├── .github
└── FUNDING.yml
├── .gitignore
├── LICENSE
├── README.md
├── Test_Notebook.ipynb
├── assets
├── 2d_lstm.png
├── 2d_lstm_1.png
├── md_lstm.png
├── md_lstm.vsz
├── md_lstm2.png
├── md_lstm2.vsz
├── md_lstm_2.vsz
├── md_pred_0.png
├── md_pred_500.png
├── md_truth_0.png
├── md_truth_500.png
├── out_HORIZONTAL_SD_LSTM.tsv
├── out_MD_LSTM.tsv
└── out_SNAKE_SD_LSTM.tsv
├── data_gen_gaussian_fields.py
├── data_random_short_diagonal.py
├── md_lstm.py
├── requirements.txt
├── run.sh
├── scripts
└── display.py
└── trainer.py
/.github/FUNDING.yml:
--------------------------------------------------------------------------------
1 | github: [philipperemy]
2 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | .idea/
7 | .DS_Store
8 | # C extensions
9 | *.so
10 |
11 | # Distribution / packaging
12 | .Python
13 | env/
14 | build/
15 | develop-eggs/
16 | dist/
17 | downloads/
18 | eggs/
19 | .eggs/
20 | lib/
21 | lib64/
22 | parts/
23 | sdist/
24 | var/
25 | wheels/
26 | *.egg-info/
27 | .installed.cfg
28 | *.egg
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .coverage
44 | .coverage.*
45 | .cache
46 | nosetests.xml
47 | coverage.xml
48 | *.cover
49 | .hypothesis/
50 |
51 | # Translations
52 | *.mo
53 | *.pot
54 |
55 | # Django stuff:
56 | *.log
57 | local_settings.py
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # dotenv
85 | .env
86 |
87 | # virtualenv
88 | .venv
89 | venv/
90 | ENV/
91 |
92 | # Spyder project settings
93 | .spyderproject
94 | .spyproject
95 |
96 | # Rope project settings
97 | .ropeproject
98 |
99 | # mkdocs documentation
100 | /site
101 |
102 | # mypy
103 | .mypy_cache/
104 |
105 | out_*.tsv
106 |
--------------------------------------------------------------------------------
/LICENSE:
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/README.md:
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1 | # Multi Dimensional Recurrent Networks
2 | Tensorflow Implementation of the model described in Alex Graves' paper https://arxiv.org/pdf/0705.2011.pdf.
3 |
4 | - [x] **Fully compatible with Tensorflow 1.7.**
5 | - [x] **Only 2D is supported now.**
6 | - [x] **[Comes with some big limitations](https://github.com/philipperemy/tensorflow-multi-dimensional-lstm#limitations).**
7 |
8 |
9 | 
10 |
Example: 2D LSTM Architecture
11 |
12 |
13 | ## What is MD LSTM?
14 |
15 | Basically a LSTM that is multidirectional, for example, that can operate on a 2D grid. Here's a figure describing the way it works:
16 |
17 |
18 |
19 |
Example: 2D LSTM Architecture
20 |
21 |
22 | ## How to get started?
23 | ```bash
24 | git clone git@github.com:philipperemy/tensorflow-multi-dimensional-lstm.git
25 | cd tensorflow-multi-dimensional-lstm
26 |
27 | # create a new virtual python environment
28 | virtualenv -p python3 venv
29 | source venv/bin/activate
30 | pip install -r requirements.txt
31 |
32 | # usage: trainer.py [-h] --model_type {MD_LSTM,HORIZONTAL_SD_LSTM,SNAKE_SD_LSTM}
33 | python trainer.py --model_type MD_LSTM
34 | python trainer.py --model_type HORIZONTAL_SD_LSTM
35 | python trainer.py --model_type SNAKE_SD_LSTM
36 | ```
37 |
38 | ## Random diagonal Task
39 |
40 | The random diagonal task consists in initializing a matrix with values very close to 0 except two which are set to 1. Those two values are on a straight line parallel to the diagonal of the matrix. The idea is to predict where those two values are. Here are some examples:
41 |
42 | ```
43 | ____________
44 | | |
45 | |x |
46 | | x |
47 | | |
48 | |__________|
49 |
50 |
51 | ____________
52 | | |
53 | | |
54 | | x |
55 | | x |
56 | |__________|
57 |
58 | ____________
59 | | |
60 | | x |
61 | | x |
62 | | |
63 | |__________|
64 |
65 | ```
66 |
67 | A model performing on this task is considered as successful if it can correctly predict the second x (it's impossible to predict the first x).
68 |
69 | - A simple recurrent model going vertical or horizontal cannot predict any locations of x. This model is called `HORIZONTAL_SD_LSTM`. It should perform the worst.
70 | - If the matrix is flattened as one single vector, then the first location of x still cannot be predicted. However, a recurrent model should understand that the second x always comes after the first x (width+1 steps). (Model is `SNAKE_SD_LSTM`).
71 | - When predicting the second location of x, a MD recurrent model has a full view of the TOP LEFT corner. In that case, it should understand that when the first x is in the bottom right of its window, the second x will be next on the diagonal axis. Of course the first location x still cannot be predicted at all with this MD model.
72 |
73 | After training on this task for 8x8 matrices, the losses look like this:
74 |
75 |
76 | Overall loss of the random diagonal task (loss applied on all the elements of the inputs)
77 | 
78 |
79 |
80 |
81 |
82 | Overall loss of the random diagonal task (loss applied only on the location of the second x)
83 | 
84 |
85 |
86 | No surprise that MD LSTM performs the best here. It has direct connections between the grid cell that contains the first x and the second x (2 connections). The snake LSTM has width+1 = 9 steps between the two x. As expected, the vertical LSTM does not learn anything apart from outputting values very close to 0.
87 |
88 |
89 |
90 | 
91 | 
92 |
MD LSTM predictions (left) and ground truth (right) before training (predictions are all random).
93 |
94 |
95 |
96 | 
97 | 
98 |
MD LSTM predictions (left) and ground truth (right) after training. As expected, the MD LSTM can only predict the second x and not the first one. That means the task is correctly predicted.
99 |
100 |
101 | ## Limitations
102 | - I could test it successfully with 32x32 matrices but the implementation is far from being well optimised.
103 | - This implementation can become numerically unstable quite easily.
104 | - I've noticed that inputs should be != 0. Otherwise some gradients are nan. So consider `inputs += eps` in case.
105 | - It's hard to use in Keras. This implementation is in pure tensorflow.
106 | - It runs on a GPU but the code is not optimized at all so I would say it's equally fast (CPU vs GPU).
107 |
108 | ## Contributions
109 |
110 | Welcome!
111 |
112 |
113 | ## Special Thanks
114 | - A big *thank you* to [Mosnoi Ion](https://stackoverflow.com/questions/42071074/multidimentional-lstm-tensorflow) who provided the first skeleton of this MD LSTM.
115 |
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/assets/2d_lstm_1.png:
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/assets/md_lstm.png:
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/assets/md_lstm.vsz:
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1 | # Veusz saved document (version 1.26.1)
2 | # Saved at 2018-05-18T06:05:55.143581
3 |
4 | AddImportPath(u'/Users/philipperemy/Desktop')
5 | ImportFileCSV(u'../PycharmProjects/tensorflow-multi-dimensional-lstm/out.csv', linked=True)
6 | Set('colorTheme', 'default-latest')
7 | Add('page', name='page1', autoadd=False)
8 | To('page1')
9 | Set('width', '27.982cm')
10 | Set('height', '20.962cm')
11 | Add('graph', name='graph1', autoadd=False)
12 | To('graph1')
13 | Add('axis', name='x', autoadd=False)
14 | To('x')
15 | Set('label', u'Steps')
16 | To('..')
17 | Add('axis', name='y', autoadd=False)
18 | To('y')
19 | Set('label', u'Relevant Loss (MSE)')
20 | Set('log', True)
21 | Set('direction', 'vertical')
22 | To('..')
23 | Add('xy', name='xy1', autoadd=False)
24 | To('xy1')
25 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
26 | Set('yData', u'relevant_loss_HORIZONTAL_SD_LSTM')
27 | Set('key', u'HORIZONTAL LSTM')
28 | To('..')
29 | Add('xy', name='xy2', autoadd=False)
30 | To('xy2')
31 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
32 | Set('yData', u'relevant_loss_MD_LSTM')
33 | Set('key', u'MD LSTM')
34 | To('..')
35 | Add('xy', name='xy3', autoadd=False)
36 | To('xy3')
37 | Set('xData', u'steps_MD_LSTM')
38 | Set('yData', u'relevant_loss_SNAKE_SD_LSTM')
39 | Set('key', u'SNAKE LSTM')
40 | To('..')
41 | Add('key', name='key1', autoadd=False)
42 | To('key1')
43 | Set('horzPosn', 'manual')
44 | Set('vertPosn', 'manual')
45 | Set('horzManual', 0.060223937637822106)
46 | Set('vertManual', 0.06754592881716667)
47 | To('..')
48 | To('..')
49 | To('..')
