wdevazelhes

import numpy as np
from sklearn.utils.extmath import softmax
from sklearn.kernel_approximation import RBFSampler
from sklearn_extra.kernel_approximation import Fastfood

seed = 42
rng = np.random.RandomState(seed)

D = 20

ClementPinard

Still Box

Torrent description

In addition to this README, this torrent contains 4 datasets:

Name	Image size (px)	Scene number	Size compressed (B)	Total size (B)
64.tar.xz	64x64	80K	9.8G	19G
128.tar.xz	128x128	20K	7.1G	12G

culurciello

-- Xception model
-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357
-- E. Culurciello, October 2016

require 'nn'
local nClasses = 1000

function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)
   local block = nn.Sequential()
   block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1))

fchollet

'''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

zaemiel

1. Go to Sublime Text to: Tools -> Build System -> New Build System 
and put the next lines:

{
    "cmd": ["python3", "-i", "-u", "$file"],
    "file_regex": "^[ ]File \"(...?)\", line ([0-9]*)",
    "selector": "source.python"
}

Then save it with a meaningful name like: python3.sublime-build

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

nylki

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

SiestaMadokaist

class SummedAreaTable(object):
  def __init__(self, size, data):
    """
    Just because I dislike a 2d array / list.
    data should be a List of Integer.
    """   
    width, height = size 
    assert width * height == len(data), "invalid data length and or data size"
    self.size = size 
    self.data = data    

tylerneylon

--[[ json.lua

A compact pure-Lua JSON library.
The main functions are: json.stringify, json.parse.

## json.stringify:

This expects the following to be true of any tables being encoded:
 * They only have string or number keys. Number keys must be represented as
   strings in json; this is part of the json spec.

jakevdp

# By Jake VanderPlas
# License: BSD-style

import matplotlib.pyplot as plt
import numpy as np


def discrete_cmap(N, base_cmap=None):
    """Create an N-bin discrete colormap from the specified input map"""