etendue

import multiprocessing as mp
import ai2thor.controller
import numpy as np
import time
import os

actions = [
    'MoveAhead',
    'MoveBack',
    'MoveRight',

davidnvq

# https://github.com/pytorch/examples/blob/master/imagenet/main.py#L392-L396

def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

        # Create a learning rate adjustment function that divides the learning rate by 10 every 30 epochs
def adjust_learning_rate(epoch):

davidnvq

import torch
from torch import nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence


class DynamicRNN(nn.Module):
	"""
	The wrapper version of recurrent modules including RNN, LSTM
	that support packed sequence batch.
	"""

williamFalcon

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F

"""
Blog post:
Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health:
https://medium.com/@_willfalcon/taming-lstms-variable-sized-mini-batches-and-why-pytorch-is-good-for-your-health-61d35642972e
"""

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

davidnvq

# Using two packages `tmux` (Ctrl+B) and `screen` (Ctrl+A):

#1. Create a new session: 
#tmux
tmux new -s session_name

#screen
screen -S session_name

#2. Attach to the session:

ihsgnef

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

def colorize(words, color_array):
    # words is a list of words
    # color_array is an array of numbers between 0 and 1 of length equal to words
    cmap = matplotlib.cm.get_cmap('RdBu')
    template = '<span class="barcode"; style="color: black; background-color: {}">{}</span>'
    colored_string = ''

karpathy

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

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

karpathy

"""
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)