50 |
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/assets/md_lstm2.png:
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https://raw.githubusercontent.com/philipperemy/tensorflow-multi-dimensional-lstm/6269790409a62a3285a02faefd7e33178a901421/assets/md_lstm2.png
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/assets/md_lstm2.vsz:
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1 | # Veusz saved document (version 1.26.1)
2 | # Saved at 2018-05-18T06:12:15.167115
3 |
4 | AddImportPath(u'/Users/philipperemy/PycharmProjects/tensorflow-multi-dimensional-lstm/assets')
5 | ImportFileCSV(u'../out.csv', linked=True)
6 | Set('colorTheme', 'default-latest')
7 | Add('page', name='page1', autoadd=False)
8 | To('page1')
9 | Set('width', '28.052cm')
10 | Set('height', '20.856cm')
11 | Add('graph', name='graph1', autoadd=False)
12 | To('graph1')
13 | Add('axis', name='x', autoadd=False)
14 | To('x')
15 | Set('label', u'Steps')
16 | To('..')
17 | Add('axis', name='y', autoadd=False)
18 | To('y')
19 | Set('label', u'Overall Loss')
20 | Set('log', True)
21 | Set('direction', 'vertical')
22 | To('..')
23 | Add('xy', name='xy1', autoadd=False)
24 | To('xy1')
25 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
26 | Set('yData', u'overall_loss_HORIZONTAL_SD_LSTM')
27 | Set('key', u'overall loss HORIZONTAL SD LSTM')
28 | To('..')
29 | Add('xy', name='xy2', autoadd=False)
30 | To('xy2')
31 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
32 | Set('yData', u'overall_loss_MD_LSTM')
33 | Set('key', u'overall loss MD LSTM')
34 | To('..')
35 | Add('xy', name='xy3', autoadd=False)
36 | To('xy3')
37 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
38 | Set('yData', u'overall_loss_SNAKE_SD_LSTM')
39 | Set('key', u'overall loss SNAKE SD LSTM')
40 | To('..')
41 | Add('key', name='key2', autoadd=False)
42 | To('key2')
43 | Set('horzPosn', 'manual')
44 | Set('vertPosn', 'manual')
45 | Set('horzManual', 0.5635879976889437)
46 | Set('vertManual', 0.8288552711730526)
47 | To('..')
48 | To('..')
49 | To('..')
50 |
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/assets/md_lstm_2.vsz:
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1 | # Veusz saved document (version 1.26.1)
2 | # Saved at 2018-05-18T06:07:15.011240
3 |
4 | AddImportPath(u'/Users/philipperemy/Desktop')
5 | ImportFileCSV(u'../PycharmProjects/tensorflow-multi-dimensional-lstm/out.csv', linked=True)
6 | Set('colorTheme', 'default-latest')
7 | Add('page', name='page1', autoadd=False)
8 | To('page1')
9 | Set('width', '28.052cm')
10 | Set('height', '20.856cm')
11 | Add('graph', name='graph1', autoadd=False)
12 | To('graph1')
13 | Add('axis', name='x', autoadd=False)
14 | To('x')
15 | Set('label', u'Steps')
16 | To('..')
17 | Add('axis', name='y', autoadd=False)
18 | To('y')
19 | Set('label', u'Overall Loss')
20 | Set('log', True)
21 | Set('direction', 'vertical')
22 | To('..')
23 | Add('xy', name='xy1', autoadd=False)
24 | To('xy1')
25 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
26 | Set('yData', u'overall_loss_HORIZONTAL_SD_LSTM')
27 | Set('key', u'overall loss HORIZONTAL SD LSTM')
28 | To('..')
29 | Add('xy', name='xy2', autoadd=False)
30 | To('xy2')
31 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
32 | Set('yData', u'overall_loss_MD_LSTM')
33 | Set('key', u'overall loss MD LSTM')
34 | To('..')
35 | Add('xy', name='xy3', autoadd=False)
36 | To('xy3')
37 | Set('xData', u'steps_HORIZONTAL_SD_LSTM')
38 | Set('yData', u'overall_loss_SNAKE_SD_LSTM')
39 | Set('key', u'overall loss SNAKE SD LSTM')
40 | To('..')
41 | Add('key', name='key2', autoadd=False)
42 | To('key2')
43 | Set('horzPosn', 'manual')
44 | Set('vertPosn', 'manual')
45 | Set('horzManual', 0.6283529379279876)
46 | Set('vertManual', 0.8288552711730526)
47 | To('..')
48 | To('..')
49 | To('..')
50 |
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/assets/md_pred_0.png:
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/assets/out_HORIZONTAL_SD_LSTM.tsv:
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1 | steps_HORIZONTAL_SD_LSTM overall_loss_HORIZONTAL_SD_LSTM time_HORIZONTAL_SD_LSTM relevant_loss_HORIZONTAL_SD_LSTM
2 | 0000 0.19921911 0.11474728584289551 0.2534082040974048
3 | 0001 0.18925178 0.004488229751586914 0.26900125143844733
4 | 0002 0.17646053 0.002721071243286133 0.2852932000335859
5 | 0003 0.16319916 0.0027451515197753906 0.31101570466387995
6 | 0004 0.14868134 0.002891063690185547 0.3362361713033714
7 | 0005 0.13134192 0.0031118392944335938 0.3613336626907548
8 | 0006 0.11651701 0.002902984619140625 0.43579135842853084
9 | 0007 0.098357484 0.0028929710388183594 0.4571256138150545
10 | 0008 0.0811672 0.0031108856201171875 0.45236578616999357
11 | 0009 0.071983606 0.0032372474670410156 0.5512274019723333
12 | 0010 0.06462401 0.0031728744506835938 0.6274200876090908
13 | 0011 0.05721963 0.0035829544067382812 0.6145898053104314
14 | 0012 0.053943742 0.0037279129028320312 0.6710905154004343
15 | 0013 0.05413095 0.0035941600799560547 0.7645731984003308
16 | 0014 0.047608055 0.003703594207763672 0.694361463385007
17 | 0015 0.050139584 0.0035538673400878906 0.8018395369011759
18 | 0016 0.048492372 0.004003763198852539 0.8240083486888639
19 | 0017 0.048527025 0.003874063491821289 0.8751049246235165
20 | 0018 0.047630273 0.005222320556640625 0.8916541782332844
21 | 0019 0.044566017 0.005071163177490234 0.838994357567827
22 | 0020 0.045053244 0.003345012664794922 0.8912290908269676
23 | 0021 0.044019755 0.003729104995727539 0.8967056130644648
24 | 0022 0.042942643 0.004739999771118164 0.8847022713122112
25 | 0023 0.0422449 0.003988981246948242 0.8875752302606301
26 | 0024 0.04569094 0.0038280487060546875 0.9757007735016868
27 | 0025 0.040280495 0.003412961959838867 0.8791521205025896
28 | 0026 0.040343776 0.0032241344451904297 0.8891382050606871
29 | 0027 0.041487712 0.003286123275756836 0.9322345068852735
30 | 0028 0.04073081 0.0032303333282470703 0.9182681045067755
31 | 0029 0.04126718 0.004177093505859375 0.9481470229977802
32 | 0030 0.037650265 0.0031731128692626953 0.8747850422735908
33 | 0031 0.039802358 0.0034248828887939453 0.9316404405088908
34 | 0032 0.04038801 0.0040280818939208984 0.9546399588505345
35 | 0033 0.04044111 0.0035200119018554688 0.9613300762629271
36 | 0034 0.039351158 0.003147125244140625 0.9462487938284407
37 | 0035 0.04046199 0.003184080123901367 0.9757523994843713
38 | 0036 0.03891567 0.0028388500213623047 0.9468941561769415
39 | 0037 0.03905035 0.005041837692260742 0.9527976822386237
40 | 0038 0.038655337 0.007875919342041016 0.9445469057208045
41 | 0039 0.039112944 0.0044820308685302734 0.9690157825256693
42 | 0040 0.040094577 0.0057888031005859375 0.9829895640050047
43 | 0041 0.038635097 0.003484964370727539 0.9596844529877856
44 | 0042 0.0376267 0.003312826156616211 0.9512845145097408
45 | 0043 0.03933361 0.003202199935913086 0.9829278430629858
46 | 0044 0.038486097 0.003014087677001953 0.9727503929054843
47 | 0045 0.037466343 0.0029861927032470703 0.9540487569652043
48 | 0046 0.036803663 0.003365039825439453 0.9556342329756662
49 | 0047 0.038981702 0.0037310123443603516 0.985729862929269
50 | 0048 0.037916593 0.003679990768432617 0.9680608537113391
51 | 0049 0.0396908 0.003888845443725586 0.9963176665161719
52 | 0050 0.03745916 0.0033729076385498047 0.9692530615062551
53 | 0051 0.03755745 0.005339860916137695 0.9693485063906832
54 | 0052 0.03677555 0.007209062576293945 0.9592219117479626
55 | 0053 0.03529394 0.004534006118774414 0.9365204021258375
56 | 0054 0.037371315 0.006788969039916992 0.9832493894232531
57 | 0055 0.037894063 0.004296064376831055 0.9881014245690728
58 | 0056 0.035908855 0.0037660598754882812 0.956198043598651
59 | 0057 0.036457054 0.0037119388580322266 0.9660389754614884
60 | 0058 0.036186107 0.0034711360931396484 0.9653800631121061
61 | 0059 0.03857635 0.003453969955444336 0.9970385026748281
62 | 0060 0.036415927 0.003378152847290039 0.9730809211865028
63 | 0061 0.03671799 0.0032892227172851562 0.9808442756271112
64 | 0062 0.03669276 0.0032050609588623047 0.9804477946032402
