy 0.899766
Accuracies for italian are: 0.0354212,0.883855,0.89181,0.899766
Final accuracy for italian, after 4 iterations, is 0.907804
Be patient... Training secret language and sending predictions to server ...
2019-04-29 21:40:30.301451: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-04-29 21:40:30.307103: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300055000 Hz
2019-04-29 21:40:30.307310: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5636aaae31c0 executing computations on platform Host. Devices:
2019-04-29 21:40:30.307342: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: False fc_keep_prob None
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:377: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:From /home/ubuntu/hw5/starter.py:391: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py:443: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
All done
####################################################################################
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.0235837
[japanese] accuracy 0.902271
[japanese] accuracy 0.92629
[japanese] accuracy 0.927247
[japanese] accuracy 0.931338
[japanese] accuracy 0.928379
Accuracies for japanese are: 0.0235837,0.902271,0.92629,0.927247,0.931338,0.928379
Final accuracy for japanese, after 6 iterations, is 0.929684
[italian] accuracy 0.0197622
[italian] accuracy 0.906883
[italian] accuracy 0.924133
Accuracies for italian are: 0.0197622,0.906883,0.924133
Final accuracy for italian, after 3 iterations, is 0.932340
###########################################################################################
size_embed: 100, state_size: 40, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.0235837
[japanese] accuracy 0.909581
[japanese] accuracy 0.92864
[japanese] accuracy 0.931425
[japanese] accuracy 0.932382
Accuracies for japanese are: 0.0235837,0.909581,0.92864,0.931425,0.932382
Final accuracy for japanese, after 5 iterations, is 0.932643
[italian] accuracy 0.0197622
[italian] accuracy 0.911824
Accuracies for italian are: 0.0197622,0.911824
Final accuracy for italian, after 2 iterations, is 0.915592
#############
size_embed: 100, state_size: 45, batch size: 30, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.0400313
[japanese] accuracy 0.899051
[japanese] accuracy 0.931251
[japanese] accuracy 0.936124
[japanese] accuracy 0.935428
[japanese] accuracy 0.936559
Accuracies for japanese are: 0.0400313,0.899051,0.931251,0.936124,0.935428,0.936559
Final accuracy for japanese, after 6 iterations, is 0.933426
[italian] accuracy 0.0199297
[italian] accuracy 0.895579
[italian] accuracy 0.916011
Accuracies for italian are: 0.0199297,0.895579,0.916011
Final accuracy for italian, after 3 iterations, is 0.917016
##################
size_embed: 80, state_size: 35, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.0166217
[japanese] accuracy 0.910539
[japanese] accuracy 0.928379
[japanese] accuracy 0.932991
[japanese] accuracy 0.935254
[japanese] accuracy 0.935602
Accuracies for japanese are: 0.0166217,0.910539,0.928379,0.932991,0.935254,0.935602
Final accuracy for japanese, after 6 iterations, is 0.933078
[italian] accuracy 0.0169151
[italian] accuracy 0.885865
[italian] accuracy 0.923045
Accuracies for italian are: 0.0169151,0.885865,0.923045
Final accuracy for italian, after 3 iterations, is 0.924971
#############################################################
size_embed: 110, state_size: 42, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.0274128
[japanese] accuracy 0.900966
[japanese] accuracy 0.926203
[japanese] accuracy 0.928727
[japanese] accuracy 0.930293
[japanese] accuracy 0.932295
Accuracies for japanese are: 0.0274128,0.900966,0.926203,0.928727,0.930293,0.932295
Final accuracy for japanese, after 6 iterations, is 0.934557
[italian] accuracy 0.0253726
[italian] accuracy 0.897588
[italian] accuracy 0.92338
Accuracies for italian are: 0.0253726,0.897588,0.92338
Final accuracy for italian, after 3 iterations, is 0.929409
#################################################################
size_embed: 200, state_size: 100, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.021234
[japanese] accuracy 0.942651
[japanese] accuracy 0.945784
[japanese] accuracy 0.940562
Accuracies for japanese are: 0.021234,0.942651,0.945784,0.940562
Final accuracy for japanese, after 4 iterations, is 0.941868
[italian] accuracy 0.0268799
[italian] accuracy 0.926059
Accuracies for italian are: 0.0268799,0.926059
Final accuracy for italian, after 2 iterations, is 0.930665
################################################################
size_embed: 200, state_size: 150, batch size: 32, lr: 0.01, cell type: bidic_rnn, use_fc: True, dropout_keep_prob: None, usc_bn: True fc_keep_prob None
[japanese] accuracy 0.0293273
[japanese] accuracy 0.938561
[japanese] accuracy 0.941084
Accuracies for japanese are: 0.0293273,0.938561,0.941084
Final accuracy for japanese, after 3 iterations, is 0.943086
[italian] accuracy 0.0232792
[italian] accuracy 0.925976
Accuracies for italian are: 0.0232792,0.925976
Final accuracy for italian, after 2 iterations, is 0.930581
###########################################################################
size_embed: 100, state_size: 50, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, usc_bn: True usc_fc_bn: True, fc_keep_prob: None, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0136629
[japanese] accuracy 0.943608
[japanese] accuracy 0.94735
[japanese] accuracy 0.944565
Accuracies for japanese are: 0.0136629,0.943608,0.94735,0.944565
Final accuracy for japanese, after 4 iterations, is 0.946741
[italian] accuracy 0.0220231
