cs224d 作業 problem set2 (二) TensorFlow 實現命名實體識別

阿新 • • 發佈：2019-01-09

WARNING:tensorflow:From C:\Users\weizhen\Documents\GitHub\TflinearClassifier\q2_NER.py:291: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
2017-10-02 16:31:40.821644: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn' 
t compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:40.822256: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 

2017-10-02 16:31:40.822842: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:40.823263: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn' 
t compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:40.823697: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:40.824035: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:40.824464: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:40.824850: W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-10-02 16:31:42.184267: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:887] Found device 0 with properties: 
name: GeForce 940MX
major: 5 minor: 0 memoryClockRate (GHz) 1.189
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.66GiB
2017-10-02 16:31:42.184794: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:908] DMA: 0 
2017-10-02 16:31:42.185018: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:918] 0:   Y 
2017-10-02 16:31:42.185582: I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0)
Epoch 0

0/3181.578125 : loss = 1.6745071411132812
Training loss : 1.6745071411132812
Training acc : 0.046875
Validation loss : 1.6497892141342163

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0 ----- O
C:\Users\weizhen\Documents\GitHub\TflinearClassifier\q2_NER.py:262: RuntimeWarning: invalid value encountered in true_divide
  prec = confusion[i, i] / float(total_guessed_tags[i])
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 3.293267250061035
Epoch 1

0/3181.578125 : loss = 1.6299598217010498
Training loss : 1.6299598217010498
Training acc : 0.0625
Validation loss : 1.6258254051208496

[[    0     0     0     0 42759]
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0 ----- O
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 3.019239664077759
Epoch 2

0/3181.578125 : loss = 1.6292331218719482
Training loss : 1.6292331218719482
Training acc : 0.078125
Validation loss : 1.6021082401275635

[[    0     0     0     0 42759]
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0 ----- O
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 2.9794013500213623
Epoch 3

0/3181.578125 : loss = 1.6349217891693115
Training loss : 1.6349217891693115
Training acc : 0.015625
Validation loss : 1.5785211324691772

[[    0     0     0     0 42759]
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0 ----- O
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 2.4377009868621826
Epoch 4

0/3181.578125 : loss = 1.5779037475585938
Training loss : 1.5779037475585938
Training acc : 0.09375
Validation loss : 1.5549894571304321

[[    0     0     0     0 42759]
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0 ----- O
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 2.4941294193267822
Epoch 5

0/3181.578125 : loss = 1.5726330280303955
Training loss : 1.5726330280303955
Training acc : 0.078125
Validation loss : 1.5313135385513306

[[    0     0     0     0 42759]
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0 ----- O
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 1.0000 / R 0.0008
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 2.4369616508483887
Epoch 6

0/3181.578125 : loss = 1.530135989189148
Training loss : 1.530135989189148
Training acc : 0.046875
Validation loss : 1.5071308612823486

[[    0     0     0     0 42759]
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0 ----- O
Tag: O - P nan / R 0.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 1.0000 / R 0.0047
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0613 / R 1.0000
Total time: 2.4289004802703857
Epoch 7

0/3181.578125 : loss = 1.4907350540161133
Training loss : 1.4907350540161133
Training acc : 0.0625
Validation loss : 1.482757806777954

[[  789     0     0     0 41970]
 [   19     0     0     0  2075]
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 [   45     0     1     0  2046]
 [   48     0     0     0  3101]]
0 ----- O
Tag: O - P 0.8747 / R 0.0185
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 0.8750 / R 0.0055
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0615 / R 0.9848
Total time: 3.0616846084594727
Epoch 8

0/3181.578125 : loss = 1.474185824394226
Training loss : 1.474185824394226
Training acc : 0.046875
Validation loss : 1.4580132961273193

[[ 7684     0     0     0 35075]
 [  364     0     0     0  1730]
 [   51     0    11     0  1206]
 [  445     0     1     0  1646]
 [  500     0     0     0  2649]]
0 ----- O
Tag: O - P 0.8496 / R 0.1797
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 0.9167 / R 0.0087
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0626 / R 0.8412
Total time: 2.969224214553833
Epoch 9

