Bilgee Bayaraa Bilgee

##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

Age and Gender Classification using Convolutional Neural Networks

caffemodel: age_net.caffemodel

$ cp /etc/redis.conf /etc/redis-xxx.conf

...

	// imgLib -> Image package from https://pub.dartlang.org/packages/image
	import 'package:image/image.dart' as imglib;
	import 'package:camera/camera.dart';

	Future<List<int>> convertImagetoPng(CameraImage image) async {
	try {
	imglib.Image img;
	if (image.format.group == ImageFormatGroup.yuv420) {
	img = _convertYUV420(image);
	} else if (image.format.group == ImageFormatGroup.bgra8888) {

	"""
	simple character rnn from Karpathy's blog
	"""

	import numpy as np

	def random_init(num_rows, num_cols):
	return np.random.rand(num_rows, num_cols)*0.01

	def zero_init(num_rows, num_cols):

	# Remove anything linked to nvidia
	sudo apt-get remove --purge nvidia*
	sudo apt-get autoremove

	# Search for your driver
	apt search nvidia

	# Select one driver (the last one is a decent choice)
	sudo apt install nvidia-370

	#!/usr/bin/env python3
	import tweepy
	import re
	import argparse
	import sys
	import time
	import traceback

	# Fill with your Twitter API keys!
	consumer_key = ''

	from math import sqrt

	def put_kernels_on_grid (kernel, pad = 1):

	'''Visualize conv. filters as an image (mostly for the 1st layer).
	Arranges filters into a grid, with some paddings between adjacent filters.

	Args:
	kernel: tensor of shape [Y, X, NumChannels, NumKernels]
	pad: number of black pixels around each filter (between them)

	server {
	client_body_in_file_only clean;
	client_body_buffer_size 32K;

	client_max_body_size 300M;

	sendfile on;
	send_timeout 300s;
	# Port that the web server will listen on.
	#listen 80;