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Douwe Osinga
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Requirements update (#63)
* Requirements and doc update * Sort * Update notebook too
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‎05.1 Generating Text in the Style of an Example Text.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Installing gutenberg is tricky, so it is not included in the requirements.txt\n",
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"# For it to work we need a berkelydb version <= 6 for licensing reasons. On OSX\n",
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"# using brew you can do:\n",
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"# brew install berkeley-db@4\n",
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"\n",
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"!pip install gutenberg\n",
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"\n",
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"# If this doesn't work, this notebook should still run, just not fetching data\n",
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"# from gutenberg."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

‎08.2 Import Gutenberg.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting gutenberg\n",
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" Using cached https://files.pythonhosted.org/packages/14/b1/6e99867c38e70d46366966a0a861c580377f38312cf9dbad38b82ed1823d/Gutenberg-0.7.0.tar.gz\n",
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"Collecting bsddb3>=6.1.0 (from gutenberg)\n",
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" Using cached https://files.pythonhosted.org/packages/e9/fc/ebfbd4de236b493f9ece156f816c21df0ae87ccc22604c5f9b664efef1b9/bsddb3-6.2.6.tar.gz\n",
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"Collecting future>=0.15.2 (from gutenberg)\n",
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"Collecting rdflib-sqlalchemy>=0.3.8 (from gutenberg)\n",
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" Using cached https://files.pythonhosted.org/packages/92/a2/bc580a51ac1f9680aa04da4b6e96d499903d6e606d2f78f02e73527799da/rdflib_sqlalchemy-0.3.8-py3-none-any.whl\n",
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"Collecting rdflib>=4.2.0 (from gutenberg)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/3c/fe/630bacb652680f6d481b9febbb3e2c3869194a1a5fc3401a4a41195a2f8f/rdflib-4.2.2-py3-none-any.whl (344kB)\n",
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"\u001b[K |████████████████████████████████| 348kB 4.0MB/s eta 0:00:01\n",
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"\u001b[?25hRequirement already satisfied: requests>=2.5.1 in ./venv3/lib/python3.7/site-packages (from gutenberg) (2.22.0)\n",
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"Requirement already satisfied: setuptools>=18.5 in ./venv3/lib/python3.7/site-packages (from gutenberg) (41.0.1)\n",
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"Requirement already satisfied: six>=1.10.0 in ./venv3/lib/python3.7/site-packages (from gutenberg) (1.12.0)\n",
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"Collecting SQLAlchemy>=1.1.4 (from rdflib-sqlalchemy>=0.3.8->gutenberg)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/55/98/56b7155bab287cd0c78dee26258835db36e91f2efef41f125ed6f6f1f334/SQLAlchemy-1.3.6.tar.gz (5.9MB)\n",
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"\u001b[K |████████████████████████████████| 5.9MB 22.9MB/s eta 0:00:01\n",
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"\u001b[?25hCollecting alembic>=0.8.8 (from rdflib-sqlalchemy>=0.3.8->gutenberg)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7b/8b/0c98c378d93165d9809193f274c3c6e2151120d955b752419c7d43e4d857/alembic-1.0.11.tar.gz (1.0MB)\n",
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"\u001b[K |████████████████████████████████| 1.0MB 27.3MB/s eta 0:00:01\n",
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"\u001b[?25hRequirement already satisfied: pyparsing in ./venv3/lib/python3.7/site-packages (from rdflib>=4.2.0->gutenberg) (2.4.1)\n",
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"Collecting isodate (from rdflib>=4.2.0->gutenberg)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/9b/9f/b36f7774ff5ea8e428fdcfc4bb332c39ee5b9362ddd3d40d9516a55221b2/isodate-0.6.0-py2.py3-none-any.whl (45kB)\n",
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"\u001b[K |████████████████████████████████| 51kB 8.3MB/s eta 0:00:01\n",
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"\u001b[?25hRequirement already satisfied: chardet<3.1.0,>=3.0.2 in ./venv3/lib/python3.7/site-packages (from requests>=2.5.1->gutenberg) (3.0.4)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in ./venv3/lib/python3.7/site-packages (from requests>=2.5.1->gutenberg) (2019.6.16)\n",
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"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in ./venv3/lib/python3.7/site-packages (from requests>=2.5.1->gutenberg) (1.25.3)\n",
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"Requirement already satisfied: idna<2.9,>=2.5 in ./venv3/lib/python3.7/site-packages (from requests>=2.5.1->gutenberg) (2.8)\n",
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"Collecting Mako (from alembic>=0.8.8->rdflib-sqlalchemy>=0.3.8->gutenberg)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1b/a5/023aba3d69aacef6bfc13797bdc3dd03c6fb4ae2dcd2fde7dffc37233924/Mako-1.0.14.tar.gz (462kB)\n",
