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Deep Learning with Keras

Tutorial @ EuroScipy 2016

Yam Peleg, Valerio Maggio

Goal of this Tutorial

  • Introduce main features of Keras
  • Learn how simple and Pythonic is doing Deep Learning with Keras
  • Understand how easy is to do basic and advanced DL models in Keras;
    • Examples and Hand-on Excerises along the way.

Source

https://github.com/leriomaggio/deep-learning-keras-euroscipy2016/

(Tentative) Schedule

Attention: Spoilers Warning!

  • Setup (10 mins)

  • Part I: Introduction (~65 mins)

    • Intro to ANN (~20 mins)

      • naive pure-Python implementation
      • fast forward, sgd, backprop

    • Intro to Theano (15 mins)

      • Model + SGD with Theano

    • Introduction to Keras (30 mins)

      • Overview and main features
        • Theano backend
        • Tensorflow backend
      • Multi-Layer Perceptron and Fully Connected
        • Examples with keras.models.Sequential and Dense
        • HandsOn: MLP with keras

  • Coffe Break (30 mins)

  • Part II: Supervised Learning and Convolutional Neural Nets (~45 mins)

    • Intro: Focus on Image Classification (5 mins)

    • Intro to CNN (25 mins)

      • meaning of convolutional filters
        • examples from ImageNet
      • Meaning of dimensions of Conv filters (through an exmple of ConvNet)
      • Visualising ConvNets
      • HandsOn: ConvNet with keras

    • Advanced CNN (10 mins)

      • Dropout
      • MaxPooling
      • Batch Normalisation

    • Famous Models in Keras (likely moved somewhere else) (10 mins) (ref: https://github.com/fchollet/deep-learning-models) - VGG16 - VGG19 - ResNet50 - Inception v3

      • HandsOn: Fine tuning a network on new dataset

  • Part III: Unsupervised Learning (10 mins)

    • AutoEncoders (5 mins)
    • word2vec & doc2vec (gensim) & keras.datasets (5 mins)
      • Embedding
      • word2vec and CNN
    • Exercises

  • Part IV: Advanced Materials (20 mins)

    • RNN and LSTM (10 mins)
      • RNN, LSTM, GRU
    • Example of RNN and LSTM with Text (~10 mins) -- Tentative
    • HandsOn: IMDB

  • Wrap up and Conclusions (5 mins)

Requirements

This tutorial requires the following packages:

(Optional but recommended):

The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures.

Python Version

I'm currently running this tutorial with Python 3 on Anaconda

!python --version
Python 3.5.2

How to set up your environment

The quickest and simplest way to setup the environment is to use conda environment manager.

We provide in the materials a deep-learning.yml that is complete and ready to use to set up your virtual environment with conda.

!cat deep-learning.yml
name: deep-learning
channels:
- conda-forge
- defaults
dependencies:
- accelerate=2.3.0=np111py35_3
- accelerate_cudalib=2.0=0
- bokeh=0.12.1=py35_0
- cffi=1.6.0=py35_0
- backports.shutil_get_terminal_size=1.0.0=py35_0
- blas=1.1=openblas
- ca-certificates=2016.8.2=3
- cairo=1.12.18=8
- certifi=2016.8.2=py35_0
- cycler=0.10.0=py35_0
- cython=0.24.1=py35_0
- decorator=4.0.10=py35_0
- entrypoints=0.2.2=py35_0
- fontconfig=2.11.1=3
- freetype=2.6.3=1
- gettext=0.19.7=1
- glib=2.48.0=4
- h5py=2.6.0=np111py35_6
- harfbuzz=1.0.6=0
- hdf5=1.8.17=2
- icu=56.1=4
- ipykernel=4.3.1=py35_1
- ipython=5.1.0=py35_0
- ipywidgets=5.2.2=py35_0
- jinja2=2.8=py35_1
- jpeg=9b=0
- jsonschema=2.5.1=py35_0
- jupyter_client=4.3.0=py35_0
- jupyter_console=5.0.0=py35_0
- jupyter_core=4.1.1=py35_1
- libffi=3.2.1=2
- libiconv=1.14=3
- libpng=1.6.24=0
- libsodium=1.0.10=0
- libtiff=4.0.6=6
- libxml2=2.9.4=0
- markupsafe=0.23=py35_0
- matplotlib=1.5.2=np111py35_6
- mistune=0.7.3=py35_0
- nbconvert=4.2.0=py35_0
- nbformat=4.0.1=py35_0
- ncurses=5.9=8
- nose=1.3.7=py35_1
- notebook=4.2.2=py35_0
- numpy=1.11.1=py35_blas_openblas_201
- openblas=0.2.18=4
- openssl=1.0.2h=2
- pandas=0.18.1=np111py35_1
- pango=1.40.1=0
- path.py=8.2.1=py35_0
- pcre=8.38=1
- pexpect=4.2.0=py35_1
- pickleshare=0.7.3=py35_0
- pip=8.1.2=py35_0
- pixman=0.32.6=0
- prompt_toolkit=1.0.6=py35_0
- protobuf=3.0.0b3=py35_1
- ptyprocess=0.5.1=py35_0
- pygments=2.1.3=py35_1
- pyparsing=2.1.7=py35_0
- python=3.5.2=2
- python-dateutil=2.5.3=py35_0
- pytz=2016.6.1=py35_0
- pyyaml=3.11=py35_0
- pyzmq=15.4.0=py35_0
- qt=4.8.7=0
- qtconsole=4.2.1=py35_0
- readline=6.2=0
- requests=2.11.0=py35_0
- scikit-learn=0.17.1=np111py35_blas_openblas_201
- scipy=0.18.0=np111py35_blas_openblas_201
- setuptools=25.1.6=py35_0
- simplegeneric=0.8.1=py35_0
- sip=4.18=py35_0
- six=1.10.0=py35_0
- sqlite=3.13.0=1
- terminado=0.6=py35_0
- tk=8.5.19=0
- tornado=4.4.1=py35_1
- traitlets=4.2.2=py35_0
- wcwidth=0.1.7=py35_0
- wheel=0.29.0=py35_0
- widgetsnbextension=1.2.6=py35_3
- xz=5.2.2=0
- yaml=0.1.6=0
- zeromq=4.1.5=0
- zlib=1.2.8=3
- cudatoolkit=7.5=0
- ipython_genutils=0.1.0=py35_0
- jupyter=1.0.0=py35_3
- libgfortran=3.0.0=1
- llvmlite=0.11.0=py35_0
- mkl=11.3.3=0
- mkl-service=1.1.2=py35_2
- numba=0.26.0=np111py35_0
- pycparser=2.14=py35_1
- pyqt=4.11.4=py35_4
- snakeviz=0.4.1=py35_0
- pip:
  - backports.shutil-get-terminal-size==1.0.0
  - certifi==2016.8.2
  - cycler==0.10.0
  - cython==0.24.1
  - decorator==4.0.10
  - h5py==2.6.0
  - ipykernel==4.3.1
  - ipython==5.1.0
  - ipython-genutils==0.1.0
  - ipywidgets==5.2.2
  - jinja2==2.8
  - jsonschema==2.5.1
  - jupyter-client==4.3.0
  - jupyter-console==5.0.0
  - jupyter-core==4.1.1
  - keras==1.0.7
  - mako==1.0.4
  - markupsafe==0.23
  - matplotlib==1.5.2
  - mistune==0.7.3
  - nbconvert==4.2.0
  - nbformat==4.0.1
  - nose==1.3.7
  - notebook==4.2.2
  - numpy==1.11.1
  - pandas==0.18.1
  - path.py==8.2.1
  - pexpect==4.2.0
  - pickleshare==0.7.3
  - pip==8.1.2
  - prompt-toolkit==1.0.6
  - protobuf==3.0.0b2
  - ptyprocess==0.5.1
  - pygments==2.1.3
  - pygpu==0.2.1
  - pyparsing==2.1.7
  - python-dateutil==2.5.3
  - pytz==2016.6.1
  - pyyaml==3.11
  - pyzmq==15.4.0
  - qtconsole==4.2.1
  - requests==2.11.0
  - scikit-learn==0.17.1
  - scipy==0.18.0
  - setuptools==25.1.4
  - simplegeneric==0.8.1
  - six==1.10.0
  - tensorflow==0.10.0rc0
  - terminado==0.6
  - theano==0.8.2
  - tornado==4.4.1
  - traitlets==4.2.2
  - wcwidth==0.1.7
  - wheel==0.29.0
  - widgetsnbextension==1.2.6
prefix: /home/valerio/anaconda3/envs/deep-learning

