jbum

# DreamImage.py - parameterized deepdream with support for multiple models,
# auto-image-scaling, guided dreaming and kaleido mode (self-guided).
# Jim Bumgardner 7-12-15

# this assumes the deploy file is called 'deploy.prototxt' - you may need
# to rename it if using a different model such as googlenet_places205.

# sample invocations that produce nice results:
# python dreamImage.py -i $1.jpg -model ../../caffe/models/googlenet_places205 -end inception_3a/output -iter 20 -octaves 5 -o $1_m3a.jpg
# python dreamImage.py -i $1.jpg -model ../../caffe/models/googlenet_places205 -end inception_3b/output -iter 20 -octaves 5 -o $1_m3b.jpg

cem2ran

let map = (fn,[head, ...tail]) => head === undefined 
	? []
	: [fn(head), ...map(fn,tail)]

console.log("map(x=>x+1,[1,2,3,4]) :",map(x=>x+1,[1,2,3,4]))

let filter = (predicate, [head, ...tail]) => head === undefined 
	? []
	: predicate(head)
	? [head, ...filter(predicate, tail)]

yang-wei

When receiving JSON data from other resources(server API etc), we need Json.Decode to convert the JSON values into Elm values. This gist let you quickly learn how to do that.

I like to follow working example code so this is how the boilerplate will look like:

import Graphics.Element exposing (Element, show)
import Task exposing (Task, andThen)
import Json.Decode exposing (Decoder, int, string, object3, (:=))

import Http

coreyhaines

module Animation exposing (..)

import Task
import Process
import Time exposing (Time, millisecond)


type Animation state
    = Setup state
    | Animate state