| def defModel(): | |
| model = Sequential() | |
| #Izda.add(TimeDistributed( | |
| # Convolution2D(40,3,3,border_mode='same'), input_shape=(sequence_lengths, 1,8,10))) | |
| model.add( | |
| TimeDistributed( | |
| Conv2D(32, (7, 7), padding='same', strides = 2), | |
| input_shape=(None, 540, 960, 2))) | |
| model.add(Activation('relu')) |
| class Node: | |
| def __init__(self, val): | |
| self.val = val | |
| self.leftChild = None | |
| self.rightChild = None | |
| def get(self): | |
| return self.val | |
| def set(self, val): |
| """ 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 |
$ sudo xcode-select --switch /Library/Developer/CommandLineTools/
$ clang --version
Apple LLVM version 8.1.0 (clang-802.0.42)
Target: x86_64-apple-darwin17.3.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin
####Rets Rabbit http://www.retsrabbit.com
Rets Rabbit removes the nightmare of importing thousands of real estate listings and photos from RETS or ListHub and gives you an easy to use import and Web API server so you can focus on building your listing search powered website or app.
| # Authors: Kyle Kastner | |
| # License: BSD 3-clause | |
| import theano.tensor as T | |
| import numpy as np | |
| import theano | |
| class rmsprop(object): | |
| """ | |
| RMSProp with nesterov momentum and gradient rescaling |
| from __future__ import division | |
| import string | |
| import math | |
| tokenize = lambda doc: doc.lower().split(" ") | |
| document_0 = "China has a strong economy that is growing at a rapid pace. However politically it differs greatly from the US Economy." | |
| document_1 = "At last, China seems serious about confronting an endemic problem: domestic violence and corruption." | |
| document_2 = "Japan's prime minister, Shinzo Abe, is working towards healing the economic turmoil in his own country for his view on the future of his people." | |
| document_3 = "Vladimir Putin is working hard to fix the economy in Russia as the Ruble has tumbled." |
| from __future__ import division | |
| import string | |
| import math | |
| tokenize = lambda doc: doc.lower().split(" ") | |
| document_0 = "China has a strong economy that is growing at a rapid pace. However politically it differs greatly from the US Economy." | |
| document_1 = "At last, China seems serious about confronting an endemic problem: domestic violence and corruption." | |
| document_2 = "Japan's prime minister, Shinzo Abe, is working towards healing the economic turmoil in his own country for his view on the future of his people." | |
| document_3 = "Vladimir Putin is working hard to fix the economy in Russia as the Ruble has tumbled." |
These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.