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"\u001b[K |████████████████████████████████| 471kB 10.3MB/s eta 0:00:01\n",
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"\u001b[?25hCollecting python-editor>=0.3 (from alembic>=0.8.8->rdflib-sqlalchemy>=0.3.8->gutenberg)\n",
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" Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
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"Requirement already satisfied: python-dateutil in ./venv3/lib/python3.7/site-packages (from alembic>=0.8.8->rdflib-sqlalchemy>=0.3.8->gutenberg) (2.8.0)\n",
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"Requirement already satisfied: MarkupSafe>=0.9.2 in ./venv3/lib/python3.7/site-packages (from Mako->alembic>=0.8.8->rdflib-sqlalchemy>=0.3.8->gutenberg) (1.1.1)\n",
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"Building wheels for collected packages: gutenberg, bsddb3, SQLAlchemy, alembic, Mako\n",
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" Building wheel for gutenberg (setup.py) ... \u001b[?25ldone\n",
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"\u001b[?25h Stored in directory: /Users/douwe/Library/Caches/pip/wheels/8e/cd/75/4bc6f16541a1b7a69b02168da567695b2271c23ac4a0a0a453\n",
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" Building wheel for bsddb3 (setup.py) ... \u001b[?25ldone\n",
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"\u001b[?25h Stored in directory: /Users/douwe/Library/Caches/pip/wheels/11/b8/b3/fa84db10bf8c563e4ba1a72837a0946d123f12adb34b164bf5\n",
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" Building wheel for SQLAlchemy (setup.py) ... \u001b[?25ldone\n",
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"\u001b[?25h Stored in directory: /Users/douwe/Library/Caches/pip/wheels/f2/ec/e0/d7deb0c981557e373edf7370574b7001690892afe5fea30c3c\n",
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" Building wheel for alembic (setup.py) ... \u001b[?25ldone\n",
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"\u001b[?25h Stored in directory: /Users/douwe/Library/Caches/pip/wheels/8b/65/b2/9837b4422d13e739c3324c428f1b3aa9e3c3df666bb420e4b3\n",
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" Building wheel for Mako (setup.py) ... \u001b[?25ldone\n",
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"\u001b[?25h Stored in directory: /Users/douwe/Library/Caches/pip/wheels/36/0e/1d/4b28d9bc3f835432132648d6d1d038a7b3d9dbfd7df453e0f1\n",
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"Successfully built gutenberg bsddb3 SQLAlchemy alembic Mako\n",
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"Installing collected packages: bsddb3, future, SQLAlchemy, isodate, rdflib, Mako, python-editor, alembic, rdflib-sqlalchemy, gutenberg\n",
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"Successfully installed Mako-1.0.14 SQLAlchemy-1.3.6 alembic-1.0.11 bsddb3-6.2.6 future-0.17.1 gutenberg-0.7.0 isodate-0.6.0 python-editor-1.0.4 rdflib-4.2.2 rdflib-sqlalchemy-0.3.8\n"
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]
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}
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],
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"source": [
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"# Installing gutenberg is tricky, so it is not included in the requirements.txt\n",
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"# For it to work we need a berkelydb version <= 6 for licensing reasons. On OSX\n",
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"# using brew you can do:\n",
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"# brew install berkeley-db@4\n",
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"\n",
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"!pip install gutenberg"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

‎README.md‎

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# Deep Learning Cookbook Notebooks
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This repository contains 36 python notebooks demonstrating most of the key
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This repository contains 35 python notebooks demonstrating most of the key
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machine learning techniques in Keras. The notebooks accompany the book
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[Deep Learning Cookbook](https://www.amazon.com/Deep-Learning-Cookbook-Practical-Recipes) but work well on their own. A GPU is not required to run them,
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but on a mere CPU things will take quite a while.
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#### [08.1 Sequence to sequence mapping](https://github.com/DOsinga/deep_learning_cookbook/blob/master/08.1%20Sequence%20to%20sequence%20mapping.ipynb)
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#### [08.2 Import Gutenberg](https://github.com/DOsinga/deep_learning_cookbook/blob/master/08.2%20Import%20Gutenberg.ipynb)
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#### [08.3 Subword tokenizing](https://github.com/DOsinga/deep_learning_cookbook/blob/master/08.3%20Subword%20tokenizing.ipynb)
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Small notebook demonstrating how to download books from the Gutenberg project. Tokenizes a set of book in preparation of
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the subword tokenizing in the next notebook.