[italian] accuracy 0.949925
Accuracies for italian are: 0.0220231,0.949925
Final accuracy for italian, after 2 iterations, is 0.952186
##########################################################################
size_embed: 120, state_size: 67, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0375076
[japanese] accuracy 0.943782
[japanese] accuracy 0.947176
Accuracies for japanese are: 0.0375076,0.943782,0.947176
Final accuracy for japanese, after 3 iterations, is 0.947002
[italian] accuracy 0.0150729
[italian] accuracy 0.95319
Accuracies for italian are: 0.0150729,0.95319
Final accuracy for italian, after 2 iterations, is 0.953442
########################################################################
size_embed: 120, state_size: 67, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0375076
[japanese] accuracy 0.942999
[japanese] accuracy 0.947263
[japanese] accuracy 0.949352
Accuracies for japanese are: 0.0375076,0.942999,0.947263,0.949352
Final accuracy for japanese, after 4 iterations, is 0.947785
[italian] accuracy 0.0150729
[italian] accuracy 0.954363
Accuracies for italian are: 0.0150729,0.954363
Final accuracy for italian, after 2 iterations, is 0.954614
######################################################################
size_embed: 150, state_size: 70, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0374206
[japanese] accuracy 0.942825
[japanese] accuracy 0.947611
Accuracies for japanese are: 0.0374206,0.942825,0.947611
Final accuracy for japanese, after 3 iterations, is 0.949091
############################################################
##SPEC## size_embed: 100, state_size: 80, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0224524
[japanese] accuracy 0.93917
[japanese] accuracy 0.944304
[japanese] accuracy 0.947263
[japanese] accuracy 0.942651
[japanese] accuracy 0.945871
[japanese] accuracy 0.946219
Accuracies for japanese are: 0.0224524,0.93917,0.944304,0.947263,0.942651,0.945871,0.946219
Final accuracy for japanese, after 7 iterations, is 0.942999
############################################################
##SPEC## size_embed: 150, state_size: 70, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
timeed
[japanese] accuracy 0.0301975
[japanese] accuracy 0.943434
[japanese] accuracy 0.94413
[japanese] accuracy 0.947263
[japanese] accuracy 0.944913
[japanese] accuracy 0.947437
Accuracies for japanese are: 0.0301975,0.943434,0.94413,0.947263,0.944913,0.947437
Final accuracy for japanese, after 6 iterations, is 0.943782
[italian] accuracy 0.0311506
[italian] accuracy 0.953944
Accuracies for italian are: 0.0311506,0.953944
Final accuracy for italian, after 2 iterations, is 0.953442
#################################################################
##SPEC## size_embed: 150, state_size: 70, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0301975
[japanese] accuracy 0.944478
[japanese] accuracy 0.945087
Accuracies for japanese are: 0.0301975,0.944478,0.945087
Final accuracy for japanese, after 3 iterations, is 0.948133
[italian] accuracy 0.0311506
[italian] accuracy 0.952855
Accuracies for italian are: 0.0311506,0.952855
Final accuracy for italian, after 2 iterations, is 0.955200
azizim@usc.edu 95.5 94.8 >95th
#################################################################
##SPEC## size_embed: 150, state_size: 60, batch size: 32, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0285441
[japanese] accuracy 0.938909
[japanese] accuracy 0.947002
Accuracies for japanese are: 0.0285441,0.938909,0.947002
Final accuracy for japanese, after 3 iterations, is 0.949526
[italian] accuracy 0.0162452
[italian] accuracy 0.953777
Accuracies for italian are: 0.0162452,0.953777
Final accuracy for italian, after 2 iterations, is 0.955116
azizim@usc.edu 95.5 95 >85th
##############################################################
##SPEC## size_embed: 150, state_size: 60, batch size: 30, lr: 0.008, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0285441
[japanese] accuracy 0.945174
[japanese] accuracy 0.94561
[japanese] accuracy 0.948568
[japanese] accuracy 0.948742
[japanese] accuracy 0.948742
Accuracies for japanese are: 0.0285441,0.945174,0.94561,0.948568,0.948742,0.948742
Final accuracy for japanese, after 6 iterations, is 0.944478
[italian] accuracy 0.0162452
[italian] accuracy 0.952437
Accuracies for italian are: 0.0162452,0.952437
Final accuracy for italian, after 2 iterations, is 0.955368
###############################################################
##SPEC## size_embed: 150, state_size: 60, batch size: 50, lr: 0.01, cell type: bi_res+bi_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
timed
[japanese] accuracy 0.0401184
[japanese] accuracy 0.940301
[japanese] accuracy 0.945871
[japanese] accuracy 0.946393
Accuracies for japanese are: 0.0401184,0.940301,0.945871,0.946393
Final accuracy for japanese, after 4 iterations, is 0.944478
[italian] accuracy 0.0130631
Accuracies for italian are: 0.0130631
Final accuracy for italian, after 1 iterations, is 0.953274
#################################################################
##SPEC## size_embed: 170, state_size: 75, batch size: 50, lr: 0.01, cell type: bidic_lstm, use_fc: True, dropout_keep_prob: None, fc_keep_prob: None, usc_bn: True usc_fc_bn: True, rnn_n_layers: 10, multi_cell_type: lstm
[japanese] accuracy 0.0181881
[japanese] accuracy 0.94004
[japanese] accuracy 0.946567
Accuracies for japanese are: 0.0181881,0.94004,0.946567
Final accuracy for japanese, after 3 iterations, is 0.951962
[italian] accuracy 0.0198459
[italian] accuracy 0.950846
Accuracies for italian are: 0.0198459,0.950846
Final accuracy for italian, after 2 iterations, is 0.953023