This repository contains the slides and exercises for the Theano tutorial at the Deep Learning School in Stanford, September 24-25, 2016.
The tutorials are written in Python, using Theano. They are designed to be run locally on a laptop, without using a GPU.
The simplest way to install a Python software stack with most dependencies is to use Anaconda.
For Windows users, please use the Python 2.7 version (or use a conda environment with Python 3.4), as Theano does not support Pyton 3.5 on Windows yet.
First, download and execute the installer. You can install it as a user
(you do not have to use sudo
). We recommend that you let the installer
make Anaconda the default Python version.
Then, in a terminal:
$ conda update conda
These additional steps are required for Windows:
Download Git, and execute the installer. This will be necessary to get the latest version of Theano and Fuel. We recommand you select "Use Git from the Windows Command Prompt" option, so you can execute all the following command lines from the regular Windows cmd shell.
Install a C++ compiler and Python DLL. From a shell:
conda install mingw libpython
If you do not follow these steps, the pydotprint
command will raise
an exception and fail, but the other functionalities of Theano would
still work.
$ sudo apt-get install graphviz
$ pip install pydot-ng
$ sudo yum install graphviz
$ pip install pydot-ng
Download graphviz from http://www.graphviz.org/Download_macos.php
Then, from a terminal:
$ pip install pydot-ng
Download graphviz from http://www.graphviz.org/Download_windows.php
Add to the PATH environment variable the directory where the binaries were installed, by default
C:\Program Files (x86)\Graphviz2.38\bin
Then, from a terminal:
pip install pydot-ng
There have been some improvement and bug fixes since the last release, so we will use the latest development version from GitHub.
Use --user
if you installed Anaconda for all users and only want to
install Theano for the current user.
$ pip install git+git://github.com/Theano/Theano.git [--user]
Note
If you are using Windows and selected "Use Git from Git Bash only" when installing Git, or if the command above failed because git is not available in the path, then you need to run the command line above from the "Git Bash" terminal instead of the regular Windows command prompt.
The LSTM tutorial relies on Fuel for on-the-fly data processing. We install the development version of Fuel from GitHub.
$ pip install git+git://github.com/mila-udem/fuel.git [--user]
If the command above failed with
.. ERROR:: Could not find a local HDF5 installation.
You may need to explicitly state where your local HDF5 headers and
library can be found by setting the ``HDF5_DIR`` environment
variable or by using the ``--hdf5`` command-line option.
run the following command and try again
$ conda install h5py pytables
Or, on MacOS
$ brew install homebrew/science/hdf5
First, clone this repository:
$ git clone https://github.com/lamblin/bayareadlschool.git
To use the Jupyter Notebooks, you have to launch the Jupyter server on the base directory:
$ jupyter notebook bayareadlschool
A new window or tab should open in your web browser. If it does not (or if you
want to use it in a different browser), the previous command should mention a
URL you can open, probably http://localhost:8888/. From there, you can
navigate to the .ipynb
files.