65 | 0063 0.036948256 0.003793954849243164 0.9825703430923046
66 | 0064 0.03593179 0.004398822784423828 0.9759485268428187
67 | 0065 0.03700878 0.0037260055541992188 0.9833708811734804
68 | 0066 0.035280377 0.0033888816833496094 0.9670681786928201
69 | 0067 0.03571872 0.0028090476989746094 0.970598862886136
70 | 0068 0.037360445 0.003593921661376953 0.9955370518662783
71 | 0069 0.03624951 0.007442951202392578 0.9836489031302917
72 | 0070 0.03596199 0.005008220672607422 0.9776061396702873
73 | 0071 0.035457633 0.0057239532470703125 0.9783280582571112
74 | 0072 0.035026416 0.005415916442871094 0.9673926580021794
75 | 0073 0.03507918 0.0037679672241210938 0.9709150646429552
76 | 0074 0.036313456 0.0036170482635498047 0.9889222915640489
77 | 0075 0.036137372 0.003309011459350586 0.9848749340995597
78 | 0076 0.03636305 0.003326892852783203 0.9858107700071739
79 | 0077 0.03429088 0.003582000732421875 0.9622116605070385
80 | 0078 0.035030626 0.0030791759490966797 0.9761601271892777
81 | 0079 0.035564166 0.0033740997314453125 0.97821513448425
82 | 0080 0.035141878 0.003269195556640625 0.979710392629818
83 | 0081 0.035814516 0.0041408538818359375 0.9881412456730663
84 | 0082 0.034909427 0.004976987838745117 0.973634667665704
85 | 0083 0.034897618 0.0033750534057617188 0.9748375753895273
86 | 0084 0.034299802 0.0028841495513916016 0.9696932925795078
87 | 0085 0.03552524 0.0026776790618896484 0.9858430664721363
88 | 0086 0.03519621 0.005023956298828125 0.9828829690660691
89 | 0087 0.0349173 0.00770115852355957 0.980324685562037
90 | 0088 0.035314865 0.004848957061767578 0.9847659127560086
91 | 0089 0.03494843 0.006374835968017578 0.9802218719450512
92 | 0090 0.035306536 0.004216909408569336 0.9878541318293174
93 | 0091 0.034767136 0.0035369396209716797 0.9796912377443814
94 | 0092 0.034485817 0.0036249160766601562 0.9779316812662875
95 | 0093 0.03516213 0.003466367721557617 0.9869345655577536
96 | 0094 0.03438375 0.0034558773040771484 0.9784306564221754
97 | 0095 0.034817427 0.003103971481323242 0.982954049048512
98 | 0096 0.034496687 0.003120899200439453 0.9796974847709896
99 | 0097 0.0353109 0.0030510425567626953 0.9879502730329002
100 | 0098 0.03386599 0.0036101341247558594 0.9732267070690996
101 | 0099 0.03526679 0.0038900375366210938 0.9869781470591624
102 | 0100 0.033978205 0.00621795654296875 0.9743383499832623
103 | 0101 0.033348337 0.007804155349731445 0.9669728747464645
104 | 0102 0.035031654 0.00639796257019043 0.9892165576963321
105 | 0103 0.03255663 0.00412297248840332 0.9621096424690458
106 | 0104 0.03474307 0.0038390159606933594 0.9845479541005504
107 | 0105 0.034244966 0.004332780838012695 0.9802758101955171
108 | 0106 0.034403495 0.0035910606384277344 0.9860660350739759
109 | 0107 0.034172185 0.003568887710571289 0.9775796460054925
110 | 0108 0.034024626 0.0036122798919677734 0.9787331192169269
111 | 0109 0.032154556 0.003314971923828125 0.9545357331112114
112 | 0110 0.033421945 0.0034079551696777344 0.9696010050113053
113 | 0111 0.034287408 0.0035200119018554688 0.9810113346585473
114 | 0112 0.032939106 0.003840923309326172 0.964909010684255
115 | 0113 0.033398848 0.003468036651611328 0.9701966621062921
116 | 0114 0.033483773 0.0029058456420898438 0.9729318559194874
117 | 0115 0.032427646 0.002933025360107422 0.9606506228322548
118 | 0116 0.033373922 0.004595041275024414 0.9700752805814574
119 | 0117 0.032972604 0.008637189865112305 0.9659956418869788
120 | 0118 0.03182938 0.004992008209228516 0.9486066169804296
121 | 0119 0.03242305 0.005537986755371094 0.9575893280874477
122 | 0120 0.0325294 0.0038340091705322266 0.9605469701604914
123 | 0121 0.03164988 0.004287004470825195 0.9447111452704755
124 | 0122 0.032354124 0.00410008430480957 0.955631904676536
125 | 0123 0.03246685 0.0034852027893066406 0.9573944432227837
126 | 0124 0.03219133 0.003596782684326172 0.9525262150760989
127 | 0125 0.032290615 0.0036389827728271484 0.9535640734189876
128 | 0126 0.031155152 0.0036170482635498047 0.9372264029137941
129 | 0127 0.031879038 0.0033600330352783203 0.9468135371240123
130 | 0128 0.031601585 0.003863811492919922 0.9419511025097975
131 | 0129 0.031076625 0.0038590431213378906 0.9304591484855085
132 | 0130 0.03125894 0.003195047378540039 0.9319091634455848
133 | 0131 0.030203342 0.0030739307403564453 0.910227749362408
134 | 0132 0.030270174 0.0027892589569091797 0.9130464298695682
135 | 0133 0.029862365 0.0026328563690185547 0.903358618134876
136 | 0134 0.03048721 0.004868984222412109 0.9141483714155921
137 | 0135 0.030635517 0.0075337886810302734 0.9140993058733892
138 | 0136 0.030113224 0.004502058029174805 0.9043008349247079
139 | 0137 0.03022829 0.0058438777923583984 0.9034965331144932
140 | 0138 0.029833727 0.0037751197814941406 0.8961693480301504
141 | 0139 0.03040614 0.003757953643798828 0.904950416996524
142 | 0140 0.029478215 0.003760814666748047 0.8834481238116423
143 | 0141 0.029181179 0.0035169124603271484 0.8717367645904959
144 | 0142 0.030070683 0.003340005874633789 0.889647077854544
145 | 0143 0.029644266 0.003442049026489258 0.8803896025617166
146 | 0144 0.029031988 0.0032439231872558594 0.8722441467358439
147 | 0145 0.028659187 0.003598928451538086 0.8573129502818897
148 | 0146 0.028774086 0.003923892974853516 0.858093639824724
149 | 0147 0.02929325 0.004090070724487305 0.867640965847521
150 | 0148 0.029343724 0.00676417350769043 0.8698856666064858
151 | 0149 0.028713606 0.0039370059967041016 0.8511317773206515
152 | 0150 0.029363144 0.004205942153930664 0.8638909544725903
153 | 0151 0.029011227 0.00623774528503418 0.8551201920586367
154 | 0152 0.028540902 0.0039441585540771484 0.8419798851556972
155 | 0153 0.028813258 0.0036749839782714844 0.839781090173706
156 | 0154 0.028780054 0.0035359859466552734 0.8471428912097525
157 | 0155 0.028477866 0.0033969879150390625 0.8412161805489509
158 | 0156 0.028928077 0.003556966781616211 0.8505614097085177
159 | 0157 0.028608825 0.003480195999145508 0.8413029666272095
160 | 0158 0.02891048 0.0033926963806152344 0.8483469781757295
161 | 0159 0.028588999 0.0029478073120117188 0.845301498373207
162 | 0160 0.02896379 0.0030291080474853516 0.8513822924816754
163 | 0161 0.028882273 0.0036001205444335938 0.854695293703088
164 | 0162 0.028309558 0.0041027069091796875 0.8417285576878976
165 | 0163 0.028505364 0.0033600330352783203 0.8450258813186429
166 | 0164 0.028718576 0.0030219554901123047 0.8442540443736001
167 | 0165 0.02859598 0.002937793731689453 0.8488233429566011
168 | 0166 0.028038647 0.005797863006591797 0.8382923279715584
169 | 0167 0.028301615 0.007896900177001953 0.8402203767161162
170 | 0168 0.028467625 0.005025148391723633 0.8473981937694067
171 | 0169 0.028807186 0.006037712097167969 0.8573546978361091
172 | 0170 0.028592192 0.0036749839782714844 0.8547971526405882
173 | 0171 0.028225929 0.003919124603271484 0.8471804010652283
174 | 0172 0.028224278 0.003580808639526367 0.8446896890380795
175 | 0173 0.028564792 0.003473997116088867 0.8539475529778924
176 | 0174 0.028053585 0.003429889678955078 0.844987175407073
177 | 0175 0.02835222 0.003942251205444336 0.8495981819768326
178 | 0176 0.028544575 0.003826141357421875 0.8535764889512887
179 | 0177 0.02826317 0.0035789012908935547 0.8482900420409013
180 | 0178 0.028624818 0.004084110260009766 0.8523729591690528
181 | 0179 0.02854348 0.003498077392578125 0.8538706471402988
182 | 0180 0.028264817 0.0029790401458740234 0.852909259062121
183 | 0181 0.028231572 0.002826213836669922 0.8487360474025423
184 | 0182 0.028396517 0.002786874771118164 0.8540621007098764
185 | 0183 0.028273262 0.0033011436462402344 0.850210402841554
186 | 0184 0.028162532 0.0076601505279541016 0.8481864838937321
187 | 0185 0.028235652 0.005504131317138672 0.850534247310745
188 | 0186 0.028210642 0.005777120590209961 0.8501197441945392
189 | 0187 0.02806191 0.004815816879272461 0.8463054265600394
190 | 0188 0.028206997 0.0036389827728271484 0.8510986052216867
191 | 0189 0.02819692 0.0035970211029052734 0.8485067639272875
192 | 0190 0.028049704 0.003609180450439453 0.8482083249229826
193 | 0191 0.028205978 0.0033190250396728516 0.84861498968098
194 | 0192 0.028218614 0.003242015838623047 0.850143179647457
195 | 0193 0.028134603 0.003905057907104492 0.8481868874813632
196 | 0194 0.028238378 0.003247976303100586 0.8471261018803629
197 | 0195 0.027921095 0.0029959678649902344 0.8437578442164527
198 | 0196 0.028389763 0.003998994827270508 0.8496154827171447
199 | 0197 0.028189283 0.0038750171661376953 0.8452732598233761
200 | 0198 0.02830593 0.003278017044067383 0.8463975754634707
201 | 0199 0.02808813 0.002858877182006836 0.8430319445285184
202 | 0200 0.028243097 0.002544879913330078 0.8494095015530452
203 | 0201 0.028250888 0.002505064010620117 0.8499622510497815
204 | 0202 0.028266374 0.0056760311126708984 0.8469662222251939
205 | 0203 0.028246243 0.006226778030395508 0.8447417444161133
206 | 0204 0.028307207 0.004395008087158203 0.851238164162694
207 | 0205 0.028203798 0.005591869354248047 0.844320372448102
208 | 0206 0.027956452 0.004940032958984375 0.8423006808238123
209 | 0207 0.028092796 0.0037093162536621094 0.8436104595127039
210 | 0208 0.028097212 0.003631114959716797 0.8450106222718693
211 | 0209 0.028031833 0.003542184829711914 0.8453387260797688
212 | 0210 0.02830377 0.003330707550048828 0.8445741293520664
213 | 0211 0.02808189 0.003704071044921875 0.8423057524792625
214 | 0212 0.028036155 0.003824949264526367 0.8405209637196476
215 | 0213 0.028190356 0.0032362937927246094 0.8448830742450144
216 | 0214 0.028063841 0.0035729408264160156 0.8453514766582544
217 | 0215 0.028296746 0.00406193733215332 0.8445205488642515
218 | 0216 0.028291583 0.003580808639526367 0.8464393691036539
219 | 0217 0.02808912 0.0032160282135009766 0.8454501612767857
220 | 0218 0.028098725 0.0030939579010009766 0.8423149034752199
221 | 0219 0.028172396 0.002565145492553711 0.8442811429336392
222 | 0220 0.027938426 0.005608797073364258 0.8420386309727597
223 | 0221 0.028056227 0.006207942962646484 0.8429699011038438
224 | 0222 0.028080316 0.0052509307861328125 0.8416656714476054
225 | 0223 0.027961139 0.0061740875244140625 0.8411094580645053
226 | 0224 0.028093472 0.004173755645751953 0.8438504534039972
227 | 0225 0.028114531 0.003677845001220703 0.8439001966173245
228 | 0226 0.02801264 0.003595113754272461 0.8439590777453155
229 | 0227 0.028150575 0.0035431385040283203 0.8444116086946805
230 | 0228 0.028127896 0.0034580230712890625 0.8426481880999666
231 | 0229 0.028007727 0.004271984100341797 0.8388349354003669
232 | 0230 0.027967982 0.0034029483795166016 0.8400148923558007
233 | 0231 0.028018229 0.0032460689544677734 0.8412910612788218
234 | 0232 0.027992506 0.003450155258178711 0.8392425925720468
235 | 0233 0.028193908 0.004181861877441406 0.8431655524533006
236 | 0234 0.028064806 0.0034818649291992188 0.8414577716660843
237 | 0235 0.028100539 0.002830028533935547 0.8395570747781984
238 | 0236 0.028088221 0.0028150081634521484 0.8420106156068259
239 | 0237 0.028155712 0.0027370452880859375 0.8429616700711717
240 | 0238 0.027994005 0.005131959915161133 0.843654177069389
241 | 0239 0.02809978 0.006596803665161133 0.8418107590449864
242 | 0240 0.028145373 0.0050640106201171875 0.8434290282649788
243 | 0241 0.027995534 0.006562232971191406 0.8390144489968185
244 | 0242 0.02802014 0.004786968231201172 0.8413079704795166
245 | 0243 0.02813604 0.0036971569061279297 0.8410716631226266
246 | 0244 0.027931456 0.003306865692138672 0.8406064463465039
247 | 0245 0.028034348 0.0036280155181884766 0.8401751084584345
248 | 0246 0.028136622 0.0035657882690429688 0.8441718855188768
249 | 0247 0.027909411 0.004049062728881836 0.8399625187203589
250 | 0248 0.02800063 0.003329038619995117 0.8405915631420908
251 | 0249 0.02817056 0.0034558773040771484 0.845746486485665
252 | 0250 0.02810134 0.003632068634033203 0.8440653911375857
253 | 0251 0.028023295 0.0039980411529541016 0.8442447608602626
254 | 0252 0.028051864 0.0036749839782714844 0.8422235094973627
255 | 0253 0.028053237 0.0030498504638671875 0.8435514457747151
256 | 0254 0.028056022 0.002730846405029297 0.839759349998279
257 | 0255 0.027891465 0.0027840137481689453 0.8399170282524635
258 | 0256 0.028080132 0.005417346954345703 0.8450254790737903
259 | 0257 0.028212208 0.005260944366455078 0.8433398432295555
260 | 0258 0.027929585 0.005143880844116211 0.8380774200535248
261 | 0259 0.02799796 0.006044149398803711 0.8350714803706115
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263 | 0261 0.027921395 0.003621816635131836 0.8360244308046358
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812 | 0810 0.027981319 0.0036559104919433594 0.8354245087141995
813 | 0811 0.02780215 0.003922939300537109 0.8315994737531033
814 | 0812 0.027871292 0.00357818603515625 0.8301014364429238
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846 | 0844 0.02806734 0.0035681724548339844 0.8383001340900287
847 | 0845 0.02799854 0.003262042999267578 0.8340178047705065
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859 | 0857 0.027888678 0.003422975540161133 0.8294850830789999
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872 | 0870 0.02798155 0.002504110336303711 0.8292014667363814
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883 | 0881 0.027934175 0.0034952163696289062 0.8325737162524087
884 | 0882 0.02795688 0.0031096935272216797 0.8313796162311168
885 | 0883 0.02806671 0.0033397674560546875 0.8351771950165658
886 | 0884 0.02769934 0.004026174545288086 0.8290349758014841
887 | 0885 0.027746562 0.0035300254821777344 0.8327868627388029
888 | 0886 0.027807131 0.003185272216796875 0.8334953348058076
889 | 0887 0.027921028 0.002937793731689453 0.8367307296662658
890 | 0888 0.028005932 0.0029320716857910156 0.8394048604496195
891 | 0889 0.027870063 0.005793094635009766 0.8367558217620445
892 | 0890 0.027874809 0.008326053619384766 0.8383592472033398
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896 | 0894 0.02801802 0.003487825393676758 0.8382534379039329
897 | 0895 0.02759356 0.003525972366333008 0.8200125317164764
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900 | 0898 0.02782498 0.003820180892944336 0.816388831993107
901 | 0899 0.0277796 0.0033779144287109375 0.817675454700477
902 | 0900 0.027949069 0.003038167953491211 0.8229369345204922
903 | 0901 0.02789054 0.0035109519958496094 0.8177370685357379
904 | 0902 0.027803436 0.003968000411987305 0.8222834909201912
905 | 0903 0.0277466 0.0030298233032226562 0.8273610675586529
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907 | 0905 0.027855948 0.002607107162475586 0.8381941353362328
908 | 0906 0.02811972 0.002610921859741211 0.8378720653299438
909 | 0907 0.027883954 0.0025908946990966797 0.8406310672089592
910 | 0908 0.02773277 0.006561994552612305 0.8328028722225626
911 | 0909 0.028015848 0.005544900894165039 0.8351810866562381
912 | 0910 0.02755621 0.004761695861816406 0.8236717982090556
913 | 0911 0.027845656 0.005657672882080078 0.8352208074885035
914 | 0912 0.027713519 0.00401616096496582 0.8286590420624608
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916 | 0914 0.02792779 0.0034389495849609375 0.829306943889927
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920 | 0918 0.027707033 0.0036780834197998047 0.8206404582640994
921 | 0919 0.02797746 0.003186941146850586 0.828118099128526
922 | 0920 0.027857302 0.0036580562591552734 0.8259933712075402
923 | 0921 0.027854241 0.00397801399230957 0.8253143298727672
924 | 0922 0.027700806 0.0032460689544677734 0.8239165848228481
925 | 0923 0.02807712 0.0028281211853027344 0.8389963860635357
926 | 0924 0.02772094 0.002701997756958008 0.8349436093450782
927 | 0925 0.027711112 0.0026099681854248047 0.8313928526340493
928 | 0926 0.02786509 0.005197763442993164 0.83590593309511
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930 | 0928 0.027870711 0.005427122116088867 0.8362978653266229
931 | 0929 0.02778231 0.0060498714447021484 0.8303124768523827
932 | 0930 0.027918745 0.00397801399230957 0.8359024730564626
933 | 0931 0.027876252 0.0035812854766845703 0.8310869513089596
934 | 0932 0.028022155 0.0035288333892822266 0.8390758036672936
935 | 0933 0.028067863 0.0034999847412109375 0.8347801533329651
936 | 0934 0.027866216 0.0033681392669677734 0.8328149268721015
937 | 0935 0.027822282 0.003740072250366211 0.8304786272664844
938 | 0936 0.027945086 0.003735065460205078 0.8284349707845147
939 | 0937 0.027619174 0.0033369064331054688 0.8196250211352523
940 | 0938 0.028050803 0.003583192825317383 0.8371030359362497
941 | 0939 0.027781405 0.0040090084075927734 0.8294483753911672
942 | 0940 0.027836453 0.0035240650177001953 0.8347459872838461
943 | 0941 0.02787608 0.0031430721282958984 0.8281075493603496
944 | 0942 0.027724735 0.002664804458618164 0.8267325618429148
945 | 0943 0.027751766 0.0026960372924804688 0.8276976627710237
946 | 0944 0.027582768 0.0045931339263916016 0.8266089169285313
947 | 0945 0.027611986 0.006933927536010742 0.823346872531044
948 | 0946 0.027624633 0.00516200065612793 0.8239976279830041
949 | 0947 0.027962228 0.006350040435791016 0.8342946178311887
950 | 0948 0.027885957 0.00506281852722168 0.8307778577361755
951 | 0949 0.027822107 0.0037000179290771484 0.8305014407243516
952 | 0950 0.027839743 0.0037407875061035156 0.8268128981648541
953 | 0951 0.0278567 0.003473997116088867 0.8303237335332612
954 | 0952 0.027739387 0.0033080577850341797 0.822538601540217
955 | 0953 0.02796164 0.0038530826568603516 0.8323513848781452
956 | 0954 0.028106399 0.0032765865325927734 0.8400439566466252
957 | 0955 0.028031494 0.003056049346923828 0.8447946367160799
958 | 0956 0.02796605 0.003381967544555664 0.8361428727966662
959 | 0957 0.027999675 0.004086017608642578 0.8387333085297062
960 | 0958 0.02785923 0.003175973892211914 0.8349627706389506
961 | 0959 0.028077971 0.0033767223358154297 0.8327497638101757
962 | 0960 0.027988892 0.0032477378845214844 0.8253283385606516
963 | 0961 0.02792597 0.0030019283294677734 0.8299473173336036
964 | 0962 0.028263919 0.005881309509277344 0.8345332779617565
965 | 0963 0.027889762 0.006916999816894531 0.8201704517858108
966 | 0964 0.027800754 0.005110025405883789 0.8075534902815941
967 | 0965 0.028075982 0.006021022796630859 0.8151885696527672
968 | 0966 0.027959645 0.004203081130981445 0.8195784998920818
969 | 0967 0.027949702 0.003882884979248047 0.8272761443654868
970 | 0968 0.027752303 0.0035741329193115234 0.825330932479249
971 | 0969 0.027726658 0.00356292724609375 0.8283165755273043
972 | 0970 0.028018916 0.003312826156616211 0.8366849701709524
973 | 0971 0.027877629 0.003802061080932617 0.8369866625450526
974 | 0972 0.027747404 0.003453969955444336 0.8367861813238073
975 | 0973 0.028103767 0.003339052200317383 0.8504605819681891
976 | 0974 0.027787384 0.0033872127532958984 0.8365439349620947
977 | 0975 0.027960453 0.004022836685180664 0.8405864446721378
978 | 0976 0.027945904 0.003294229507446289 0.8354710371754449
979 | 0977 0.02783828 0.002916097640991211 0.8358342491682434
980 | 0978 0.027873922 0.0026900768280029297 0.832348921484107
981 | 0979 0.028103285 0.0026721954345703125 0.8385469167157449
982 | 0980 0.027772903 0.005795001983642578 0.8311810149090337
983 | 0981 0.027719567 0.0053141117095947266 0.831230260912652
984 | 0982 0.028196238 0.004619121551513672 0.8355993328595943
985 | 0983 0.027914502 0.0065310001373291016 0.8297110996157213
986 | 0984 0.027865805 0.004281044006347656 0.8335783055672022
987 | 0985 0.02777072 0.0036458969116210938 0.8293460548844687
988 | 0986 0.027665619 0.0036711692810058594 0.8321937700935848
989 | 0987 0.02799322 0.0035707950592041016 0.8377149797710199
990 | 0988 0.027923666 0.003325939178466797 0.834677570558258
991 | 0989 0.027983423 0.003751993179321289 0.8404447996250016
992 | 0990 0.027803946 0.0034401416778564453 0.8326137656917718
993 | 0991 0.027850728 0.0031719207763671875 0.8326099313758666
994 | 0992 0.02786529 0.0034589767456054688 0.8313813275619686
995 | 0993 0.027729785 0.004249095916748047 0.8305298653158414
996 | 0994 0.027776081 0.0033559799194335938 0.8248353318544409
997 | 0995 0.02783361 0.0029039382934570312 0.8269179512123539
998 | 0996 0.027784115 0.0026297569274902344 0.8210843401447167
999 | 0997 0.028109808 0.0025627613067626953 0.830644271584071
1000 | 0998 0.027921844 0.004399776458740234 0.8312746740796464
1001 | 0999 0.027728144 0.007276058197021484 0.8215907909941915
1002 |
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/assets/out_MD_LSTM.tsv:
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1 | steps_MD_LSTM overall_loss_MD_LSTM time_MD_LSTM relevant_loss_MD_LSTM
2 | 0000 0.24492839 0.7478148937225342 0.1982293610987767
3 | 0001 0.11369451 0.06525993347167969 0.4093332954068995
4 | 0002 0.08006727 0.06498885154724121 0.5058314370636465
5 | 0003 0.06533909 0.06403779983520508 0.5740046153598379
6 | 0004 0.0558106 0.06518793106079102 0.6141264361579866
7 | 0005 0.049886983 0.06496262550354004 0.6532773903780447
8 | 0006 0.0444355 0.06593990325927734 0.6836362099838689
9 | 0007 0.041533586 0.06383013725280762 0.7187649391371922
10 | 0008 0.039179984 0.06627297401428223 0.7427241978921335
11 | 0009 0.037311308 0.06420707702636719 0.768492595022398
12 | 0010 0.03648377 0.06388306617736816 0.7762760990515265
13 | 0011 0.03497373 0.0675349235534668 0.7732017334332886
14 | 0012 0.033909686 0.06693601608276367 0.7867994603995911
15 | 0013 0.033516973 0.0668938159942627 0.8035717827361665
16 | 0014 0.03292703 0.06831097602844238 0.8092649281862478
17 | 0015 0.030946126 0.06518721580505371 0.8169326025716511
18 | 0016 0.031924 0.06519818305969238 0.8222035062875708
19 | 0017 0.0308168 0.06522178649902344 0.8346114038962769
20 | 0018 0.03154654 0.06458401679992676 0.8362648364781811
21 | 0019 0.03100736 0.0868377685546875 0.8442167887761209
22 | 0020 0.030852076 0.0896768569946289 0.8490088453112712
23 | 0021 0.030333426 0.06461024284362793 0.854973731083843
24 | 0022 0.030361356 0.06439781188964844 0.8600199821882744
25 | 0023 0.029747717 0.06708097457885742 0.864286528354878
26 | 0024 0.030412307 0.0652608871459961 0.8675320452259743
27 | 0025 0.029573431 0.0657510757446289 0.8675851800226064
28 | 0026 0.0302605 0.0654599666595459 0.8734688112655744
29 | 0027 0.029642675 0.06491899490356445 0.8717507810024985
30 | 0028 0.029421004 0.06375885009765625 0.8771337185943386
31 | 0029 0.0297351 0.06405019760131836 0.8790719626036128
32 | 0030 0.0292309 0.06424927711486816 0.8814137130210676
33 | 0031 0.029799817 0.06375885009765625 0.8786395772783898
34 | 0032 0.029647596 0.0650949478149414 0.8812046727087022
35 | 0033 0.0295749 0.08528995513916016 0.8815248670894383
36 | 0034 0.029897586 0.06716418266296387 0.8833354516914227
37 | 0035 0.028874809 0.07294702529907227 0.8786298753010661
38 | 0036 0.029401083 0.07699799537658691 0.8758281692668711
39 | 0037 0.029188916 0.07445096969604492 0.8751925994895902
40 | 0038 0.029420126 0.0814201831817627 0.8761924272467496
41 | 0039 0.029277379 0.07209491729736328 0.8791179965882063
42 | 0040 0.029316355 0.07305598258972168 0.8770119244167255
43 | 0041 0.029420182 0.07585930824279785 0.8747723575400415
44 | 0042 0.02943873 0.0739278793334961 0.8737895196360671
45 | 0043 0.02918459 0.07755494117736816 0.8674994351928245
46 | 0044 0.02917278 0.07760000228881836 0.868612975763065
47 | 0045 0.028948288 0.07301497459411621 0.8676053499203863
48 | 0046 0.029153341 0.07767391204833984 0.8653141283306411
49 | 0047 0.029071387 0.07365822792053223 0.8638901957768075
50 | 0048 0.028887771 0.0762948989868164 0.8633401675704813
51 | 0049 0.028686758 0.0740668773651123 0.859665652642522
52 | 0050 0.02909645 0.07660293579101562 0.8609468245108349
53 | 0051 0.029057296 0.08469510078430176 0.8617292749874359
54 | 0052 0.028830778 0.07785916328430176 0.8529807314124711
55 | 0053 0.028880758 0.07242822647094727 0.8609957468421198
56 | 0054 0.028968137 0.0769500732421875 0.8624733794953094
57 | 0055 0.02869536 0.07767772674560547 0.8506994781676188
58 | 0056 0.028693497 0.07940292358398438 0.862132313420936
59 | 0057 0.02863172 0.0729517936706543 0.8582330580407185
60 | 0058 0.028842464 0.07761979103088379 0.8586895451658312
61 | 0059 0.028690219 0.08006811141967773 0.8647348083475712
62 | 0060 0.028682603 0.07138490676879883 0.8589903091401148
63 | 0061 0.02848846 0.07231616973876953 0.856747002210323
64 | 0062 0.028740117 0.07722592353820801 0.8574671502354674
65 | 0063 0.02866602 0.07671117782592773 0.8626803385814301
66 | 0064 0.028611742 0.07431817054748535 0.8538320351204881
67 | 0065 0.028469276 0.0773928165435791 0.8586659680017947
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--------------------------------------------------------------------------------
/assets/out_SNAKE_SD_LSTM.tsv:
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1 | steps_SNAKE_SD_LSTM overall_loss_SNAKE_SD_LSTM time_SNAKE_SD_LSTM relevant_loss_SNAKE_SD_LSTM
2 | 0000 0.20431629 0.1272599697113037 0.25014039065014787
3 | 0001 0.1936512 0.015352010726928711 0.26652705342392025
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730 | 0728 0.016957894 0.01723313331604004 0.08224343956174718
731 | 0729 0.017449891 0.02223801612854004 0.11742798851922165
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733 | 0731 0.016779792 0.01707315444946289 0.03131490064134579
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736 | 0734 0.017622992 0.018965959548950195 0.14571885274506957
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782 | 0780 0.01667388 0.023801803588867188 0.018294401373719182
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807 | 0805 0.016925747 0.0168612003326416 0.09125582315743919
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831 | 0829 0.015892036 0.020397186279296875 0.05692841076162014
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863 | 0861 0.01656138 0.014799833297729492 0.01417519355474317
864 | 0862 0.015800767 0.016165971755981445 0.018061337192268434
865 | 0863 0.016953789 0.060604095458984375 0.12727183554323873
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867 | 0865 0.01693577 0.027386188507080078 0.03183446192266848
868 | 0866 0.01725706 0.021249055862426758 0.008725934571719884
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876 | 0874 0.015840353 0.018613100051879883 0.007399096159106833
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880 | 0878 0.015638921 0.01922297477722168 0.0247727164188567
881 | 0879 0.016587188 0.016176223754882812 0.008917005273365364
882 | 0880 0.016785484 0.016214847564697266 0.010658199560426018
883 | 0881 0.016031474 0.0210721492767334 0.013173456495190639
884 | 0882 0.01609321 0.025302648544311523 0.0469951060920335
885 | 0883 0.017446117 0.017348051071166992 0.14280226112112676
886 | 0884 0.016215956 0.016360998153686523 0.04889519992125391
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892 | 0890 0.015804013 0.026670217514038086 0.037241886021649906
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896 | 0894 0.015581757 0.027080297470092773 0.013438440037646604
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900 | 0898 0.017086802 0.039858102798461914 0.122857393304169
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905 | 0903 0.015701339 0.018297910690307617 0.015860693758672495
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907 | 0905 0.015725525 0.02025294303894043 0.01478745827129102
908 | 0906 0.016001265 0.0208590030670166 0.012957194990339538
909 | 0907 0.015314406 0.017061233520507812 0.010999998208416129
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937 | 0935 0.015372824 0.022109270095825195 0.01859418945535385
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945 | 0943 0.015201138 0.020342111587524414 0.010198257439106184
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953 | 0951 0.015308169 0.02187800407409668 0.013113471051421355
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957 | 0955 0.015784223 0.02163100242614746 0.004395535197148304
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961 | 0959 0.015667241 0.02178502082824707 0.04589820734821487
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967 | 0965 0.016167397 0.016490936279296875 0.015297287297385997
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969 | 0967 0.01618402 0.017129182815551758 0.06855412654157833
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973 | 0971 0.0170485 0.014194011688232422 0.004954019483218941
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978 | 0976 0.015181182 0.026697874069213867 0.018556962783879083
979 | 0977 0.015605984 0.019462108612060547 0.006156427918446505
980 | 0978 0.015957525 0.016114234924316406 0.0036329555429777116
981 | 0979 0.015589893 0.015330791473388672 0.007070626607098118
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985 | 0983 0.015754096 0.018304824829101562 0.05581227325518273
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987 | 0985 0.015472369 0.026198863983154297 0.004023160258102232
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990 | 0988 0.015354905 0.020101308822631836 0.008851495053732172
991 | 0989 0.01585142 0.021899938583374023 0.04560335193477366
992 | 0990 0.015710875 0.0159761905670166 0.03628493426440915
993 | 0991 0.015732355 0.017007827758789062 0.05440917967537695
994 | 0992 0.015248211 0.028002023696899414 0.007686914545640633
995 | 0993 0.015481521 0.017353057861328125 0.007122648236732809
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997 | 0995 0.015544686 0.015681982040405273 0.004729515639316784
998 | 0996 0.0151973115 0.019881248474121094 0.015444526431102501
999 | 0997 0.015204936 0.01896810531616211 0.00941731730205797
1000 | 0998 0.015374982 0.01620316505432129 0.016822768073663674
1001 | 0999 0.015812743 0.020589828491210938 0.05218287515122022
1002 |
--------------------------------------------------------------------------------
/data_gen_gaussian_fields.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 |
3 |
4 | def fft_ind_gen(n):
5 | a = list(range(0, int(n / 2 + 1)))
6 | b = list(range(1, int(n / 2)))
7 | b.reverse()
8 | b = [-i for i in b]
9 | return a + b
10 |
11 |
12 | def gaussian_random_field(pk=lambda k: k ** -3.0, size1=100, size2=100, anisotropy=True):
13 | def pk2(kx_, ky_):
14 | if kx_ == 0 and ky_ == 0:
15 | return 0.0
16 | if anisotropy:
17 | if kx_ != 0 and ky_ != 0:
18 | return 0.0
19 | return np.sqrt(pk(np.sqrt(kx_ ** 2 + ky_ ** 2)))
20 |
21 | noise = np.fft.fft2(np.random.normal(size=(size1, size2)))
22 | amplitude = np.zeros((size1, size2))
23 | for i, kx in enumerate(fft_ind_gen(size1)):
24 | for j, ky in enumerate(fft_ind_gen(size2)):
25 | amplitude[i, j] = pk2(kx, ky)
26 | return np.fft.ifft2(noise * amplitude)
27 |
28 |
29 | def next_batch(bs, h, w, anisotropy=True):
30 | x = []
31 | for i in range(bs):
32 | o = gaussian_random_field(pk=lambda k: k ** -4.0, size1=h, size2=w, anisotropy=anisotropy).real
33 | x.append(o)
34 | x = np.array(x)
35 | y = np.roll(x, shift=-1, axis=2)
36 | y[:, :, -1] = 0.0
37 | return x, y
38 |
39 |
40 | if __name__ == '__main__':
41 | import matplotlib.pyplot as plt
42 |
43 | for alpha in [-4.0]:
44 | out = gaussian_random_field(pk=lambda k: k ** alpha, size1=32, size2=32, anisotropy=True)
45 | plt.figure()
46 | plt.imshow(out.real, cmap='jet', interpolation='none')
47 | plt.show()
48 |
49 | # anisotropy: vertical or horizontal
50 | # so having a LSTM vertical or a LSTM horizontal is sufficient
51 |
52 | # isotropy: random gaussian fields have terms that depend on all the directions (not only horizontal or vertical)
53 | # so MD LSTM should help there.
54 |
--------------------------------------------------------------------------------
/data_random_short_diagonal.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 |
3 | """
4 |
5 | ____________
6 | | |
7 | | x |
8 | | x |
9 | | |
10 | |__________|
11 |
12 |
13 | ____________
14 | | |
15 | | |
16 | | x |
17 | | x |
18 | |__________|
19 |
20 | ____________
21 | | |
22 | | x |
23 | | x |
24 | | |
25 | |__________|
26 |
27 | Just consider the loss here of the bottom.
28 |
29 | A simple vertical/horizontal cannot predict both x = very high loss.
30 | A Grid LSTM cannot read from the TOP_LEFT corner. It cannot predict the first x,
31 | But can definitely use the information of the first x to predict with 100% the second.
