Erik Nijkamp enijkamp
enijkamp
/ gist:7a8ef87c2dc021f66b88eb2069520340
Created
May 14, 2018 12:29
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| t=+0.0000 d=+0000.0000 c_train=+0.1425 c_test=+1.6767 a_train=+0.9600 a_test=+0.6351 diff(a)=+0.0000 eigs=[+1.5348 +5.0679 -6.0570 +7.4783 -10.9613-12.9609 -14.8142 +15.2833 -15.6782 +19.5550] | |
| t=+0.0526 d=+0336.5288 c_train=+0.1837 c_test=+1.7479 a_train=+0.9394 a_test=+0.6369 diff(a)=+0.0018 eigs=[-0.0151 +0.1797 -0.5535 -0.9537 +1.9200 -2.3797 -4.3579 +5.5634 -6.2135 +6.4144] | |
| t=+0.1053 d=+0616.0807 c_train=+0.1502 c_test=+1.9037 a_train=+0.9518 a_test=+0.6341 diff(a)=-0.0010 eigs=[+1.0179 +5.3170 -5.8028 +6.4890-10.6475-13.4638+14.7852-16.7331+17.7944-19.7590] | |
| t=+0.1579 d=+0850.7733 c_train=+0.1273 c_test=+2.0505 a_train=+0.9590 a_test=+0.6330 diff(a)=-0.0021 eigs=[-1.0922 +2.3346 -3.0863 -3.4616 +3.8922 -5.6419 -6.1383 -6.5192 -7.0559 +7.3406] | |
| t=+0.2105 d=+1047.5834 c_train=+0.1127 c_test=+2.1657 a_train=+0.9646 a_test=+0.6326 diff(a)=-0.0025 eigs=[-0.5418 +2.1378 -4.2646 -6.3989 +7.4868+10.0773+12.0573+15.8084-16.1579-18.1559] | |
| t=+0.2632 d=+1210.4380 c_train=+0.1081 c_test=+2.2417 a_train=+0.9641 a_test=+0 |
enijkamp
/ gist:b803a91f01d7408fc8203fa7727614b2
Created
May 14, 2018 21:00
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| /media/vclagpu/Data1/enijkamp/repeller-sgd/venv/bin/python /media/vclagpu/Data1/enijkamp/repeller-sgd/experiments/bezier/eval_bezier_eigen_5.py | |
| 2018-05-14 13:57:18.728290: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA | |
| 2018-05-14 13:57:19.053588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: | |
| name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582 | |
| pciBusID: 0000:09:00.0 | |
| totalMemory: 11.90GiB freeMemory: 11.74GiB | |
| 2018-05-14 13:57:19.053614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 1, name: TITAN Xp, pci bus id: 0000:09:00.0, compute capability: 6.1) | |
| 2018-05-14 13:57:19,165 - loading ../../resnet_cifar100/model_1/model.ckpt-97675 | |
| 2018-05-14 13:57:22,610 - Restoring parameters from ../../resnet_cifar100/model_1/model.ckpt-97675 | |
| 2018-05-14 13:57:23,706 - conv2d_w |
enijkamp
/ gist:9efbbac3c0ae86ce2bf2976f9e291b87
Created
May 15, 2018 09:59
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| /home/enijkamp/env/tf/bin/python3 /home/enijkamp/Dropbox/repeller-sgd/experiments/cyclic/cyclic_9.py | |
| 2018-05-15 01:55:34,154 - cyclic_9 | |
| 2018-05-15 01:55:34,156 - loading ../../resnet_cifar100/model_2/model.ckpt-156280 | |
| 2018-05-15 01:55:34.156473: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA | |
| 2018-05-15 01:55:34.273215: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | |
| 2018-05-15 01:55:34.273501: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1344] Found device 0 with properties: | |
| name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531 | |
| pciBusID: 0000:02:00.0 | |
| totalMemory: 11.90GiB freeMemory: 11.54GiB | |
| 2018-05-15 01:55:34.325401: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there |
enijkamp
/ gist:d4767c207bb11657a7920d902d016beb
Created
May 15, 2018 10:05
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| 2018-05-15 05:51:51,407 - i=14890 lr=0.002255 c=0.67255330 l=18.93780136 mean(l)=18.67137085 r=38.65760803 t=17.92041802 | |
| 2018-05-15 05:52:09,288 - i=14900 lr=0.002253 c=0.80631238 l=20.48881531 mean(l)=18.76381592 r=38.61412811 t=17.88345909 | |
| 2018-05-15 05:52:27,237 - i=14910 lr=0.002251 c=0.81058139 l=19.97891998 mean(l)=18.68626587 r=38.73333359 t=17.94503307 | |
| 2018-05-15 05:52:45,187 - i=14920 lr=0.002248 c=0.73022264 l=18.31782150 mean(l)=18.73870117 r=38.68188858 t=17.95400310 | |