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#### [09.1 Reusing a pretrained image recognition network](https://github.com/DOsinga/deep_learning_cookbook/blob/master/09.1%20Reusing%20a%20pretrained%20image%20recognition%20network.ipynb)
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Quick notebook demonstrating how to load a pretrained network and apply it on an image of, well, what else? a cat.
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Shows how to normalize the image and decode the predictions.
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#### [09.2 Images as embeddings](https://github.com/DOsinga/deep_learning_cookbook/blob/master/09.2%20Images%20as%20embeddings.ipynb)
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In this notebook we use the Flickr API to fetch a feed of search results for the search term cat. By running each result through a pre-trained network
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we get vectors that project the images in a 'space'. The center of that space in some way represents the most cat image possible. By reranking on
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distance to that center we can weed out images that are less cat like. Effectively we can improve upon the Flickr search results without
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knowing the content!
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#### [09.3 Retraining](https://github.com/DOsinga/deep_learning_cookbook/blob/master/09.3%20Retraining.ipynb)
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#### [10.1 Building an inverse image search service](https://github.com/DOsinga/deep_learning_cookbook/blob/master/10.1%20Building%20an%20inverse%20image%20search%20service.ipynb)
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#### [11.1 Detecting Multiple Images](https://github.com/DOsinga/deep_learning_cookbook/blob/master/11.1%20Detecting%20Multiple%20Images.ipynb)

‎requirements.in‎

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Pillow
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bs4
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emoji
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flickrapi
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gensim
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geopandas
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gutenberg
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h5py
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imageio
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inflect
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mwparserfromhell
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nltk
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notebook
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Pillow
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plyvel
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psycopg2-binary
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pyyaml
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sklearn
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spotipy
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svglib
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tensorflow
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tensorflow==1.14
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tqdm
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twitter

‎requirements.txt‎

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#
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# pip-compile
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#
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absl-py==0.7.1 # via tensorboard, tensorflow, tensorflow-estimator
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alembic==1.0.10 # via rdflib-sqlalchemy
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absl-py==0.7.1 # via tensorboard, tensorflow
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appnope==0.1.0 # via ipython
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args==0.1.0 # via clint
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astor==0.7.1 # via tensorflow
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astor==0.8.0 # via tensorflow
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attrs==19.1.0 # via fiona, jsonschema
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audioread==2.1.7 # via librosa
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audioread==2.1.8 # via librosa
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backcall==0.1.0 # via ipython
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backports.csv==1.0.7 # via internetarchive
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beautifulsoup4==4.7.1 # via bs4
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beautifulsoup4==4.8.0 # via bs4
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bleach==3.1.0 # via nbconvert
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boto3==1.9.151 # via smart-open
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boto3==1.9.192 # via smart-open
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boto==2.49.0 # via smart-open
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botocore==1.12.151 # via boto3, s3transfer
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botocore==1.12.192 # via boto3, s3transfer
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bs4==0.0.1
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bsddb3==6.2.6 # via gutenberg
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certifi==2019.3.9 # via requests
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certifi==2019.6.16 # via requests
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cffi==1.12.3 # via soundfile
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chardet==3.0.4 # via requests
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click-plugins==1.1.1 # via fiona
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click==7.0 # via click-plugins, cligj, fiona
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entrypoints==0.3 # via nbconvert
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fiona==1.8.6 # via geopandas
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flickrapi==2.4.0
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future==0.17.1 # via gutenberg
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gast==0.2.2 # via tensorflow
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gensim==3.7.3
