Peter Bloem pbloem

Assistant prof. at Vrije Universiteit Amsterdam.

pbloem / data_trf.py

Last active November 30, 2025 14:04

Data loaders for assignment 3B (Transformers) of the Deep Learning course at Vrije Universiteit Amsterdam (dlvu.github.io).

	import wget, os, gzip, pickle, random, re, sys, importlib, tqdm, math, os, gzip, re, string

	from tqdm import trange
	from collections import Counter

	import torch

	import random
	from random import choice

pbloem / data_mnist.py

Last active March 28, 2025 06:55

	import numpy as np
	import torch
	from urllib import request
	import gzip
	import pickle
	import os

	def load_mnist(final=False, flatten=True, verbose=False, normalize=True):
	"""
	Load the MNIST data.

pbloem / infinite_dataset.py

Created May 29, 2022 12:29

Pytorch: use an IterableDataset to create an infinite stream of batched data

	from torch.utils.data import IterableDataset, DataLoader

	# The dataset has no length
	class TestDataset(IterableDataset):

	def __iter__(self): # __iter__() can be a generator
	while True:
	yield 5

	# The loader is called as normal

pbloem / data_rnn.py

Created September 2, 2021 14:33

Data loaders for DLVU assignment 3B (recurrent neural nets)

	import wget, os, gzip, pickle, random, re, sys

	IMDB_URL = 'http://dlvu.github.io/data/imdb.{}.pkl.gz'
	IMDB_FILE = 'imdb.{}.pkl.gz'

	PAD, START, END, UNK = '.pad', '.start', '.end', '.unk'

	def load_imdb(final=False, val=5000, seed=0, voc=None, char=False):

	cst = 'char' if char else 'word'

pbloem / ShuaiLoader.java

Created November 12, 2020 12:56

	package org.submassive;

	import java.io.BufferedInputStream;
	import java.io.BufferedReader;
	import java.io.File;
	import java.io.FileInputStream;
	import java.io.FileReader;
	import java.io.IOException;
	import java.io.InputStream;
	import java.io.InputStreamReader;

pbloem / powerlaw.ipynb

Created November 11, 2020 12:16

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pbloem / data.py

Last active November 14, 2025 13:24

	# -- assignment 1 --
	import numpy as np
	from urllib import request
	import gzip
	import pickle
	import os

	def load_synth(num_train=60_000, num_val=10_000, seed=0):
	"""
	Load some very basic synthetic data that should be easy to classify. Two features, so that we can plot the

pbloem / kemeny.py

Last active October 6, 2020 10:06

Gradient estimators

	import torch
	from torch import nn

	import torch.distributions as dist

	## REINFORCE

	adjacencies, num_edges, targets = load_data(...)
	opt = ...

pbloem / rgcn.py

Created April 30, 2020 01:43

RGCN implementation from scratch. Untested in gist form. Let me know if you need this for something.

	import torch, os, sys

	from torch import nn
	import torch.nn.functional as F
	import torch.distributions as ds

	from math import sqrt, ceil

	import layers, util

pbloem / beta_loss.py

Created June 17, 2019 19:24

	import torch
	import torch.nn as nn
	import torch.nn.functional as F

	import torchvision

	from torch.autograd import Variable
	from torchvision.transforms import CenterCrop, ToTensor, Compose, Lambda, Resize, Grayscale
	from torchvision.datasets import coco