Skip to content

Instantly share code, notes, and snippets.

View dribnet's full-sized avatar

tom white dribnet

449 followers · 101 following
View GitHub Profile
@kashif
kashif / es.py
Last active June 5, 2017 12:07
Initial implementation of Evolution Strategies
import numpy as np
import gym
from gym.spaces import Discrete, Box
from gym.wrappers import Monitor
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
# ================================================================
# Policies
@lucasb-eyer
lucasb-eyer / ILSVRC2012_ids.json
Last active June 10, 2017 02:37
Data for Beacon8 Pretrained Imagenet example
{
"n02112350": 148,
"n04344873": 311,
"n01692333": 470,
"n03459775": 725,
"n04133789": 751,
"n01871265": 214,
"n04366367": 681,
"n03891332": 527,
"n03085013": 543,
@ssahu
ssahu / classifier_from_little_data_script_3.py
Last active January 14, 2018 07:53 — forked from fchollet/classifier_from_little_data_script_3.py
Fine-tuning a Keras model. Updated to the Keras 2.0 API.
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
@hiroyachiba
hiroyachiba / classifier_from_little_data_script_3.py
Last active January 21, 2018 22:51 — forked from fchollet/classifier_from_little_data_script_3.py
Fine-tuning a Keras model.
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
@soumith
soumith / dcgan.py
Created January 30, 2017 03:25 — forked from anonymous/dcgan.py
import torch
import torch.nn as nn
import torch.nn.parallel
class DCGAN_D(nn.Container):
def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
super(DCGAN_D, self).__init__()
self.ngpu = ngpu
assert isize % 16 == 0, "isize has to be a multiple of 16"
@hussius
hussius / ae_toy_example.py
Last active June 28, 2019 17:12
Toy example of single-layer autoencoder in TensorFlow
import tensorflow as tf
import numpy as np
import math
#import pandas as pd
#import sys
input = np.array([[2.0, 1.0, 1.0, 2.0],
[-2.0, 1.0, -1.0, 2.0],
[0.0, 1.0, 0.0, 2.0],
[0.0, -1.0, 0.0, -2.0],
@jfsantos
jfsantos / example_hdf5matrix.py
Last active May 11, 2020 06:57
from keras.models import Sequential
from keras.layers import Dense
from keras.utils.io_utils import HDF5Matrix
import numpy as np
def create_dataset():
import h5py
X = np.random.randn(200,10).astype('float32')
y = np.random.randint(0, 2, size=(200,1))
f = h5py.File('test.h5', 'w')
@nicksam112
nicksam112 / keras_es.py
Last active November 28, 2020 16:02
Evolution Strategies with Keras
#Evolution Strategies with Keras
#Based off of: https://blog.openai.com/evolution-strategies/
#Implementation by: Nicholas Samoray
#README
#Meant to be run on a single machine
#APPLY_BIAS is currently not working, keep to False
#Solves Cartpole as-is in about 50 episodes
#Solves BipedalWalker-v2 in about 1000
@juliensimon
juliensimon / extractCifar10.py
Last active May 26, 2021 11:27
Extract 10 images from the CIFAR-10 data set
import mxnet as mx
import numpy as np
import cPickle
import cv2
def extractImagesAndLabels(path, file):
f = open(path+file, 'rb')
dict = cPickle.load(f)
images = dict['data']
images = np.reshape(images, (10000, 3, 32, 32))
@letmaik
letmaik / index.html
Created October 7, 2016 10:07
Leaflet GridLayer example with Canvas
<!DOCTYPE html>
<html>
<head>
<title>GridLayer Test</title>
<meta charset="utf-8" />
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.0.1/dist/leaflet.css" />
<style>
body {
padding: 0;
margin: 0;