| // g++ -std=c++14 -pedantic-errors decorate.cpp | |
| #include <type_traits> | |
| #include <utility> | |
| #include <iostream> | |
| template<typename T> | |
| void print_args(const T &t) { | |
| std::cout << t; | |
| } |
| import collections | |
| import random | |
| import time | |
| def elapsed_time(f): | |
| start = time.time() | |
| result = f() | |
| finish = time.time() | |
| return result, finish - start |
| class IndexMaxHeap: | |
| def __init__(self, n, max_size, value): | |
| self._value = value | |
| self._size = 0 | |
| self._heap = [0] * max_size | |
| self._pos = [-1] * n | |
| def size(self): | |
| return self._size |
| import threading | |
| import tensorflow as tf | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| import keras.backend as K | |
| model = Sequential() | |
| model.add(Dense(output_dim=1, input_dim=1)) |
By contributing to the RELAAX repository through pull-request, comment, or otherwise, the contributor releases their content to the RELAAX license and copyright terms of the license.
If you have improvements to RELAAX, send us your pull requests! For those just getting started, Github has a howto.
We recommended you use an isolated Python environment to run RELAAX. Virtualenv or Anaconda are examples. If you're using the system's python environment, you may need to run pip install commands with sudo. On OSX / macOS, we recommend using Homebrew to install a current python version.
Install Docker
Clone RELAAX repo.
git clone git@github.com:deeplearninc/relaax.git