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geopandas==0.5.0
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grpcio==1.20.1 # via tensorboard, tensorflow
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gutenberg==0.7.0
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gensim==3.8.0
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geopandas==0.5.1
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google-pasta==0.1.7 # via tensorflow
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grpcio==1.22.0 # via tensorboard, tensorflow
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h5py==2.9.0
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idna==2.8 # via requests
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imageio==2.5.0
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inflect==2.1.0
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internetarchive==1.8.4
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internetarchive==1.8.5
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ipykernel==5.1.1 # via ipywidgets, jupyter, jupyter-console, notebook, qtconsole
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ipython-genutils==0.2.0 # via nbformat, notebook, qtconsole, traitlets
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ipython==7.5.0
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ipywidgets==7.4.2 # via jupyter
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isodate==0.6.0 # via rdflib
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jedi==0.13.3 # via ipython
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ipython==7.6.1
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ipywidgets==7.5.0 # via jupyter
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jedi==0.14.1 # via ipython
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jinja2==2.10.1 # via nbconvert, notebook
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jmespath==0.9.4 # via boto3, botocore
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joblib==0.13.2 # via librosa, scikit-learn
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jsonpatch==1.23 # via internetarchive
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jsonpointer==2.0 # via jsonpatch
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jsonschema==3.0.1 # via nbformat
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jupyter-client==5.2.4 # via ipykernel, jupyter-console, notebook, qtconsole
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jupyter-client==5.3.1 # via ipykernel, jupyter-console, notebook, qtconsole
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jupyter-console==6.0.0 # via jupyter
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jupyter-core==4.4.0 # via jupyter-client, nbconvert, nbformat, notebook, qtconsole
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jupyter-core==4.5.0 # via jupyter-client, nbconvert, nbformat, notebook, qtconsole
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jupyter==1.0.0
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keras-applications==1.0.7 # via keras, tensorflow
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keras-preprocessing==1.0.9 # via keras, tensorflow
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keras-applications==1.0.8 # via keras, tensorflow
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keras-preprocessing==1.1.0 # via keras, tensorflow
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keras==2.2.4
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kiwisolver==1.1.0 # via matplotlib
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librosa==0.6.3
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llvmlite==0.28.0 # via numba
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lxml==4.3.3 # via svglib
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mako==1.0.10 # via alembic
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markdown==3.1 # via tensorboard
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markupsafe==1.1.1 # via jinja2, mako
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matplotlib==3.0.3
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librosa==0.7.0
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llvmlite==0.29.0 # via numba
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lxml==4.3.4 # via svglib
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markdown==3.1.1 # via tensorboard
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markupsafe==1.1.1 # via jinja2
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matplotlib==3.1.1
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mistune==0.8.4 # via nbconvert
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mock==3.0.5 # via tensorflow-estimator
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munch==2.3.2 # via fiona
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mwparserfromhell==0.5.4
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nbconvert==5.5.0 # via jupyter, notebook
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nbformat==4.4.0 # via ipywidgets, nbconvert, notebook
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nltk==3.4.1
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notebook==5.7.8
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numba==0.43.1 # via librosa, resampy
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numpy==1.16.3 # via gensim, h5py, imageio, keras, keras-applications, keras-preprocessing, librosa, matplotlib, numba, pandas, resampy, scikit-learn, scipy, tensorboard, tensorflow, tensorflow-estimator
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oauthlib==3.0.1 # via requests-oauthlib
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pandas==0.24.2 # via geopandas
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nltk==3.4.4
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notebook==6.0.0
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numba==0.45.0 # via librosa, resampy
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numpy==1.16.4 # via gensim, h5py, imageio, keras, keras-applications, keras-preprocessing, librosa, matplotlib, numba, pandas, resampy, scikit-learn, scipy, tensorboard, tensorflow
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oauthlib==3.0.2 # via requests-oauthlib
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pandas==0.25.0 # via geopandas
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pandocfilters==1.4.2 # via nbconvert
