sbarratt

"""
This script provides coordinate transformations from Geodetic -> ECEF, ECEF -> ENU
and Geodetic -> ENU (the composition of the two previous functions). Running the script
by itself runs tests.
based on https://gist.github.com/govert/1b373696c9a27ff4c72a.
"""
import math

a = 6378137
b = 6356752.3142

melgor

import tensorflow as tf
from tensorflow.contrib.layers.python.layers import initializers
slim = tf.contrib.slim

'''
============================================================================
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
============================================================================
Based on the paper: https://arxiv.org/pdf/1707.03718.pdf
'''

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

haje01

도커와 AWS를 활용한 클라우드 딥러닝 환경 구축

글쓴이: 김정주(haje01@gmail.com)

최근 딥러닝 관련 패키지들은 대부분 CPU와 GPU를 함께 지원하고 있습니다. GPU를 사용하면 보다 빠르게 학습 결과를 낼 수 있지만, GPU를 활용하기 위해서는 NVIDIA계열의 그래픽 카드, 드라이버 S/W 그리고 CUDA의 설치를 필요로 합니다.

이 글에서는 AWS의 GPU 인스턴스와 도커를 활용해 딥러닝 패키지(Caffe)를 편리하게 사용하는 방법을 소개합니다.

---

baraldilorenzo

##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

thearn

import time
import Image
import ImageGrab

while True:
    time.sleep(0.5)
    t = str(time.time()).replace('.','-')
    tt = time.time()
    img=ImageGrab.grab()
    img = img.resize((800,600))