32 |
33 | """
34 |
35 |
36 | def random_short_diagonal_matrix(h, w):
37 | m = np.random.uniform(low=0.0, high=0.1, size=(h, w))
38 |
39 | x1_x = np.random.randint(low=1, high=w - 1)
40 | x1_y = np.random.randint(low=1, high=h - 1)
41 |
42 | x2_x = x1_x + 1
43 | x2_y = x1_y + 1
44 |
45 | m[x1_x, x1_y] = 1.0
46 | m[x2_x, x2_y] = 1.0
47 |
48 | return m
49 |
50 |
51 | def next_batch(bs, h, w):
52 | x = [random_short_diagonal_matrix(h, w) for i in range(bs)]
53 | x = np.array(x)
54 | y = np.roll(x, shift=-1, axis=2)
55 | t = get_relevant_prediction_index(y)
56 | return x, y, t
57 |
58 |
59 | def visualise_mat(m):
60 | import matplotlib.pyplot as plt
61 | plt.figure()
62 | plt.imshow(m.real, cmap='jet', interpolation='none')
63 | plt.show()
64 |
65 |
66 | def find_target_for_matrix(y_):
67 | w_y = np.where(y_ == 1)[1][1]
68 | h_y = np.where(y_ == 1)[0][1]
69 | return w_y, h_y
70 |
71 |
72 | def get_relevant_prediction_index(y_):
73 | return np.array([find_target_for_matrix(yy_) for yy_ in y_])
74 |
75 |
76 | if __name__ == '__main__':
77 | x_, y_, t_ = next_batch(bs=1, h=32, w=32)
78 | visualise_mat(x_[0])
79 | visualise_mat(y_[0])
80 |
--------------------------------------------------------------------------------
/md_lstm.py:
--------------------------------------------------------------------------------
1 | import tensorflow as tf
2 | from tensorflow.contrib.rnn import RNNCell, LSTMStateTuple
3 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell import _linear
4 | from tensorflow.python.ops.rnn import dynamic_rnn
5 |
6 |
7 | def ln(tensor, scope=None, epsilon=1e-5):
8 | """ Layer normalizes a 2D tensor along its second axis """
9 | assert (len(tensor.get_shape()) == 2)
10 | m, v = tf.nn.moments(tensor, [1], keep_dims=True)
11 | if not isinstance(scope, str):
12 | scope = ''
13 | with tf.variable_scope(scope + 'layer_norm'):
14 | scale = tf.get_variable('scale',
15 | shape=[tensor.get_shape()[1]],
16 | initializer=tf.constant_initializer(1))
17 | shift = tf.get_variable('shift',
18 | shape=[tensor.get_shape()[1]],
19 | initializer=tf.constant_initializer(0))
20 | ln_initial = (tensor - m) / tf.sqrt(v + epsilon)
21 |
22 | return ln_initial * scale + shift
23 |
24 |
25 | class MultiDimensionalLSTMCell(RNNCell):
26 | """
27 | Adapted from TF's BasicLSTMCell to use Layer Normalization.
28 | Note that state_is_tuple is always True.
29 | """
30 |
31 | def __init__(self, num_units, forget_bias=0.0, activation=tf.nn.tanh):
32 | self._num_units = num_units
33 | self._forget_bias = forget_bias
34 | self._activation = activation
35 |
36 | @property
37 | def state_size(self):
38 | return LSTMStateTuple(self._num_units, self._num_units)
39 |
40 | @property
41 | def output_size(self):
42 | return self._num_units
43 |
44 | def __call__(self, inputs, state, scope=None):
45 | """Long short-term memory cell (LSTM).
46 | @param: inputs (batch,n)
47 | @param state: the states and hidden unit of the two cells
48 | """
49 | with tf.variable_scope(scope or type(self).__name__):
50 | c1, c2, h1, h2 = state
51 |
52 | # change bias argument to False since LN will add bias via shift
53 | concat = _linear([inputs, h1, h2], 5 * self._num_units, False)
54 |
55 | i, j, f1, f2, o = tf.split(value=concat, num_or_size_splits=5, axis=1)
56 |
57 | # add layer normalization to each gate
58 | i = ln(i, scope='i/')
59 | j = ln(j, scope='j/')
60 | f1 = ln(f1, scope='f1/')
61 | f2 = ln(f2, scope='f2/')
62 | o = ln(o, scope='o/')
63 |
64 | new_c = (c1 * tf.nn.sigmoid(f1 + self._forget_bias) +
65 | c2 * tf.nn.sigmoid(f2 + self._forget_bias) + tf.nn.sigmoid(i) *
66 | self._activation(j))
67 |
68 | # add layer_normalization in calculation of new hidden state
69 | new_h = self._activation(ln(new_c, scope='new_h/')) * tf.nn.sigmoid(o)
70 | new_state = LSTMStateTuple(new_c, new_h)
71 |
72 | return new_h, new_state
73 |
74 |
75 | def multi_dimensional_rnn_while_loop(rnn_size, input_data, sh, dims=None, scope_n="layer1"):
76 | """Implements naive multi dimension recurrent neural networks
77 |
78 | @param rnn_size: the hidden units
79 | @param input_data: the data to process of shape [batch,h,w,channels]
80 | @param sh: [height,width] of the windows
81 | @param dims: dimensions to reverse the input data,eg.
82 | dims=[False,True,True,False] => true means reverse dimension
83 | @param scope_n : the scope
84 |
85 | returns [batch,h/sh[0],w/sh[1],rnn_size] the output of the lstm
86 | """
87 |
88 | with tf.variable_scope("MultiDimensionalLSTMCell-" + scope_n):
89 |
90 | # Create multidimensional cell with selected size
91 | cell = MultiDimensionalLSTMCell(rnn_size)
92 |
93 | # Get the shape of the input (batch_size, x, y, channels)
94 | batch_size, X_dim, Y_dim, channels = input_data.shape.as_list()
95 | # Window size
96 | X_win, Y_win = sh
97 | # Get the runtime batch size
98 | batch_size_runtime = tf.shape(input_data)[0]
99 |
100 | # If the input cannot be exactly sampled by the window, we patch it with zeros
101 | if X_dim % X_win != 0:
102 | # Get offset size
103 | offset = tf.zeros([batch_size_runtime, X_win - (X_dim % X_win), Y_dim, channels])
104 | # Concatenate X dimension
105 | input_data = tf.concat(axis=1, values=[input_data, offset])
106 | # Update shape value
107 | X_dim = input_data.shape[1].value
108 |
109 | # The same but for Y axis
110 | if Y_dim % Y_win != 0:
111 | # Get offset size
112 | offset = tf.zeros([batch_size_runtime, X_dim, Y_win - (Y_dim % Y_win), channels])
113 | # Concatenate Y dimension
114 | input_data = tf.concat(axis=2, values=[input_data, offset])
115 | # Update shape value
116 | Y_dim = input_data.shape[2].value
117 |
118 | # Get the steps to perform in X and Y axis
119 | h, w = int(X_dim / X_win), int(Y_dim / Y_win)
120 |
121 | # Get the number of features (total number of input values per step)
122 | features = Y_win * X_win * channels
123 |
124 | # Reshape input data to a tensor containing the step indexes and features inputs
125 | # The batch size is inferred from the tensor size
126 | x = tf.reshape(input_data, [batch_size_runtime, h, w, features])
127 |
128 | # Reverse the selected dimensions
129 | if dims is not None:
130 | assert dims[0] is False and dims[3] is False
131 | x = tf.reverse(x, dims)
132 |
133 | # Reorder inputs to (h, w, batch_size, features)
134 | x = tf.transpose(x, [1, 2, 0, 3])
135 | # Reshape to a one dimensional tensor of (h*w*batch_size , features)
136 | x = tf.reshape(x, [-1, features])
137 | # Split tensor into h*w tensors of size (batch_size , features)
138 | x = tf.split(axis=0, num_or_size_splits=h * w, value=x)
139 |
140 | # Create an input tensor array (literally an array of tensors) to use inside the loop
141 | inputs_ta = tf.TensorArray(dtype=tf.float32, size=h * w, name='input_ta')
142 | # Unstack the input X in the tensor array
143 | inputs_ta = inputs_ta.unstack(x)
144 | # Create an input tensor array for the states
145 | states_ta = tf.TensorArray(dtype=tf.float32, size=h * w + 1, name='state_ta', clear_after_read=False)
146 | # And an other for the output
147 | outputs_ta = tf.TensorArray(dtype=tf.float32, size=h * w, name='output_ta')
148 |
149 | # initial cell hidden states
150 | # Write to the last position of the array, the LSTMStateTuple filled with zeros
151 | states_ta = states_ta.write(h * w, LSTMStateTuple(tf.zeros([batch_size_runtime, rnn_size], tf.float32),
152 | tf.zeros([batch_size_runtime, rnn_size], tf.float32)))
153 |
154 | # Function to get the sample skipping one row
155 | def get_up(t_, w_):
156 | return t_ - tf.constant(w_)
157 |
158 | # Function to get the previous sample
159 | def get_last(t_, w_):
160 | return t_ - tf.constant(1)
161 |
162 | # Controls the initial index
163 | time = tf.constant(0)
164 | zero = tf.constant(0)
165 |
166 | # Body of the while loop operation that applies the MD LSTM
167 | def body(time_, outputs_ta_, states_ta_):
168 |
169 | # If the current position is less or equal than the width, we are in the first row
170 | # and we need to read the zero state we added in row (h*w).
171 | # If not, get the sample located at a width distance.
172 | state_up = tf.cond(tf.less_equal(time_, tf.constant(w)),
173 | lambda: states_ta_.read(h * w),
174 | lambda: states_ta_.read(get_up(time_, w)))
175 |
176 | # If it is the first step we read the zero state if not we read the immediate last
177 | state_last = tf.cond(tf.less(zero, tf.mod(time_, tf.constant(w))),
178 | lambda: states_ta_.read(get_last(time_, w)),
179 | lambda: states_ta_.read(h * w))
180 |
181 | # We build the input state in both dimensions
182 | current_state = state_up[0], state_last[0], state_up[1], state_last[1]
183 | # Now we calculate the output state and the cell output
184 | out, state = cell(inputs_ta.read(time_), current_state)
185 | # We write the output to the output tensor array
186 | outputs_ta_ = outputs_ta_.write(time_, out)
187 | # And save the output state to the state tensor array
188 | states_ta_ = states_ta_.write(time_, state)
189 |
190 | # Return outputs and incremented time step
191 | return time_ + 1, outputs_ta_, states_ta_
192 |
193 | # Loop output condition. The index, given by the time, should be less than the
194 | # total number of steps defined within the image
195 | def condition(time_, outputs_ta_, states_ta_):
196 | return tf.less(time_, tf.constant(h * w))
197 |
198 | # Run the looped operation
199 | result, outputs_ta, states_ta = tf.while_loop(condition, body, [time, outputs_ta, states_ta],
200 | parallel_iterations=1)
201 |
202 | # Extract the output tensors from the processesed tensor array
203 | outputs = outputs_ta.stack()
204 | states = states_ta.stack()
205 |
206 | # Reshape outputs to match the shape of the input
207 | y = tf.reshape(outputs, [h, w, batch_size_runtime, rnn_size])
208 |
209 | # Reorder te dimensions to match the input
210 | y = tf.transpose(y, [2, 0, 1, 3])
211 | # Reverse if selected
212 | if dims is not None:
213 | y = tf.reverse(y, dims)
214 |
215 | # Return the output and the inner states
216 | return y, states
217 |
218 |
219 | def horizontal_standard_lstm(input_data, rnn_size):
220 | # input is (b, h, w, c)
221 | b, h, w, c = input_data.shape.as_list()
222 | # transpose = swap h and w.
