how to compute true/false positives and true/false negatives in python for binary classification problems

Here’s how to compute true positives, false positives, true negatives, and false negatives in Python using the Numpy library.

Note that we are assuming a binary classification problem here. That is a value of 1 indicates a positive class, and a value of 0 indicates a negative class. For multi-class problems, this doesn’t really hold.

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Deep Dreams and a Neural Algorithm of Artistic Style – slides and explanations

Perhaps you saw an earlier post I wrote about deep dreaming Prague pictures, and you said to your self, “self, I wish I knew more about the techniques to make those crazy looking pictures.”

Well you are in luck since I’ve now posted the slides where I attempted to explain these two works to our reading group: 1) Google’s DeepDream, and 2) A Neural Algorithm of Artistic Style.

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Napoleon: A Life – by Andrew Robert – audiobook review and notes

I recently finished the audiobook, “Napoleon: A Life” by Andrew Roberts. As you may expect from the title, this hefty audiobook (nearly 33 hours) gives great detail on the life of Napoleon Bonaparte.

What struck me most about the life of Napoleon was his ability to cope – no, more than cope, he thrived – in such an environment of confrontation. Napoleon waged wars against the strongest powers in Europe, and even when in massive confrontations, he wrote obsessive letters to people on seemingly trivial topics, detailing their marriages and affairs. Even when opposed by with such great forces, he did not shut down, he did not seek escapism, except perhaps for brief periods with his mistresses.

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Cover of the book 'The Signal and the Noise' by Nate Silver. Published by The Penguin Press

The Signal and the Noise (book/audiobook review, summary and notes)

During an odd phase of fascination with American politics (I’m Canadian), I stumbled across Nate Silver’s website’s ( political coverage of the 2016 Idaho primary. Their cold, analytical coverage of the election appealed to me. It turns out Nate also wrote a book about prediction, which luckily for me, is also in audiobook format.

The core idea
The takeaway from this book is essentially this: prediction is really hard and most people (and machines) suck at it (expect for weather forecasters). More concerningly (is that a word?), most people don’t even know that they suck at it. Oh, and you should use Bayesian statistics to give probabilistic estimates and update your probabilities when you get new information.

This book encouraged me to take a hard look at my own predictions. Do I suck at it? Do I actually understand Bayes theorem?

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What is the derivative of ReLU?

gae python – importerror: no module named webapp2

– Google app engine with Python on Ubuntu
– Running the local development server.

If you start getting this error:

"importerror: no module named webapp2"

after running,
$ helloworld/

You might want to go back to an earlier version for local development.

After downloading “

I got errors with import webapp2

Luckily, I had a previous version “”

So I deleted 1.9.37. And used 1.9.34 instead.

Maybe this will work for you.

Prague travel pictures and deep dreaming

Here is a combined short summary on my travels to the city of Prague in the Czech Republic along with corresponding images created using Google’s DeepDreams.

What is this DeepDreams you speak of?

Basically, DeepDream is a deep neural network that was trained to recognize objects from millions of images. A deep neural network is composed of a stack of layers. Basically, these layers learn image filters that when applied to an image classify the image (e.g., is this an image of a cat or a dog?).

You give DeepDream an image and specify a layer in the neural network. The original image is then slightly perturbed to create a modified image that causes the specified layer in the neural network to be more activated.

Early layers in the neural network are sensitive to low level concepts like the edges and textures in the image. So if you specify an early layer, your image will be modified to have edges and textures that most activate the early selected layer.

Example CNN. Image goes as input. Early layers (purple) are sensitive to things like edges and textures. Later layers (red) are sensitive to higher level concepts like faces.

Example CNN. Image goes as input. Early layers (purple) are sensitive to things like edges and textures. Later layers (red) are sensitive to higher level concepts like faces.

Later (or deeper) layers in the neural network are activated when they see higher level concepts such as faces. So any areas in the original image that slightly look like a face, will be modified to look more like a face.

Okay, but now you might ask, but what about Prague? How was your trip? Did you like the city?

Yeah it was nice! Thanks for asking. Did you want to see some pictures? Here’s one of an old building.

Original image of some building in Prague... can't remember what/if the significance of this picture was.

Original image of some building in Prague… can’t remember what the significance of this picture was.

Let’s try some deep dreaming on this. We’ll use the neural network known as VGG16 (it’s a famous neural network that performs very well on competitions). We’ll start by telling VGG16 (the neural network) to modify this image so that one of it’s middle layers becomes more activated. Specifically, we will activate layer conv3_1 from VGG16 (if you don’t know what conv3_1 means, that’s okay – it’s just a technical detail specifying what layer to use). This gives us this:

Same Prague building but using DeepDreams with VGG16 conv3_1

Same Prague building but using DeepDreams with VGG16 conv3_1

Now if we activate a deeper layer, conv5_2, we get this crazy looking image,

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How to run an IPython/Jupyter Notebook on a remote machine

Here’s how to run an IPython Notebook (now called a Jupyter Notebook) on a remote linux machine without using VNC.

These instructions are expanded on from here,
and it’s worth reading through to get more details.

Let’s assume,
you have two machines:
local_machine that you are physically working on
remote_machine that you want to run code on.

And you want to work in the browser on your local_machine, but have the code execute on the remote_machine.

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Deep features to classify skin lesions – summary and slides

Here I'm nervously just starting our talk on our approach to skin lesion classification

Me nervously just starting to talk about our approach to skin lesion classification.

We presented our work, “Deep Features to Classify Skin Lesions” at ISBI 2016 in Prague! And I’m happy to report that our work was awarded runner-up for the Best Student Paper Award :)

In this work, we looked at how to classify skin lesions from images captured with a digital camera (i.e., non-dermoscopy). Our approach was able to distinguish among 10 different types of skin diseases over 1300 images and achieved an accuracy higher than what was previously reported over the same dataset. We did this by applying deep learning (i.e., pretrained convolutional neural networks) to melanoma and non-melanoma skin images.

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