0/3181.578125 : loss = 1.498674988746643
Training loss : 1.498674988746643
Training acc : 0.28125
Validation loss : 1.4329923391342163

[[20553     0     1     0 22205]
 [ 1273     0     0     0   821]
 [  364     0     7     0   897]
 [  980     0     2     0  1110]
 [ 1426     0     0     0  1723]]
0 ----- O
Tag: O - P 0.8356 / R 0.4807
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 0.7000 / R 0.0055
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0644 / R 0.5472
Total time: 2.5193586349487305
Epoch 10

0/3181.578125 : loss = 1.4385690689086914
Training loss : 1.4385690689086914
Training acc : 0.421875
Validation loss : 1.4074962139129639

[[34564     0     4     0  8191]
 [ 1764     0     0     0   330]
 [  767     0     7     0   494]
 [ 1594     0     2     0   496]
 [ 2355     0     0     0   794]]
0 ----- O
Tag: O - P 0.8421 / R 0.8083
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 0.5385 / R 0.0055
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.0770 / R 0.2521
Total time: 2.3797054290771484
Epoch 11

0/3181.578125 : loss = 1.4594019651412964
Training loss : 1.4594019651412964
Training acc : 0.546875
Validation loss : 1.3817591667175293

[[40966     0     2     0  1791]
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 [ 1900     0     0     0   192]
 [ 2814     0     0     0   335]]
0 ----- O
Tag: O - P 0.8404 / R 0.9581
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P 0.6667 / R 0.0032
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.1283 / R 0.1064
Total time: 2.4073784351348877
Epoch 12

0/3181.578125 : loss = 1.3720815181732178
Training loss : 1.3720815181732178
Training acc : 0.78125
Validation loss : 1.3555692434310913

[[42709     0     0     0    50]
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0 ----- O
Tag: O - P 0.8332 / R 0.9988
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P 0.3366 / R 0.0108
Total time: 2.4379138946533203
Epoch 13

0/3181.578125 : loss = 1.3634321689605713
Training loss : 1.3634321689605713
Training acc : 0.828125
Validation loss : 1.328884482383728

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.8378336429595947
Epoch 14

0/3181.578125 : loss = 1.3688112497329712
Training loss : 1.3688112497329712
Training acc : 0.75
Validation loss : 1.302013635635376

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.7120652198791504
Epoch 15

0/3181.578125 : loss = 1.3235018253326416
Training loss : 1.3235018253326416
Training acc : 0.78125
Validation loss : 1.2748615741729736

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.6104214191436768
Epoch 16

0/3181.578125 : loss = 1.3185033798217773
Training loss : 1.3185033798217773
Training acc : 0.765625
Validation loss : 1.2475427389144897

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.3700265884399414
Epoch 17

0/3181.578125 : loss = 1.3193732500076294
Training loss : 1.3193732500076294
Training acc : 0.703125
Validation loss : 1.2201541662216187

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.439958095550537
Epoch 18

0/3181.578125 : loss = 1.2185351848602295
Training loss : 1.2185351848602295
Training acc : 0.75
Validation loss : 1.1924999952316284

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.8921308517456055
Epoch 19

0/3181.578125 : loss = 1.2128124237060547
Training loss : 1.2128124237060547
Training acc : 0.75
Validation loss : 1.164793610572815

[[42759     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.3643531799316406
Epoch 20

0/3181.578125 : loss = 1.174509882926941
Training loss : 1.174509882926941
Training acc : 0.71875
Validation loss : 1.137137770652771

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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.7417917251586914
Epoch 21

0/3181.578125 : loss = 1.056962490081787
Training loss : 1.056962490081787
Training acc : 0.84375
Validation loss : 1.1092265844345093

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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.5229830741882324
Epoch 22

0/3181.578125 : loss = 1.1316486597061157
Training loss : 1.1316486597061157
Training acc : 0.796875
Validation loss : 1.0816625356674194

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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.7065508365631104
Epoch 23

0/3181.578125 : loss = 1.073209524154663
Training loss : 1.073209524154663
Training acc : 0.78125
Validation loss : 1.0542036294937134

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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.6912477016448975
Epoch 24

0/3181.578125 : loss = 1.0102397203445435
Training loss : 1.0102397203445435
Training acc : 0.859375
Validation loss : 1.0271364450454712