| 2018-05-15 05:53:03,173 - i=14930 lr=0.002246 c=0.66511607 l=18.61609650 mean(l)=18.80107788 r=38.74983215 t=17.98453307 | |
| 2018-05-15 05:53:21,123 - i=14940 lr=0.002244 c=0.75952756 l=20.30416298 mean(l)=18.91235474 r=38.73876190 t=17.95242691 | |
| 2018-05-15 05:53:39,118 - i=14950 lr=0.002242 c=0.77111256 l=19.60203362 mean(l)=18.97098755 r=38.69242859 t=17.99260187 | |
| 2018-05-15 05:53:57,082 - i=14960 lr=0.002239 c=0.74147147 l=17.00726318 mean(l)=18.93935791 r=38.76287079 t=17.96582890 | |
| 2018-05-15 05:54:15,051 - i=14970 lr=0.002237 c=0.530608 |
enijkamp
/ gist:cd5f874c878d26743048b3a84ea2cd4f
Created
May 16, 2018 22:33
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| import collections | |
| import tensorflow as tf | |
| from tensorflow.python.framework import constant_op | |
| from tensorflow.python.framework import dtypes | |
| from tensorflow.python.framework import ops | |
| from tensorflow.python.ops import array_ops | |
| from tensorflow.python.ops import control_flow_ops | |
| from tensorflow.python.ops import math_ops | |
| from tensorflow.python.ops import random_ops | |
| from tensorflow.python.ops import tensor_array_ops |
enijkamp
/ gist:9a17dec6ed7e6a093415607eb3d155b5
Created
May 20, 2018 02:27
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| 2018-05-19 02:59:12,147 - cyclic_12 | |
| 2018-05-19 02:59:12,152 - loading ../../resnet_cifar100/model_2/model.ckpt-156280 | |
| 2018-05-19 02:59:16,692 - Restoring parameters from ../../resnet_cifar100/model_2/model.ckpt-156280 | |
| 2018-05-19 02:59:20,559 - conv2d_w | |
| 2018-05-19 02:59:20,559 - conv2d_w_1 | |
| 2018-05-19 02:59:20,559 - batch_normalization_offset | |
| 2018-05-19 02:59:20,559 - batch_normalization_scales | |
| 2018-05-19 02:59:20,559 - conv2d_w_2 | |
| 2018-05-19 02:59:20,559 - batch_normalization_offset_1 | |
| 2018-05-19 02:59:20,559 - batch_normalization_scales_1 |
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| from subprocess import Popen, PIPE | |
| import os | |
| import datetime | |
| import threading | |
| import pygsheets | |
| def fetch_temps(): | |
| p = Popen(["nvidia-smi", "--query-gpu=index,uuid,temperature.gpu,utilization.gpu,memory.total,memory.used,memory.free,driver_version,name,gpu_serial,display_active,display_mode", "--format=csv,noheader,nounits"], stdout=PIPE) | |
| output = p.stdout.read().decode('UTF-8') |
enijkamp
/ gist:d6f5c879b24b6291a1efe63711b44bfc
Created
July 20, 2018 00:46
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| import numpy as np | |
| POINTS_PER_WF = int(1e4) | |
| X_SPACE = np.linspace(0, 100, POINTS_PER_WF) | |
| def make_waveform_with_noise(): | |
| def add_noise(vec): | |
| stdev = float(np.random.uniform(0, 0.2)) | |
| return vec + np.random.normal(0, stdev, size=len(vec)) |
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| def copy_source(file, output_dir): | |
| copyfile(file, os.path.join(output_dir, os.path.basename(file))) | |
| def setup_logging(output_dir): | |
| log_format = logging.Formatter("%(asctime)s : %(message)s") | |
| logger = logging.getLogger() | |
| logger.handlers = [] | |
| output_file = os.path.join(output_dir, 'output.log') | |
| file_handler = logging.FileHandler(output_file) |
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| def calc_gradient_penalty(netD, real_data, fake_data, LAMBDA=10, BATCH_SIZE=128, HW=[64, 64]): | |
| alpha = torch.rand(BATCH_SIZE, 1) | |
| alpha = alpha.expand(BATCH_SIZE, int(real_data.nelement() / BATCH_SIZE)).contiguous() | |
| alpha = alpha.view(BATCH_SIZE, 3, HW[0], HW[1]) | |
| alpha = alpha.to(device) | |
| fake_data = fake_data.view(BATCH_SIZE, 3, HW[0], HW[1]) | |
| interpolates = alpha * real_data.detach() + ((1 - alpha) * fake_data.detach()) | |
| interpolates = interpolates.to(device) |