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@echo off | |
SET st2Path=C:\Program Files\Sublime Text 2\sublime_text.exe | |
rem add it for all file types | |
@reg add "HKEY_CLASSES_ROOT\*\shell\Open with Sublime Text 2" /t REG_SZ /v "" /d "Open with Sublime Text 2" /f | |
@reg add "HKEY_CLASSES_ROOT\*\shell\Open with Sublime Text 2" /t REG_EXPAND_SZ /v "Icon" /d "%st2Path%,0" /f | |
@reg add "HKEY_CLASSES_ROOT\*\shell\Open with Sublime Text 2\command" /t REG_SZ /v "" /d "%st2Path% \"%%1\"" /f | |
rem add it for folders | |
@reg add "HKEY_CLASSES_ROOT\Folder\shell\Open with Sublime Text 2" /t REG_SZ /v "" /d "Open with Sublime Text 2" /f |
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#!/usr/bin/env python2 | |
# coding: utf-8 | |
from __future__ import print_function | |
import sys | |
import re | |
import json | |
import urllib | |
from colorama import init, Fore, Back, Style | |
URL = 'http://csearch.naver.com/dcontent/spellchecker.nhn' |
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from pylearn2.models import mlp | |
from pylearn2.models import autoencoder | |
from pylearn2.models.autoencoder import Autoencoder | |
from pylearn2.training_algorithms import sgd | |
from pylearn2.training_algorithms.sgd import SGD | |
from pylearn2.termination_criteria import EpochCounter | |
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix | |
from pylearn2.utils.serial import save, load | |
from pylearn2.train import Train | |
from pylearn2.costs.autoencoder import MeanSquaredReconstructionError |
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import theano | |
from theano.tensor.io import send, recv, mpi_cmps | |
import theano.sandbox.linalg as linalg | |
from theano.gof.sched import sort_schedule_fn | |
from time import time | |
dot = theano.tensor.dot | |
dtype = 'float32' | |
n = 500 | |
run = False |
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{"lastUpload":"2020-09-25T12:33:59.722Z","extensionVersion":"v3.4.3"} |
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""" 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 |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |