Stas stas-sl
stas-sl
/ rvm error when installing rubinius 2.0.0 rc1
Last active
December 20, 2015 13:58
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| stas@stas-note:~/RubymineProjects/untitled3/tmp2$ rvm install rbx-2.0.0-rc1 --debug --force | |
| rbx-2.0.0-rc1 - install | |
| Searching for binary rubies, this might take some time. | |
| Remote file does not exist https://rvm.io/binaries/ubuntu/13.04/i386/rubinius-2.0.0-rc1.tar.bz2 | |
| Remote file does not exist http://jruby.org.s3.amazonaws.com/downloads/rubinius-2.0.0-rc1.tar.bz2 | |
| Remote file does not exist http://binaries.rubini.us/ubuntu/13.04/i386/rubinius-2.0.0-rc1.tar.bz2 | |
| rvm_remote_server_url3 not found | |
| No remote file name found | |
| No binary rubies available for: ubuntu/13.04/i386/rbx-2.0.0-rc1. | |
| Continuing with compilation. Please read 'rvm help mount' to get more information on binary rubies. |
stas-sl
/ pyro_hangs.py
Created
August 20, 2015 11:18
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| import Pyro4 | |
| import Pyro4.errors | |
| import time | |
| import threading | |
| import signal | |
| import multiprocessing as mp | |
| Pyro4.config.THREADPOOL_SIZE = 100 | |
| # Pyro4.config.THREADING2 = True | |
| # Pyro4.config.SERVERTYPE = "multiplex" |
stas-sl
/ blocks_lstm_parity_problem.py
Created
August 26, 2015 13:31
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| import theano.tensor as T | |
| import numpy as np | |
| from fuel.streams import DataStream | |
| from fuel.datasets import IterableDataset | |
| from blocks.main_loop import MainLoop | |
| from blocks.extensions import FinishAfter, Printing, Timing, ProgressBar | |
| from blocks.algorithms import GradientDescent, Scale | |
| from blocks.bricks import Linear, Logistic |
stas-sl
/ blocks_lstm_parity_problem_solved.py
Last active
August 26, 2015 22:34
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| import theano.tensor as T | |
| import numpy as np | |
| import random | |
| from fuel.streams import DataStream | |
| from fuel.datasets import IterableDataset | |
| from blocks.main_loop import MainLoop | |
| from blocks.extensions import FinishAfter, Printing, Timing, ProgressBar | |
| from blocks.algorithms import GradientDescent, Scale, Adam |
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| I0321 19:35:15.407027 2639 solver.cpp:280] Solving mixed_lstm | |
| I0321 19:35:15.407040 2639 solver.cpp:281] Learning Rate Policy: fixed | |
| I0321 19:35:15.425205 2639 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0321 19:35:46.798645 2639 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.011 | |
| I0321 19:35:46.798874 2639 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.014 | |
| I0321 19:35:46.798893 2639 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.013 | |
| I0321 19:35:46.798907 2639 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.02 | |
| I0321 19:35:46.798918 2639 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.085 | |
| I0321 19:35:46.798930 2639 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.253 | |
| I0321 19:35:46.798943 2639 solver.cpp:406] Test net output #6: loss1/accuracy07 = 0.434 |
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| I0327 12:46:12.071159 21344 solver.cpp:280] Solving mixed_lstm | |
| I0327 12:46:12.071171 21344 solver.cpp:281] Learning Rate Policy: fixed | |
| I0327 12:46:12.088115 21344 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0327 12:46:43.353196 21344 solver.cpp:393] Test loss: 256.606 | |
| I0327 12:46:43.353467 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.002 | |
| I0327 12:46:43.353493 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.008 | |
| I0327 12:46:43.353507 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.018 | |
| I0327 12:46:43.353519 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.037 | |
