| // Use Gists to store code you would like to remember later on | |
| console.log(window); // log the "window" object to the console |
| '''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 |
| import sys | |
| sys.path.append('../../facenet/src') | |
| import facenet | |
| import argparse | |
| import os | |
| import importlib | |
| import tensorflow as tf | |
| from tqdm import tqdm | |
| import tensorflow.contrib.slim as slim |
| import numpy as np | |
| from random import shuffle | |
| from past.builtins import xrange | |
| def svm_loss_naive(W, X, y, reg): | |
| """ | |
| Structured SVM loss function, naive implementation (with loops). | |
| Inputs have dimension D, there are C classes, and we operate on minibatches | |
| of N examples. |
| import numpy as np | |
| from random import shuffle | |
| from past.builtins import xrange | |
| def softmax_loss_naive(W, X, y, reg): | |
| """ | |
| Softmax loss function, naive implementation (with loops) | |
| Inputs have dimension D, there are C classes, and we operate on minibatches | |
| of N examples. |
| [a,b,c,d for a,b in AB for c,d in CD if a if b if c] |
You could always use Smart HTTP.
For read-only (git:) urls, install corkscrew.
Download git-proxy.sh. Put it somewhere and make it executable.
Run git config --global core.gitproxy '/usr/local/bin/git-proxy.sh'
To clone, push, pull over ssh, add the contents of ssh_config to your ~/.ssh/config file.