| # We set an environment variable in this phase so it gets picked up by pip, but we don't want to bake secrets into our container image | |
| FROM python:3.6-alpine AS builder | |
| ARG INDEX_URL | |
| ENV PIP_EXTRA_INDEX_URL=$INDEX_URL | |
| COPY requirements.txt . | |
| RUN pip install -U pip \ | |
| && pip install --user -r requirements.txt |
| from collections import OrderedDict | |
| import numpy as np | |
| import spacy | |
| from spacy.lang.en.stop_words import STOP_WORDS | |
| nlp = spacy.load('en_core_web_sm') | |
| class TextRank4Keyword(): | |
| """Extract keywords from text""" | |
| ''' Script for downloading all GLUE data. | |
| Note: for legal reasons, we are unable to host MRPC. | |
| You can either use the version hosted by the SentEval team, which is already tokenized, | |
| or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. | |
| For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). | |
| You should then rename and place specific files in a folder (see below for an example). | |
| mkdir MRPC | |
| cabextract MSRParaphraseCorpus.msi -d MRPC |
| '''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 |
##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
| <select name="Sehir"> | |
| <option value="0">------</option> | |
| <option value="1">Adana</option> | |
| <option value="2">Adıyaman</option> | |
| <option value="3">Afyonkarahisar</option> | |
| <option value="4">Ağrı</option> | |
| <option value="5">Amasya</option> | |
| <option value="6">Ankara</option> | |
| <option value="7">Antalya</option> | |
| <option value="8">Artvin</option> |
| <?xml version="1.0" encoding="utf-8"?> | |
| <resources> | |
| <!-- google's material design colours from | |
| http://www.google.com/design/spec/style/color.html#color-ui-color-palette --> | |
| <!--reds--> | |
| <color name="md_red_50">#FFEBEE</color> | |
| <color name="md_red_100">#FFCDD2</color> | |
| <color name="md_red_200">#EF9A9A</color> |
| $mimeTypes = array( | |
| 'x3d' => array( | |
| 'name' => '3D Crossword Plugin', | |
| 'type' => 'application/vnd.hzn-3d-crossword' | |
| ), | |
| '3gp' => array( | |
| 'name' => '3GP', | |
| 'type' => 'video/3gpp' | |
| ), | |
| '3g2' => array( |
| private boolean isServiceRunning() { | |
| ActivityManager manager = (ActivityManager) getSystemService(ACTIVITY_SERVICE); | |
| for (RunningServiceInfo service : manager.getRunningServices(Integer.MAX_VALUE)){ | |
| if("com.example.MyNeatoIntentService".equals(service.service.getClassName())) { | |
| return true; | |
| } | |
| } | |
| return false; | |
| } |
| i | |
| me | |
| my | |
| myself | |
| we | |
| our | |
| ours | |
| ourselves | |
| you | |
| your |