223 | new_input_data = tf.reshape(input_data, (b * h, w, c)) # horizontal.
224 | rnn_out, _ = dynamic_rnn(tf.contrib.rnn.LSTMCell(rnn_size),
225 | inputs=new_input_data,
226 | dtype=tf.float32)
227 | rnn_out = tf.reshape(rnn_out, (b, h, w, rnn_size))
228 | return rnn_out
229 |
230 |
231 | def snake_standard_lstm(input_data, rnn_size):
232 | # input is (b, h, w, c)
233 | b, h, w, c = input_data.shape.as_list()
234 | # transpose = swap h and w.
235 | new_input_data = tf.reshape(input_data, (b, w * h, c)) # snake.
236 | rnn_out, _ = dynamic_rnn(tf.contrib.rnn.LSTMCell(rnn_size),
237 | inputs=new_input_data,
238 | dtype=tf.float32)
239 | rnn_out = tf.reshape(rnn_out, (b, h, w, rnn_size))
240 | return rnn_out
241 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | tensorflow==1.8.0
2 | matplotlib==2.2.2
3 | numpy==1.15.4
4 |
--------------------------------------------------------------------------------
/run.sh:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env bash
2 | rm out.MD_LSTM.txt
3 | rm out.SI_LSTM.txt
4 | export CUDA_VISIBLE_DEVICES=; nohup python3 -u main.py 0 > out.SI_LSTM.txt 2>&1 &
5 | export CUDA_VISIBLE_DEVICES=; nohup python3 -u main.py 1 > out.MD_LSTM.txt 2>&1 &
6 |
--------------------------------------------------------------------------------
/scripts/display.py:
--------------------------------------------------------------------------------
1 | import pandas as pd
2 |
3 | out_hori = pd.read_table('out_HORIZONTAL_SD_LSTM.tsv', sep=' ')
4 | out_md = pd.read_table('out_MD_LSTM.tsv', sep=' ')
5 | out_snake = pd.read_table('out_SNAKE_SD_LSTM.tsv', sep=' ')
6 |
7 | print(out_hori.shape)
8 | print(out_md.shape)
9 | print(out_snake.shape)
10 |
11 | out = pd.DataFrame(pd.concat([out_hori, out_md, out_snake], axis=1))
12 | print(out.shape)
13 |
14 | print(out.columns)
15 |
16 | out.to_csv('out.csv', index=False)
17 |
--------------------------------------------------------------------------------
/trainer.py:
--------------------------------------------------------------------------------
1 | from time import time
2 |
3 | import argparse
4 | import logging
5 | import numpy as np
6 | import tensorflow.contrib.slim as slim
7 | from enum import Enum
8 |
9 | from data_random_short_diagonal import next_batch, visualise_mat, get_relevant_prediction_index
10 | from md_lstm import *
11 |
12 | logger = logging.getLogger(__name__)
13 |
14 |
15 | def get_script_arguments():
16 | parser = argparse.ArgumentParser(description='MD LSTM trainer.')
17 | parser.add_argument('--model_type', required=True, type=ModelType.from_string,
18 | choices=list(ModelType), help='Model type.')
19 | parser.add_argument('--enable_plotting', action='store_true')
20 |
21 | args = get_arguments(parser)
22 | logger.info('Script inputs: {}.'.format(args))
23 | return args
24 |
25 |
26 | class FileLogger(object):
27 | def __init__(self, full_filename, headers):
28 | self._headers = headers
29 | self._out_fp = open(full_filename, 'w')
30 | self._write(headers)
31 |
32 | def write(self, line):
33 | assert len(line) == len(self._headers)
34 | self._write(line)
35 |
36 | def close(self):
37 | self._out_fp.close()
38 |
39 | def _write(self, arr):
40 | arr = [str(e) for e in arr]
41 | self._out_fp.write(' '.join(arr) + '\n')
42 | self._out_fp.flush()
43 |
44 |
45 | class ModelType(Enum):
46 | MD_LSTM = 'MD_LSTM'
47 | HORIZONTAL_SD_LSTM = 'HORIZONTAL_SD_LSTM'
48 | SNAKE_SD_LSTM = 'SNAKE_SD_LSTM'
49 |
50 | def __str__(self):
51 | return self.value
52 |
53 | @staticmethod
54 | def from_string(s):
55 | try:
56 | return ModelType[s]
57 | except KeyError:
58 | raise ValueError()
59 |
60 |
61 | def get_arguments(parser: argparse.ArgumentParser):
62 | args = None
63 | try:
64 | args = parser.parse_args()
65 | except Exception:
66 | parser.print_help()
67 | exit(1)
68 | return args
69 |
70 |
71 | def run(model_type='md_lstm', enable_plotting=True):
72 | learning_rate = 0.01
73 | batch_size = 16
74 | h = 8
75 | w = 8
76 | channels = 1
77 | hidden_size = 16
78 |
79 | x = tf.placeholder(tf.float32, [batch_size, h, w, channels])
80 | y = tf.placeholder(tf.float32, [batch_size, h, w, channels])
81 |
82 | if model_type == ModelType.MD_LSTM:
83 | logger.info('Using Multi Dimensional LSTM.')
84 | rnn_out, _ = multi_dimensional_rnn_while_loop(rnn_size=hidden_size, input_data=x, sh=[1, 1])
85 | elif model_type == ModelType.HORIZONTAL_SD_LSTM:
86 | logger.info('Using Standard LSTM.')
87 | rnn_out = horizontal_standard_lstm(input_data=x, rnn_size=hidden_size)
88 | elif model_type == ModelType.SNAKE_SD_LSTM:
89 | rnn_out = snake_standard_lstm(input_data=x, rnn_size=hidden_size)
90 | else:
91 | raise Exception('Unknown model type: {}.'.format(model_type))
92 |
93 | model_out = slim.fully_connected(inputs=rnn_out,
94 | num_outputs=1,
95 | activation_fn=tf.nn.sigmoid)
96 |
97 | loss = tf.reduce_mean(tf.square(y - model_out))
98 | grad_update = tf.train.AdamOptimizer(learning_rate).minimize(loss)
99 |
100 | sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
101 | sess.run(tf.global_variables_initializer())
102 |
103 | fp = FileLogger('out_{}.tsv'.format(model_type), ['steps_{}'.format(model_type),
104 | 'overall_loss_{}'.format(model_type),
105 | 'time_{}'.format(model_type),
106 | 'relevant_loss_{}'.format(model_type)])
107 | steps = 1000
108 | for i in range(steps):
109 | batch = next_batch(batch_size, h, w)
110 | grad_step_start_time = time()
111 | batch_x = np.expand_dims(batch[0], axis=3)
112 | batch_y = np.expand_dims(batch[1], axis=3)
113 |
114 | model_preds, tot_loss_value, _ = sess.run([model_out, loss, grad_update], feed_dict={x: batch_x, y: batch_y})
115 |
116 | """
117 | ____________
118 | | |
119 | | |
120 | | x |
121 | | x <----- extract this prediction. Relevant loss is only computed for this value.
122 | |__________| we don't care about the rest (even though the model is trained on all values
123 | for simplicity). A standard LSTM should have a very high value for relevant loss
124 | whereas a MD LSTM (which can see all the TOP LEFT corner) should perform well.
125 | """
126 |
127 | # extract the predictions for the second x
128 | relevant_pred_index = get_relevant_prediction_index(batch_y)
129 | true_rel = np.array([batch_y[i, x, y, 0] for (i, (y, x)) in enumerate(relevant_pred_index)])
130 | pred_rel = np.array([model_preds[i, x, y, 0] for (i, (y, x)) in enumerate(relevant_pred_index)])
131 | relevant_loss = np.mean(np.square(true_rel - pred_rel))
132 |
133 | values = [str(i).zfill(4), tot_loss_value, time() - grad_step_start_time, relevant_loss]
134 | format_str = 'steps = {0} | overall loss = {1:.3f} | time {2:.3f} | relevant loss = {3:.3f}'
135 | logger.info(format_str.format(*values))
136 | fp.write(values)
137 |
138 | display_matplotlib_every = 500
139 | if enable_plotting and i % display_matplotlib_every == 0 and i != 0:
140 | visualise_mat(sess.run(model_out, feed_dict={x: batch_x})[0].squeeze())
141 | visualise_mat(batch_y[0].squeeze())
142 |
143 |
144 | def main():
145 | args = get_script_arguments()
146 | logging.basicConfig(format='%(asctime)12s - %(levelname)s - %(message)s', level=logging.INFO)
147 | run(args.model_type, args.enable_plotting)
148 |
149 |
150 | if __name__ == '__main__':
151 | main()
152 |
--------------------------------------------------------------------------------