PGK kalaidin

python qlearn.py

##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

すごいHaskellたのしく学ぼう！をScalaでやってみたメモ

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

	from __future__ import print_function
	import imageio
	from PIL import Image
	import numpy as np
	import keras

	from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
	from keras.models import Model
	from keras.regularizers import l2
	from keras.optimizers import SGD

	# Context manager to generate batches in the background via a process pool
	# Usage:
	#
	# def batch(seed):
	# .... # generate minibatch
	# return minibatch
	#
	# with BatchGenCM(batch) as bg:
	# minibatch = next(bg)
	# .... # do something with minibatch

	import subprocess, itertools, numpy
	import matplotlib.pyplot as plt

	command = 'git log --shortstat --log-size --format=oneline --no-merges'.split()
	data = subprocess.check_output(command).split('\n')

	def read_groups():
	buf = []
	for line in data:
	buf.append(line)