Recreate the Conda Environment

A. Create the Environment

conda env create -f deep-learning.yml  # this file is for Linux channels.

If you're using a Mac OSX, we also provided in the repo the conda file that is compatible with osx-channels:

conda env create -f deep-learning-osx.yml  # this file is for OSX channels.

B. Activate the new deep-learning Environment

source activate deep-learning

Optionals

1. Enabling Conda-Forge

It is strongly suggested to enable conda forge in your Anaconda installation.

Conda-Forge is a github organisation containing repositories of conda recipies.

To add conda-forge as an additional anaconda channel it is just required to type:

conda config --add channels conda-forge

2. Configure Theano

  1. Create the theanorc file:
touch $HOME/.theanorc
  1. Copy the following content into the file:
[global]
floatX = float32
device = gpu  # switch to cpu if no GPU is available on your machine

[nvcc]
fastmath = True

[lib]
cnmem=.90

More on theano documentation

3. Installing Tensorflow as backend

# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl

pip install --ignore-installed --upgrade $TF_BINARY_URL

More on tensorflow documentation

Test if everything is up&running

1. Check import

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import sklearn
import keras
Using Theano backend.
Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)

2. Check installeded Versions

import numpy
print('numpy:', numpy.__version__)

import scipy
print('scipy:', scipy.__version__)

import matplotlib
print('matplotlib:', matplotlib.__version__)

import IPython
print('iPython:', IPython.__version__)

import sklearn
print('scikit-learn:', sklearn.__version__)
numpy: 1.11.1
scipy: 0.18.0
matplotlib: 1.5.2
iPython: 5.1.0
scikit-learn: 0.17.1

import keras
print('keras: ', keras.__version__)

import theano
print('Theano: ', theano.__version__)

# optional
import tensorflow as tf
print('Tensorflow: ', tf.__version__)
keras:  1.0.7
Theano:  0.8.2
Tensorflow:  0.10.0rc0

If everything worked till down here, you're ready to start!

Consulting Material

You have two options to go through the material presented in this tutorial:

  • Read (and execute) the material as iPython/Jupyter notebooks
  • (just) read the material as (HTML) slides

In the first case, all you need to do is just execute ipython notebook (or jupyter notebook) depending on the version of iPython you have installed on your machine

(jupyter command works in case you have iPython 4.0.x installed)

In the second case, you may simply convert the provided notebooks in HTML slides and see them into your browser thanks to nbconvert.

Thus, move to the folder where notebooks are stored and execute the following command:

jupyter nbconvert --to slides ./*.ipynb --post serve

(Please substitute jupyter with ipython in the previous command if you have iPython 3.x installed on your machine)

In case...

..you wanna do both (interactive and executable slides), I highly suggest to install the terrific RISE ipython notebook extension: https://github.com/damianavila/RISE