[[42759     0     0     0     0]
 [ 2094     0     0     0     0]
 [ 1268     0     0     0     0]
 [ 2092     0     0     0     0]
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0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.5654654502868652
Epoch 25

0/3181.578125 : loss = 1.0918526649475098
Training loss : 1.0918526649475098
Training acc : 0.734375
Validation loss : 1.001003623008728

[[42759     0     0     0     0]
 [ 2094     0     0     0     0]
 [ 1268     0     0     0     0]
 [ 2092     0     0     0     0]
 [ 3149     0     0     0     0]]
0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.663228750228882
Epoch 26

0/3181.578125 : loss = 1.0216875076293945
Training loss : 1.0216875076293945
Training acc : 0.84375
Validation loss : 0.9754133820533752

[[42759     0     0     0     0]
 [ 2094     0     0     0     0]
 [ 1268     0     0     0     0]
 [ 2092     0     0     0     0]
 [ 3149     0     0     0     0]]
0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.5872113704681396
Epoch 27

0/3181.578125 : loss = 1.0990902185440063
Training loss : 1.0990902185440063
Training acc : 0.78125
Validation loss : 0.9509017467498779

[[42759     0     0     0     0]
 [ 2094     0     0     0     0]
 [ 1268     0     0     0     0]
 [ 2092     0     0     0     0]
 [ 3149     0     0     0     0]]
0 ----- O
Tag: O - P 0.8325 / R 1.0000
1 ----- LOC
Tag: LOC - P nan / R 0.0000
2 ----- MISC
Tag: MISC - P nan / R 0.0000
3 ----- ORG
Tag: ORG - P nan / R 0.0000
4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.443047285079956
Epoch 28

0/3181.578125 : loss = 0.9783419966697693
Training loss : 0.9783419966697693
Training acc : 0.8125
Validation loss : 0.9272997379302979

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.6797432899475098
Epoch 29

0/3181.578125 : loss = 1.0568724870681763
Training loss : 1.0568724870681763
Training acc : 0.765625
Validation loss : 0.904884934425354

[[42759     0     0     0     0]
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3 ----- ORG
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.3945367336273193
Epoch 30

0/3181.578125 : loss = 1.0237849950790405
Training loss : 1.0237849950790405
Training acc : 0.78125
Validation loss : 0.883781909942627

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.7488887310028076
Epoch 31

0/3181.578125 : loss = 1.0338774919509888
Training loss : 1.0338774919509888
Training acc : 0.765625
Validation loss : 0.8637697696685791

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4 ----- PER
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Total time: 2.381608247756958
Epoch 32

0/3181.578125 : loss = 0.9260292649269104
Training loss : 0.9260292649269104
Training acc : 0.78125
Validation loss : 0.8448039889335632

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3 ----- ORG
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.5534446239471436
Epoch 33

0/3181.578125 : loss = 0.8264249563217163
Training loss : 0.8264249563217163
Training acc : 0.875
Validation loss : 0.8267776370048523

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.558243751525879
Epoch 34

0/3181.578125 : loss = 0.9866911768913269
Training loss : 0.9866911768913269
Training acc : 0.8125
Validation loss : 0.8101168274879456

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4 ----- PER
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Total time: 2.464554786682129
Epoch 35

0/3181.578125 : loss = 0.8703485727310181
Training loss : 0.8703485727310181
Training acc : 0.8125
Validation loss : 0.7947656512260437

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.3714654445648193
Epoch 36

0/3181.578125 : loss = 0.8071379661560059
Training loss : 0.8071379661560059
Training acc : 0.84375
Validation loss : 0.7804707288742065

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.8228049278259277
Epoch 37

0/3181.578125 : loss = 0.6435794234275818
Training loss : 0.6435794234275818
Training acc : 0.875
Validation loss : 0.7670522928237915

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.6811318397521973
Epoch 38

0/3181.578125 : loss = 0.6902540326118469
Training loss : 0.6902540326118469
Training acc : 0.890625
Validation loss : 0.7546741962432861

[[42759     0     0     0     0]
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3 ----- ORG
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.596187114715576
Epoch 39

