【学术】如何在15分钟内建立一个深度学习模型?

ip3 install lore
$ git clone https://github.com/montanalow/my_app.git
$ cd my_app 
$ lore install # caching all dependencies locally takes a few minutes 
the first time
$ lore server & 
$ curl "http://localhost:5000/product_popularity.Keras/predict.json?product_
name=Banana&department=produce"

# On Linux
$ pip install lore
# On OS X use homebrew python 2 or 3
$ brew install python3 && pip3 install lore

$ lore init my_app --python-version=3.6.4 --keras

$ cd my_app
$ lore generate scaffold product_popularity --keras --regression --holdout

# my_app/pipelines/product_popularity.py part 1

import os

from lore.encoders import Token, Unique, Norm
import lore.io
import lore.pipelines
import lore.env

import pandas


class Holdout(lore.pipelines.holdout.Base):
    # You can inspect the source data csv's yourself from the command line with:
    # $ wget https://s3.amazonaws.com/instacart-datasets/instacart_
online_grocery_shopping_2017_05_01.tar.gz
    # $ tar -xzvf instacart_online_grocery_shopping_2017_05_01.tar.gz

    def get_data(self):
        url = 'https://s3.amazonaws.com/instacart-datasets/instacart_online_
grocery_shopping_2017_05_01.tar.gz'

        # Lore will extract and cache files in lore.env.data_dir by default
        lore.io.download(url, cache=True, extract=True)
        
        # Defined to DRY up paths to 3rd party file hierarchy
        def read_csv(name):
            path = os.path.join(
                lore.env.data_dir,
                'instacart_2017_05_01',
                name + '.csv')
            return pandas.read_csv(path, encoding='utf8')
        
        # Published order data was split into irrelevant prior/train
        # sets, so we will combine them to re-purpose all the data.
        orders = read_csv('order_products__prior')
        orders = orders.append(read_csv('order_products__train'))

        # count how many times each product_id was ordered
        data = orders.groupby('product_id').size().to_frame('sales')

        # add product names and department ids to ordered product ids
        products = read_csv('products').set_index('product_id')
        data = data.join(products)

        # add department names to the department ids
        departments = read_csv('departments').set_index('department_id')
        data = data.set_index('department_id').join(departments)
        
        # Only return the columns we need for training
        data = data.reset_index()
        return data[['product_name', 'department', 'sales']]

# my_app/pipelines/product_popularity.py part 2

    def get_encoders(self):
        return (
            # An encoder to tokenize product names into max 15 tokens that
            # occur in the corpus at least 10 times. We also want the 
            # estimator to spend 5x as many resources on name vs department
            # since there are so many more words in english than there are
            # grocery store departments.
            Token('product_name', sequence_length=15, minimum_occurrences=10, 
embed_scale=5),
            # An encoder to translate department names into unique
            # identifiers that occur at least 50 times
            Unique('department', minimum_occurrences=50)
        )

    def get_output_encoder(self):
        # Sales is floating point which we could Pass encode directly to the
        # estimator, but Norm will bring it to small values around 0,
        # which are more amenable to deep learning.
        return Norm('sales')

# my_app/estimators/product_popularity.py

import lore.estimators.keras


class Keras(lore.estimators.keras.Regression):
    pass

# my_app/models/product_popularity.py

import lore.models.keras

import my_app.pipelines.product_popularity
import my_app.estimators.product_popularity


class Keras(lore.models.keras.Base):
    def __init__(self, pipeline=None, estimator=None):
        super(Keras, self).__init__(
            my_app.pipelines.product_popularity.Holdout(),
            my_app.estimators.product_popularity.Keras(
                hidden_layers=2,
                embed_size=4,
                hidden_width=256,
                batch_size=1024,
                sequence_embedding='lstm',
            )
        )

$ lore test tests.unit.test_product_popularity

$ lore fit my_app.models.product_popularity.Keras --test --score

$ tail -f logs/development.log

$ lore notebook

$ lore server &
$ curl "http://localhost:5000/product_popularity.Keras/predict.json?product_
name=Banana&department=produce"
$ curl "http://localhost:5000/product_popularity.Keras/predict.json?product_
name=Organic%20Banana&department=produce"
$ curl "http://localhost:5000/product_popularity.Keras/predict.json?product_
name=Green%20Banana&department=produce"
$ curl "http://localhost:5000/product_popularity.Keras/predict.json?product_
name=Brown%20Banana&department=produce"

$ git init .
$ git add .
$ git add -f models/my_app.models.product_popularity/Keras/1 # or your 
preferred fitting number to deploy
$ git commit -m "My first lore app!"

$ heroku login
$ heroku create
$ heroku config:set LORE_PROJECT=my_app
$ heroku config:set LORE_ENV=production
$ git push heroku master
$ heroku open

$ curl “`heroku info -s | grep web_url | cut -d= -f2`product_popularity.
Keras/predict.json?product_name=Banana&department=produce”