Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:
| Install i3-gaps on Ubuntu 16 | |
| apt-get install libxcb1-dev libxcb-keysyms1-dev libpango1.0-dev libxcb-util0-dev libxcb-icccm4-dev libyajl-dev libstartup-notification0-dev libxcb-randr0-dev libev-dev libxcb-cursor-dev libxcb-xinerama0-dev libxcb-xkb-dev libxkbcommon-dev libxkbcommon-x11-dev autoconf xutils-dev dh-autoreconf | |
| git clone --recursive https://github.com/Airblader/xcb-util-xrm.git | |
| cd xcb-util-xrm/ | |
| ./autogen.sh | |
| make | |
| sudo make install |
| from __future__ import division | |
| import theano | |
| import theano.tensor as T | |
| import lasagne | |
| class MirrorPadLayer(lasagne.layers.Layer): | |
| def __init__(self, incoming, width, batch_ndim=2, **kwargs): |
| # Euclidean distance. | |
| def euc_dist(pt1,pt2): | |
| return math.sqrt((pt2[0]-pt1[0])*(pt2[0]-pt1[0])+(pt2[1]-pt1[1])*(pt2[1]-pt1[1])) | |
| def _c(ca,i,j,P,Q): | |
| if ca[i,j] > -1: | |
| return ca[i,j] | |
| elif i == 0 and j == 0: | |
| ca[i,j] = euc_dist(P[0],Q[0]) | |
| elif i > 0 and j == 0: |
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
\