This PyData San Luis Argentina 2017 tutorial will focus on practical aspects of implementing and training deep learning models. We will follow, step by step, the implementation of linear and nonlinear models with Tensorflow, focusing on the impact of tuning hyperparameters and architectures. To keep track of results we will use the visualization tool Tensorboard and show its functionalities.
The goal of this tutorial is for you to learn how to build models in Tensorflow. You will apply them to a standard document classification dataset, and keep track of the experiments’ performance with Tensorboard, a visualization tool.
Who is this course aimed to?
We strongly recommend to have covered some machine learning basic concepts previously, as we wont cover them during the tutorial:
- What is a classifier? What is a logistic regression classifier?
- Basic evaluation: metrics like accuracy, precision, and recall. What is the train and test split for?
- What is gradient descent and what is a cost function?
This tutorial is based in Jupyter notebooks we uploaded to the Github platform. The notebook named as Part 0: Configuration (which can be rendered directly from Github) has all the software and data requirements needed to configure the environment. It should be followed before taking the tutorial in the conference.
The slides shown in the tutorial presentation are available using this link.
The software packages we are using are Python 3.5 (obtained through conda) along Numpy, Scipy, Jupyter, and the Tensorflow (version 1.3) libraries.
Please refer to the notebook for detailed explanation.
The dataset we will use is a pre-processed version of the 20 Newsgroup Corpus for document classification. Part 1 of the tutorial is optional and explains how this dataset is obtained. In any case it is possible to download the dataset directly to use in Tensorflow from part 2 and after of the tutorial.
Please refer to the notebook for detailed explanation and download link.
The Jupyter notebooks of the tutorial contain code and explanations that the attendees can download, execute and modify during the talk. It will comprise the following parts:
Introduction and environment setup
This part is covered by the Part 0: Configuration notebook. It has the instruction to configure the environment and download the dataset to use.
Dataset preprocessing (optional)
This part is covered by the Part 1: Dataset Preprocessing notebook. It explains the dataset we are using to experiment with Tensorflow and how we process it to have the final version ready.
Basic concepts of Tensorflow
This part is covered by the Part 2: The Basics of Tensorflow notebook. It explains the implementation of a linear model using a Tensorflow estimator to exemplify the different components of the Tensorflow pipeline. This includes compilation of models and how Tensorflow is executing your code.
Advanced concepts of Tensorflow
This part is covered by the Part 3: Advanced Tensorflow notebook. In this we will extend the linear model previously built into a multilayer perceptron using a custom built estimator (instead of the default provided by Tensorflow) using layers. We will cover the graph of Tensorflow and how to visualize it with Tensorboard.
The benefits of Tensorboard
This part is covered by the Part 4: Inspecting the model notebook. We will present the Tensorboard interface and how we can use it to make the experimentation process easier by collecting and visualizing the results.
Tensorboard for embeddings (bonus)
This final part is covered by the Part 5: Document embeddings notebook. It explains how to use Tensorboard to visualize embeddings of different kinds (in this case documents).
About the authors
I’m a Computer Scientist based in Córdoba, Argentina. I’m currently pursuing a PhD in Computing under the direction of Laura Alonso Alemany at Universidad Nacional de Córdoba and Marcelo Luis Errecalde, from the Universidad Nacional de San Luis.
I have a degree in Computer Sciences from Universidad Nacional de Córdoba, Argentina, and I am finishing my PhD. in the area of machine learning applied to natural language processing.