| I0327 12:46:43.353531 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.049 | |
| I0327 12:46:43.353559 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.136 |
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| I0327 12:46:12.071159 21344 solver.cpp:280] Solving mixed_lstm | |
| I0327 12:46:12.071171 21344 solver.cpp:281] Learning Rate Policy: fixed | |
| I0327 12:46:12.088115 21344 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0327 12:46:43.353196 21344 solver.cpp:393] Test loss: 256.606 | |
| I0327 12:46:43.353467 21344 solver.cpp:406] Test net output #0: loss1/accuracy01 = 0.002 | |
| I0327 12:46:43.353493 21344 solver.cpp:406] Test net output #1: loss1/accuracy02 = 0.008 | |
| I0327 12:46:43.353507 21344 solver.cpp:406] Test net output #2: loss1/accuracy03 = 0.018 | |
| I0327 12:46:43.353519 21344 solver.cpp:406] Test net output #3: loss1/accuracy04 = 0.037 | |
| I0327 12:46:43.353531 21344 solver.cpp:406] Test net output #4: loss1/accuracy05 = 0.049 | |
| I0327 12:46:43.353559 21344 solver.cpp:406] Test net output #5: loss1/accuracy06 = 0.136 |
stas-sl
/ caffe.ip-172-31-38-100.invalid-user.log.INFO.20160330-005805.10583
Created
March 30, 2016 07:26
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| Log file created at: 2016/03/30 00:58:05 | |
| Running on machine: ip-172-31-38-100 | |
| Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg | |
| I0330 00:58:05.628971 10583 caffe.cpp:185] Using GPUs 0 | |
| I0330 00:58:05.885462 10583 caffe.cpp:190] GPU 0: GRID K520 | |
| I0330 00:58:06.003937 10583 solver.cpp:48] Initializing solver from parameters: | |
| test_iter: 1000 | |
| test_interval: 5000 | |
| base_lr: 0.01 | |
| display: 500 |
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| I0330 00:58:47.679792 10583 solver.cpp:280] Solving mixed_lstm | |
| I0330 00:58:47.679805 10583 solver.cpp:281] Learning Rate Policy: fixed | |
| I0330 00:58:47.700913 10583 solver.cpp:338] Iteration 0, Testing net (#0) | |
| I0330 00:59:21.332644 10583 solver.cpp:393] Test loss: 272.275 | |
| I0330 00:59:21.333082 10583 solver.cpp:406] Test net output #0: loss1/accuracy = 0.0014 | |
| I0330 00:59:21.333103 10583 solver.cpp:406] Test net output #1: loss1/accuracy_incl_empty = 0.664909 | |
| I0330 00:59:21.333117 10583 solver.cpp:406] Test net output #2: loss1/accuracy_top3 = 0.00426905 | |
| I0330 00:59:21.333134 10583 solver.cpp:406] Test net output #3: loss1/cross_entropy_loss = 75.728 (* 0.3 = 22.7184 loss) | |
| I0330 00:59:21.333150 10583 solver.cpp:406] Test net output #4: loss1/cross_entropy_loss_incl_empty = 75.7553 (* 0.3 = 22.7266 loss) | |
| I0330 00:59:21.333165 10583 solver.cpp:406] Test net output #5: loss2/accuracy = 0 |
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| I0331 10:11:54.822957 29371 solver.cpp:280] Solving mixed_lstm | |
| I0331 10:11:54.822969 29371 solver.cpp:281] Learning Rate Policy: fixed | |
| I0331 10:11:55.173683 29371 solver.cpp:229] Iteration 0, loss = 13.7452 | |
| I0331 10:11:55.173739 29371 solver.cpp:245] Train net output #0: loss1/accuracy = 0 | |
| I0331 10:11:55.173756 29371 solver.cpp:245] Train net output #1: loss1/accuracy_incl_empty = 0 | |
| I0331 10:11:55.173769 29371 solver.cpp:245] Train net output #2: loss1/accuracy_top3 = 0.0217391 | |
| I0331 10:11:55.173785 29371 solver.cpp:245] Train net output #3: loss1/cross_entropy_loss = 4.35526 (* 0.3 = 1.30658 loss) | |
| I0331 10:11:55.173800 29371 solver.cpp:245] Train net output #4: loss1/cross_entropy_loss_incl_empty = 4.39893 (* 0.3 = 1.31968 loss) | |
| I0331 10:11:55.173812 29371 solver.cpp:245] Train net output #5: loss2/accuracy = 0 | |
| I0331 10:11:55.173825 29371 solver.cpp:245] Train net output #6: loss2/accuracy_incl_empty = 0 |
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