jjuanrivvera99

version: '3.3'

services:
  mssql:
    container_name: sql-server
    image: mcr.microsoft.com/mssql/server:2017-latest
    #image: mcr.microsoft.com/mssql/server:2017-CU11-ubuntu
    restart: always
    environment:
      ACCEPT_EULA: "Y"

haraonline

# code for the lesson "understanding query strings"
# section 2: lecture 9

from flask import Flask, render_template, request

app = Flask(__name__)


@app.route('/index')
@app.route('/')

didacroyo

# from the guide https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
# and from other resources found, trying to achieve a good classifier based on Inveption V3 pre-trained netfork

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
from keras.callbacks import ModelCheckpoint

redlotus

# -*- coding: utf-8 -*-
"""Example Google style docstrings.

This module demonstrates documentation as specified by the `Google Python
Style Guide`_. Docstrings may extend over multiple lines. Sections are created
with a section header and a colon followed by a block of indented text.

Example:
    Examples can be given using either the ``Example`` or ``Examples``
    sections. Sections support any reStructuredText formatting, including

jpchateau

Docker cheat sheet

Commandes Docker

$ docker ps # Visualiser les conteneurs actifs
$ docker ps -a # Visualiser tous les conteneurs
$ docker rm [container] # Supprimer un conteneur inactif
$ docker rm -f [container] # Forcer la suppression d'un conteneur actif
$ docker images # Lister les images existantes

semeltheone

knime.exe -consoleLog -noexit -nosplash -application org.knime.product.KNIME_BATCH_APPLICATION -workflowFile="PathToYourWorkflow.zip" -destFile="OutputPathToYourWorkflow.zip"