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Deep Learning Tutorial
by Geoff French
Table of Contents
- PyData Amsterdam 2017 slides
- Requirements
- Notebooks for the tutorial
- The Python modules
Slides
This repo has been used to accompany various talks that I have given, one of which was at PyData Amsterdam 2017. The slides for that talk probably refer to Theano and Lasagne; two neural network toolkits that are no longer developed and maintained. You can find these slides on Speakdeck.
Requirements
I recommend using the Anaconda Python distribution, using Python 3.6. Anaconda will provide Python, Numpy, Matplotlib and Scikit-image.
You will need to have the following installed:
- PyTorch and torchvision - get version 1.0.
- OpenCV - use
condato install it on Linux. On windows go to Unofficial Windows Binaries for Python Extension Packages, download the.whl(wheel) file and install it usingpip install <path_of_opencv_whl>.
Notebooks for the tutorial
Using a pre-trained VGG network
Using a pre-trained network to classify an image:
[Using a pretrained conv-net - VGG net.ipynb](TUTORIAL 01 - Using a pretrained conv-net - VGG net.ipynb)
Converting a network for use in a convolutional fashion:
[Using a pretrained VGG-19 conv-net to find a peacock.ipynb](TUTORIAL 02 - Using a pretrained VGG-19 conv-net to find a peacock.ipynb)
Saliency
Saliency tells you which parts of the image had most influence over a network's prediction. Two approaches are demonstrated: [region level saliency](TUTORIAL 03 - Image region-level saliency using VGG-19 conv-net.ipynb) and [pixel level saliency](TUTORIAL 04 - Image pixel-level saliency using VGG-19 conv-net.ipynb)
Transfer learning
Transfer learning is the process by which we adapt a pre-trained neural network for other uses. To demonstrate it we train a classifier on the Kaggle Cats vs Dogs dataset. There are 3 notebooks that demonstrate the effect of using transfer learning and data augmentation:
[Dogs vs cats with standard learning.ipynb](TUTORIAL 05 - Dogs vs cats with standard learning.ipynb) - train a neural network without transfer learning; gets around 10% error rate.
[Dogs vs cats with transfer learning.ipynb](TUTORIAL 05 - Dogs vs cats with transfer learning.ipynb) - train a neural network with transfer learning; gets 5-6% error rate
[Dogs vs cats with transfer learning and data augmentation.ipynb](TUTORIAL 05 - Dogs vs cats with transfer learning and data augmentation.ipynb) - train a neural network with transfer learning and data augmentation; gets 2.7-3.7% error rate
The Python modules
A brief description of the Python modules in this repo:
utils.py - utility functions - main converting images for use with pre-trained models
imagenet_classes.py - provides a dictionary if ImageNet class names, so that we can give meaningful names to the predictions of a pre-trained network.