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parso==0.4.0 # via jedi
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parso==0.5.1 # via jedi
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pexpect==4.7.0 # via ipython
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pickleshare==0.7.5 # via ipython
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pillow==6.0.0
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pillow==6.1.0
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plyvel==1.1.0
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prometheus-client==0.6.0 # via notebook
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prometheus-client==0.7.1 # via notebook
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prompt-toolkit==2.0.9 # via ipython, jupyter-console
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protobuf==3.7.1 # via tensorboard, tensorflow
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psycopg2-binary==2.8.2
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protobuf==3.9.0 # via tensorboard, tensorflow
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psycopg2-binary==2.8.3
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ptyprocess==0.6.0 # via pexpect, terminado
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pygments==2.4.0 # via ipython, jupyter-console, nbconvert, qtconsole
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pyparsing==2.4.0 # via matplotlib, rdflib
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pyproj==2.1.3 # via geopandas
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pyrsistent==0.15.2 # via jsonschema
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python-dateutil==2.8.0 # via alembic, botocore, jupyter-client, matplotlib, pandas
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python-editor==1.0.4 # via alembic
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pycparser==2.19 # via cffi
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pygments==2.4.2 # via ipython, jupyter-console, nbconvert, qtconsole
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pyparsing==2.4.1 # via matplotlib
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pyproj==2.2.1 # via geopandas
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pyrsistent==0.15.3 # via jsonschema
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python-dateutil==2.8.0 # via botocore, jupyter-client, matplotlib, pandas
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pytz==2019.1 # via pandas
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pyyaml==5.1
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pyzmq==18.0.1 # via jupyter-client, notebook
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qtconsole==4.4.4 # via jupyter
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rdflib-sqlalchemy==0.3.8 # via gutenberg
111-
rdflib==4.2.2 # via gutenberg, rdflib-sqlalchemy
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reportlab==3.5.21 # via svglib
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pyyaml==5.1.1
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pyzmq==18.0.2 # via jupyter-client, notebook
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qtconsole==4.5.1 # via jupyter
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reportlab==3.5.23 # via svglib
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requests-oauthlib==1.2.0 # via flickrapi
114108
requests-toolbelt==0.9.1 # via flickrapi
115-
requests==2.22.0 # via flickrapi, gutenberg, internetarchive, requests-oauthlib, requests-toolbelt, smart-open, spotipy
109+
requests==2.22.0 # via flickrapi, internetarchive, requests-oauthlib, requests-toolbelt, smart-open, spotipy
116110
resampy==0.2.1 # via librosa
117-
s3transfer==0.2.0 # via boto3
111+
s3transfer==0.2.1 # via boto3
118112
schema==0.7.0 # via internetarchive
119-
scikit-learn==0.21.1 # via librosa, sklearn
113+
scikit-learn==0.21.2 # via librosa, sklearn
120114
scipy==1.3.0 # via gensim, keras, librosa, resampy, scikit-learn
121115
send2trash==1.5.0 # via notebook
122116
shapely==1.6.4.post2 # via geopandas
123-
singledispatch==3.4.0.3 # via nltk
124-
six==1.12.0 # via absl-py, bleach, cycler, fiona, flickrapi, gensim, grpcio, gutenberg, h5py, internetarchive, isodate, jsonschema, keras, keras-preprocessing, librosa, mock, munch, nltk, prompt-toolkit, protobuf, pyrsistent, python-dateutil, rdflib-sqlalchemy, resampy, singledispatch, tensorboard, tensorflow, tensorflow-estimator, traitlets
117+
six==1.12.0 # via absl-py, bleach, cycler, fiona, flickrapi, gensim, grpcio, h5py, internetarchive, jsonschema, keras, keras-preprocessing, librosa, munch, nltk, prompt-toolkit, protobuf, pyrsistent, python-dateutil, resampy, tensorboard, tensorflow, traitlets
125118
sklearn==0.0
126-
smart-open==1.8.3 # via gensim
127-
soupsieve==1.9.1 # via beautifulsoup4
119+
smart-open==1.8.4 # via gensim
120+
soundfile==0.10.2 # via librosa
121+
soupsieve==1.9.2 # via beautifulsoup4
128122
spotipy==2.4.4
129-
sqlalchemy==1.3.3 # via alembic, rdflib-sqlalchemy
130-
svglib==0.9.0
131-
tensorboard==1.13.1 # via tensorflow
132-
tensorflow-estimator==1.13.0 # via tensorflow
133-
tensorflow==1.13.1
123+
svglib==0.9.2
124+
tensorboard==1.14.0 # via tensorflow
125+
tensorflow-estimator==1.14.0 # via tensorflow
126+
tensorflow==1.14
134127
termcolor==1.1.0 # via tensorflow
135128
terminado==0.8.2 # via notebook
136129
testpath==0.4.2 # via nbconvert
137130
tinycss2==1.0.2 # via cssselect2, svglib
138-
tornado==6.0.2 # via ipykernel, jupyter-client, notebook, terminado
131+
tornado==6.0.3 # via ipykernel, jupyter-client, notebook, terminado
139132
total-ordering==0.1.0 # via internetarchive
140-
tqdm==4.32.1
133+
tqdm==4.32.2
141134
traitlets==4.3.2 # via ipykernel, ipython, ipywidgets, jupyter-client, jupyter-core, nbconvert, nbformat, notebook, qtconsole
142135
twitter==1.18.0
143-
urllib3==1.24.3 # via botocore, requests
136+
urllib3==1.25.3 # via botocore, requests
144137
wcwidth==0.1.7 # via prompt-toolkit
145138
webencodings==0.5.1 # via bleach, tinycss2
146-
werkzeug==0.15.4 # via tensorboard
139+
werkzeug==0.15.5 # via tensorboard
147140
wheel==0.33.4 # via tensorboard, tensorflow
148-
widgetsnbextension==3.4.2 # via ipywidgets
141+
widgetsnbextension==3.5.0 # via ipywidgets
142+
wrapt==1.11.2 # via tensorflow
143+
144+
# The following packages are considered to be unsafe in a requirements file:
145+
# setuptools==41.0.1 # via ipython, jsonschema, kiwisolver, markdown, protobuf, tensorboard, tinycss2

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