d92490b808
Moved comparison to the top
2019-11-03 20:17:58 -05:00
a3b76a56c6
Added PyTorch vs TF MAE comparison
2019-11-03 20:14:40 -05:00
bb32eb0f46
Changed README to reflect PyTorch implementation
2019-10-30 12:30:45 -04:00
073f1d4a6e
updated epeoch num
2019-10-08 17:32:16 -04:00
b2d2b21dbd
logging MAE
2019-10-08 13:46:34 -04:00
f720529ac9
demoing with test data
2019-10-08 13:11:43 -04:00
d2913fd6f1
converting to CPU
2019-10-08 13:09:02 -04:00
f92e7295a0
added run_demo_pytorch
2019-10-08 13:05:49 -04:00
dda7013f07
returning predictions from the model during eval at every timestep
2019-10-08 12:56:20 -04:00
46b552e075
updated eps value
2019-10-08 02:44:13 -04:00
02fb2430f0
removed logging of every horizon
2019-10-08 02:10:59 -04:00
765142de00
refactored
2019-10-07 20:55:26 -04:00
5d7694e293
logging to 4 decimals
2019-10-07 20:54:58 -04:00
f6e6713f74
fixed range bug
2019-10-07 20:40:54 -04:00
2560e1d954
Added per timestep loss
2019-10-07 20:03:00 -04:00
3d93008a3e
improved saving and restoring of model
2019-10-07 11:56:14 -04:00
a5a1063160
fixed docstring
2019-10-07 10:48:48 -04:00
5509e9aae5
Ensured all parameters are added to the optimizer
2019-10-07 09:47:38 -04:00
de42a67391
added logging statement
2019-10-07 07:59:41 -04:00
941675d6a7
Added kwargs
2019-10-06 18:57:13 -04:00
96d8dc4417
handling nans in loss tensor
2019-10-06 18:55:35 -04:00
5dd0f1dd3a
implemented masked mae loss, added tensorflow writer, changed % logic
2019-10-06 18:08:13 -04:00
a8814d5d93
Added docstrings
2019-10-06 17:12:06 -04:00
ad8ac8ff2f
Merge branch 'pytorch_integration' into pytorch_scratch
2019-10-06 17:07:54 -04:00
5b93f3c778
Merge branch 'pytorch_integration' of github.com:chnsh/DCRNN into pytorch_integration
2019-10-06 17:02:35 -04:00
9fb999c3bb
squash! Added dcrnn_cell
2019-10-06 17:01:49 -04:00
d1964672c2
Added dcrnn_cell
...
Rough implementation complete - could forward pass it through the network
Ensured sparse mm for readability, logging sparsely as well
moving tensors to GPU
moving tensors to GPU [v2]
moving tensors to GPU [v3]
logging and refactor
logging and refactor
logging and refactor
logging and refactor
logging and refactor
logging and refactor
logging and refactor
ensured row major ordering
fixed log message
2019-10-06 17:00:23 -04:00
d46b605a65
ensured row major ordering
2019-10-06 15:53:14 -04:00
6331173f44
logging and refactor
2019-10-06 15:22:57 -04:00
ec5d9555a5
logging and refactor
2019-10-06 15:15:11 -04:00
55a087ac9f
logging and refactor
2019-10-06 14:40:53 -04:00
02c4681ad9
logging and refactor
2019-10-06 14:38:44 -04:00
b6a2b3fe8e
logging and refactor
2019-10-06 14:34:58 -04:00
036e552bf6
logging and refactor
2019-10-06 14:31:46 -04:00
31acadedce
logging and refactor
2019-10-06 14:29:28 -04:00
e563e1bf37
moving tensors to GPU [v3]
2019-10-06 14:13:02 -04:00
017ec70783
moving tensors to GPU [v2]
2019-10-06 14:10:20 -04:00
ba304e9f04
moving tensors to GPU
2019-10-06 14:00:54 -04:00
9454fd91a2
Ensured sparse mm for readability, logging sparsely as well
2019-10-06 13:44:55 -04:00
2e1836df40
Rough implementation complete - could forward pass it through the network
2019-10-06 13:24:37 -04:00
b65df994e4
Added dcrnn_cell
2019-10-06 11:55:02 -04:00
e80c47390d
Merge branch 'pytorch_implementation' into pytorch_scratch
2019-10-06 11:49:49 -04:00
5a790d5586
cuda no grad
2019-10-04 23:30:10 -04:00
593e3db1bf
Using model.cuda() if cuda is available
2019-10-04 22:45:08 -04:00
8d3b1d0d66
Implemented lr annealing schedule
2019-10-04 21:18:05 -04:00
ba880b8230
Implementing load and save models and early stopping
2019-10-04 17:25:03 -04:00
d9f41172dc
Implemented eval and function
2019-10-04 17:07:38 -04:00
20c6aa5862
Fixed bugs with refactoring
2019-10-04 16:05:52 -04:00
2b8d5e6b31
Refactored code and moved everything into a DCRNN forward pass
2019-10-04 13:02:50 -04:00
f41dc442b0
Implemented gradient clipping and returning output from training one batch
2019-10-03 19:35:54 -04:00
Chintan Shah
Chintan Shah