`gem install roo-xls`
`ruby xls2csv.rb ~/Downloads/hoge.xls`
| #!/usr/bin/env bash | |
| VERSION="0.0.1" | |
| set -e | |
| [ -n "$DEBUG" ] && set -x | |
| usage() { | |
| printf " | |
| Usage: $(basename $0) PROJECT_ID |
| # The 'other' host | |
| REMOTE_IP=192.168.122.189 | |
| # Name of the bridge | |
| BRIDGE_NAME=docker0 | |
| # Bridge address | |
| BRIDGE_ADDRESS=172.16.42.2/24 | |
| # Deactivate the docker0 bridge | |
| ip link set $BRIDGE_NAME down | |
| # Remove the docker0 bridge |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>JS Bin</title> | |
| <link rel="stylesheet" media="all" href="http://marianoguerra.github.io/json.human.js/css/json.human.css" /> | |
| <link rel="stylesheet" media="all" href="//cdnjs.cloudflare.com/ajax/libs/codemirror/3.16.0/codemirror.css" /> | |
| <script src="//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script> | |
| <script src="//cdnjs.cloudflare.com/ajax/libs/codemirror/3.16.0/codemirror.min.js"></script> | |
| <script src="http://marianoguerra.github.io/json.human.js/lib/crel.js"></script> |
| diff --git libavdevice/decklink_dec.cpp libavdevice/decklink_dec.cpp | |
| index 747f47e..d43faed 100644 | |
| --- libavdevice/decklink_dec.cpp | |
| +++ libavdevice/decklink_dec.cpp | |
| @@ -235,8 +235,8 @@ HRESULT decklink_input_callback::VideoInputFrameArrived( | |
| if (videoFrame->GetFlags() & bmdFrameHasNoInputSource) { | |
| if (videoFrame->GetPixelFormat() == bmdFormat8BitYUV) { | |
| unsigned bars[8] = { | |
| - 0xEA80EA80, 0xD292D210, 0xA910A9A5, 0x90229035, | |
| - 0x6ADD6ACA, 0x51EF515A, 0x286D28EF, 0x10801080 }; |
This gist has been superceded by Meta Graph functionality that has since been added to tensorflow core.
The code remains posted for archival purposes only.
| diff --git i/tensorflow/contrib/makefile/compile_ios_tensorflow.sh w/tensorflow/contrib/makefile/compile_ios_tensorflow.sh | |
| index 61ab844..e2bcf5a 100755 | |
| --- i/tensorflow/contrib/makefile/compile_ios_tensorflow.sh | |
| +++ w/tensorflow/contrib/makefile/compile_ios_tensorflow.sh | |
| @@ -72,19 +72,10 @@ then | |
| exit 1 | |
| fi | |
| -make -f tensorflow/contrib/makefile/Makefile \ | |
| -TARGET=IOS IOS_ARCH=X86_64 LIB_NAME=${LIB_PREFIX}-x86_64.a OPTFLAGS="$1" $2 $3 |
https://cloud.google.com/ml-engine/docs/concepts/training-overview
Even though the exact specifications of the machine types are subject to change at any time, you can compare them in terms of relative capability. The following table uses rough "t-shirt" sizing to describe the machine types.
ココらへんがいまいち曖昧なので調べてみた
trainer/task.py
| from sklearn.externals import joblib | |
| def save_model(model, name): | |
| ''' | |
| saves trained model | |
| ''' | |
| joblib.dump(model, name) | |
| def load_model(name): |
| [Unit] | |
| Description=Jupyter Service | |
| After=docker.service | |
| Requires=docker.service | |
| [Service] | |
| TimeoutStartSec=0 | |
| Restart=on-failure | |
| ExecStartPre=-/usr/bin/docker stop jupyter | |
| ExecStartPre=-/usr/bin/docker rm jupyter |