| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| NEW_VER="0.33.1" | |
| POD_VER=`pod --version 2>/dev/null`|| POD_VER=`~/.rbenv/shims/pod --version 2>/dev/null` | |
| if [[ "$POD_VER" != *$NEW_VER ]]; then | |
| echo "error: WTF! You MUST upgrade cocoapods!!!" | |
| exit 1 | |
| fi |
| #!/bin/bash | |
| # | |
| # git-svn-diff originally by (http://mojodna.net/2009/02/24/my-work-git-workflow.html) | |
| # modified by mike@mikepearce.net | |
| # | |
| # Generate an SVN-compatible diff against the tip of the tracking branch | |
| # Get the tracking branch (if we're on a branch) | |
| TRACKING_BRANCH=`git svn info | grep URL | sed -e 's/.*\/branches\///'` |
| #if TARGET_IPHONE_SIMULATOR | |
| #pragma clang diagnostic push | |
| #pragma clang diagnostic ignored "-Wimplicit" | |
| // Log all notifications via tail -f /tmp/msgSends-* | |
| instrumentObjcMessageSends(YES); | |
| #pragma clang diagnostic pop | |
| #endif |
| http://devstreaming.apple.com/videos/wwdc/2013/710xfx3xn8197k4i9s2rvyb/710/710-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/202xdx2x47ezp1wein/202/202-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/200xdx2x35e1pxiinm/200/200-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/413xdx5x97itb5ek4yex3r7/413/413-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/612xax4xx65z1ervy5np1qb/612/612-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/221xex4xxohbllf4hblyngt/221/221-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/220xbx4xipaxfd1tggxuoib/220/220-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/711xcx4x8yuutk8sady6t9f/711/711-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/404xbx2xvp1eaaqonr8zokm/404/404-HD.mov?dl=1 | |
| http://devstreaming.apple.com/videos/wwdc/2013/505xbx4xrgmhwby4oiwkrpp/505/505-HD.mov?dl=1 |
| # xcode-build-bump.sh | |
| # @desc Auto-increment the build number every time the project is run. | |
| # @usage | |
| # 1. Select: your Target in Xcode | |
| # 2. Select: Build Phases Tab | |
| # 3. Select: Add Build Phase -> Add Run Script | |
| # 4. Paste code below in to new "Run Script" section | |
| # 5. Drag the "Run Script" below "Link Binaries With Libraries" | |
| # 6. Insure that your starting build number is set to a whole integer and not a float (e.g. 1, not 1.0) |