This is a guide for aligning images.
See the full Advanced Markdown doc for more tips and tricks
import torch, torch.nn as nn, torch.nn.functional as F | |
import numpy as np | |
import torch.optim as optim | |
# tied autoencoder using off the shelf nn modules | |
class TiedAutoEncoderOffTheShelf(nn.Module): | |
def __init__(self, inp, out, weight): | |
super().__init__() | |
self.encoder = nn.Linear(inp, out, bias=False) | |
self.decoder = nn.Linear(out, inp, bias=False) |
import torch | |
import torch.nn as nn | |
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
seqs = ['gigantic_string','tiny_str','medium_str'] | |
# make <pad> idx 0 | |
vocab = ['<pad>'] + sorted(set(''.join(seqs))) | |
# make model |
This is a guide for aligning images.
See the full Advanced Markdown doc for more tips and tricks
""" 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 |
-- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters. | |
require 'torch' | |
require 'nn' | |
require 'optim' | |
torch.setdefaulttensortype('torch.FloatTensor') | |
-- (1) Define a model for this example. | |
local model = nn.Sequential() | |
model:add(nn.Linear(10,20)) |
-- suppose you have a model called model | |
lrs_model = model:clone() | |
lrs = lrs_model:getParameters() | |
lrs:fill(1) -- setting the base learning rate to 1 | |
-- now lets set the learning rate factor of the bias of module 5 to 2 | |
lrs_model:get(5).bias:fill(2) | |
-- same thing for the weights of module 2, let's set them to 3 | |
lrs_model:get(2).weight:fill(3) |
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." |
"""Information Retrieval metrics | |
Useful Resources: | |
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
http://www.nii.ac.jp/TechReports/05-014E.pdf | |
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
""" | |
import numpy as np |
#!/bin/bash | |
# Script for installing tmux on systems where you don't have root access. | |
# tmux will be installed in $HOME/local/bin. | |
# It's assumed that wget and a C/C++ compiler are installed. | |
# exit on error | |
set -e | |
TMUX_VERSION=1.8 |