0/3181.578125 : loss = 0.6969885230064392
Training loss : 0.6969885230064392
Training acc : 0.859375
Validation loss : 0.7434151768684387

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.6147048473358154
Epoch 40

0/3181.578125 : loss = 0.87004554271698
Training loss : 0.87004554271698
Training acc : 0.78125
Validation loss : 0.7334315776824951

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.3841097354888916
Epoch 41

0/3181.578125 : loss = 0.95426344871521
Training loss : 0.95426344871521
Training acc : 0.78125
Validation loss : 0.7244532704353333

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.304476022720337
Epoch 42

0/3181.578125 : loss = 0.8543925285339355
Training loss : 0.8543925285339355
Training acc : 0.78125
Validation loss : 0.7163312435150146

[[42759     0     0     0     0]
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4 ----- PER
Tag: PER - P nan / R 0.0000
Total time: 2.6401660442352295
Epoch 43

0/3181.578125 : loss = 0.6948934197425842
Training loss : 0.6948934197425842
Training acc : 0.859375
Validation loss : 0.7088930606842041

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Total time: 2.954803228378296
Epoch 44

0/3181.578125 : loss = 0.8735166192054749
Training loss : 0.8735166192054749
Training acc : 0.796875
Validation loss : 0.7022351622581482

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Total time: 2.6469264030456543
Epoch 45

0/3181.578125 : loss = 0.8812070488929749
Training loss : 0.8812070488929749
Training acc : 0.828125
Validation loss : 0.695988118648529

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4 ----- PER
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Total time: 2.8571887016296387
Epoch 46

0/3181.578125 : loss = 0.5007133483886719
Training loss : 0.5007133483886719
Training acc : 0.90625
Validation loss : 0.690228283405304

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Total time: 2.360257625579834
Epoch 47

0/3181.578125 : loss = 0.8069882988929749
Training loss : 0.8069882988929749
Training acc : 0.8125
Validation loss : 0.6848161220550537

[[42759     0     0     0     0]
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Total time: 2.2997841835021973
Epoch 48

0/3181.578125 : loss = 0.6994635462760925
Training loss : 0.6994635462760925
Training acc : 0.8125
Validation loss : 0.6798946857452393

[[42759     0     0     0     0]
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Total time: 2.5274598598480225
Epoch 49

0/3181.578125 : loss = 0.7816348075866699
Training loss : 0.7816348075866699
Training acc : 0.8125
Validation loss : 0.6751761436462402

[[42759     0     0     0     0]
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Total time: 2.7655985355377197

cs224d 作業 problem set2 (二) TensorFlow 實現命名實體識別

WARNING:tensorflow:From C:\Users\weizhen\Documents\GitHub\TflinearClassifier\q2_NER.py:291: initialize_all_variables (from tensorflow.python.ops.variables

NLP（二十四）利用ALBERT實現命名實體識別

本文將會介紹如何利用ALBERT來實現`命名實體識別`。如果有對`命名實體識別`不清楚的讀者，請參考筆者的文章[NLP入門（四）命名實體識別（NER）](https://blog.csdn.net/jclian91/article/details/84073265) 。本文的專案結構如下： ![專案

BILSTM+CRF實現命名實體識別NER

#第一步：資料處理 #pikle是一個將任意複雜的物件轉成物件的文字或二進位制表示的過程。 #同樣，必須能夠將物件經過序列化後的形式恢復到原有的物件。 #在 Python 中，這種序列化過程稱為 pickle， #可以將物件 pickle 成字串、磁碟上的檔案或者任何類似於檔案的物件， #也可以

NLP入門（五）用深度學習實現命名實體識別（NER）

前言在文章：NLP入門（四）命名實體識別（NER）中，筆者介紹了兩個實現命名實體識別的工具——NLTK和Stanford NLP。在本文中，我們將會學習到如何使用深度學習工具來自己一步步地實現NER，只要你堅持看完，就一定會很有收穫的。 OK，話不多說，讓我們進入正題。幾乎所有的NLP都依賴一

NLP入門（八）使用CRF++實現命名實體識別(NER)

CRF與NER簡介 CRF，英文全稱為conditional random field, 中文名為條件隨機場，是給定一組輸入隨機變數條件下另一組輸出隨機變數的條件概率分佈模型，其特點是假設輸出隨機變數構成馬爾可夫（Markov）隨機場。較為簡單的條件隨機場是定義線上性鏈上的條件隨機場，稱為線性鏈條件

8.HanLP實現--命名實體識別

筆記轉載於GitHub專案：https://github.com/NLP-LOVE/Introduction-NLP 8. 命名實體識別 8.1 概述命名實體文字中有一些描述實體的詞彙。比如人名、地名、組織機構名、股票基金、醫學術語等，稱為命名實體。具有以下共性: 數量無窮。比如宇宙中的恆星命名、新生

cs224d 作業 problem set3 (一) 實現Recursive Nerual Net Work 遞迴神經網路

''' Created on 2017年10月5日 @author: weizhen ''' # 一個簡單的遞迴神經網路的實現，有著一個ReLU層和一個softmax層 # TODO : 必須要更新前向和後向傳遞函式 # 你可以通過執行 python rnn.py 方法來執行一個梯度檢驗 # 插入pdb.

cs224d 作業 problem set1 (一) 主要是實現word2vector模型,SGD,CBOW,Softmax,演算法

''' Created on 2017年9月13日 @author: weizhen ''' import numpy as np def sigmoid(x): return 1 / (1 + np.exp(-x)) 首先上來的是最簡單的sigmoid激勵

tensorflow實現驗證碼識別（二）

tfrecords 由於有個5W張圖片的訓練集，如果是placeholder的方式來把資料餵給graph的話，那可真蠢。。所以不如用tensorflow官方推薦的tfrecords來進行IO。這種IO方式主要是兩步： 1.先把資料dump成tfrecords檔案 2.用佇列來把資料餵

TensorFlow實現Softmax Regression識別手寫數字中"TimeoutError: [WinError 10060] 由於連接方在一段時間後沒有正確答復或連接的主機沒有反應，連接嘗試失敗”問題

http 截圖技術數字 alt 分享圖片 inf 主機 orf 出現問題：在使用TensorFlow實現MNIST手寫數字識別時，出現“TimeoutError: [WinError 10060] 由於連接方在一段時間後沒有正確答復或連接的主機沒有反應，連接嘗試失敗”

TensorFlow實現MNIST手寫體識別

# -*- coding: utf-8 -*- #匯入資料集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MINST_data/",one_hot = T

新手速成---TensorFlow實現jaffe表情識別--準備工作+step1

背景正如前文講的，博主主要是為了交作業。。。作業三選一，牛津花卉資料集、jaffe表情資料集、股票預測，很顯然我選的表情識別。 JAFFE資料集一共有213張影象．選取了10名日本女學生，每個人做出7種表情．7種表情包括： Angry，Disgust，Fear，Happy，Sad，Su

2.CNN圖片多標籤分類（基於TensorFlow實現驗證碼識別OCR）

上一篇實現了圖片CNN單標籤分類（貓狗圖片分類任務）地址：juejin.im/post/5c0739… 預告：下一篇用LSTM+CTC實現不定長文字的OCR，本質上是一種不固定標籤個數的多標籤分類問題本文所用到的10w驗證碼資料集百度網盤下載地址（也可使用下文程式碼自行生成）： pan.baidu

TensorFlow實現Softmax Regression識別手寫數字

本章已機器學習領域的Hello World任務----MNIST手寫識別做為TensorFlow的開始。MNIST是一個非常簡單的機器視覺資料集，是由幾萬張28畫素*28畫素的手寫數字組成，這些圖片只包含灰度值資訊。 import os os.environ['TF_CPP_MIN_LOG_LEVEL']=

BiLSTM+CRF(二）命名實體識別

前言前一篇部落格裡面，我們已經提到了如何構建一個雙向的LSTM網路，並在原來單層的RNN的基礎上，修改少數幾行程式碼即可實現。 Bi-LSTM其實就是兩個LSTM，只不過反向的LSTM是把輸入的資料先reverse 首尾轉置一下，然後跑一個正常的LSTM，然後再把輸出結果rever

使用Stanford Word Segmenter and Stanford Named Entity Recognizer (NER)實現中文命名實體識別

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