lianyi

#!/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()

lianyi

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)

khanhnamle1994

#--------------------------
# USER-SPECIFIED DATA
#--------------------------

# Tune these parameters

num_classes = 2
image_shape = (160, 576)
EPOCHS = 40
BATCH_SIZE = 16

lianyi

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}]}}

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

lianyi

Simplest way to add pan/zoom to a d3js visualisation

lianyi

var geo_usstates = {
    "type": "FeatureCollection",
    "features": [{
        "type": "Feature",
        "id": "01",
        "properties": {
            "name": "AL"
        },
        "geometry": {

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

lianyi

Usage

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

Notes

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

#Versions tested:

smithkyle

npm install -S passport-ldapauth