lianyi lianyi

Simplest way to add pan/zoom to a d3js visualisation

##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

Usage

java -jar start.jar etc/jetty.xml etc/iprestriction.xml

The idea is to use mixin to isolate ip configurations without touching the main configuration file.

#Versions tested:

	#!/usr/bin/env python
	"""File format conversion

	category: vtk, file conversion, tomb"""
	import os, sys
	import vtk

	def vtk2vtp(invtkfile, outvtpfile, binary=False):
	"""What it says on the label"""
	reader = vtk.vtkPolyDataReader()

	import os
	import time
	import busio
	import digitalio
	import board
	import adafruit_mcp3xxx.mcp3008 as MCP
	from adafruit_mcp3xxx.analog_in import AnalogIn

	# create the spi bus
	spi = busio.SPI(clock=board.SCK, MISO=board.MISO, MOSI=board.MOSI)

	export SOLRUPDATE='http://localhost:8983/solr/testingCore/update?wt=json&commit=true'
	export SOLRQUERY='http://localhost:8983/solr/testingCore/select?wt=json&q=:'

	curl $SOLRUPDATE --data-binary '[{"id": "1"},{"id": "2"}]' -H 'Content-Type: application/json' -X POST -H "Transfer-Encoding: chunked"
	#>> response {"responseHeader":{"status":0,"QTime":295}}
	curl $SOLRQUERY
	#>> {"responseHeader":{"status":0,"QTime":15,"params":{"q":":","wt":"json"}},"response":{"numFound":2,"start":0,"maxScore":1.0,"docs":[{"id":"1","_version_":1570026155898044416},{"id":"2","_version_":1570026155878121472}]}}

	'''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