Slides stolen gracefully from Ben Zittlau
Slide content under CC-BY-SA 4.0 and MIT License for source code. Slide Source code is MIT License as well.
First off lets get a useful Python environment!
Please install theanets and bpython.
pip install --user keras
pip install --user tensorflow
pip install --user scikit-learn
pip install --user juypterlab
Get a recent version of this presentation:
git clone https://github.com/abramhindle/keras-deep-learning-tutorial.git
Then run the notebook.
jupyter lab "./Keras Deep Learning Tutorial.ipynb"
See it here:
Building a function from data to classify, predict, group, or represent data.
There are a few kinds of tasks or functions that could help us here.
- Classification: given some input, predict the class that it belongs to. Given a point is it in the red or in the blue?
- Regression: Given a point what will its value be? In the case of a function with a continuous or numerous discrete outputs it might be appropriate.
- Representation: Learn a smaller representation of the input data. E.g. we have 300 features lets describe them in a 128-bit hash.
Imagine we have this data:
See src/genslice.py to see how we made it.
def in_circle(x,y,cx,cy,radius):
return (x - float(cx)) ** 2 + (y - float(cy)) ** 2 < radius**2
def mysolution(pt,outer=0.3):
return in_circle(pt[0],pt[1],0.5,0.5,outer) and not in_circle(pt[0],pt[1],0.5,0.5,0.1)>>> myclasses = np.apply_along_axis(mysolution,1,test[0])
>>> print "My classifier!"
My classifier!
>>> print "%s / %s " % (sum(myclasses == test[1]),len(test[1]))
181 / 200
>>> print theautil.classifications(myclasses,test[1])
[('tp', 91), ('tn', 90), ('fp', 19), ('fn', 0)]
1-NN: 1 Nearest Neighbor.
Given the data, we produce a function that outputs the CLASS of the nearest neighbour to the input data.
Whoever is closer, is the class. 3-NN is 3-nearest neighbors whereby we use voting of the 3 neighbors instead.
def euclid(pt1,pt2):
return sum([ (pt1[i] - pt2[i])**2 for i in range(0,len(pt1)) ])
def oneNN(data,labels):
def func(input):
distance = None
label = None
for i in range(0,len(data)):
d = euclid(input,data[i])
if distance == None or d < distance:
distance = d
label = labels[i]
return label
return func>>> learner = oneNN(train[0],train[1])
>>>
>>> oneclasses = np.apply_along_axis(learner,1,test[0])
>>> print "1-NN classifier!"
1-NN classifier!
>>> print "%s / %s " % (sum(oneclasses == test[1]),len(test[1]))
198 / 200
>>> print theautil.classifications(oneclasses,test[1])
[('tp', 91), ('tn', 107), ('fp', 2), ('fn', 0)]1-NN has great performance in this example, but it uses Euclidean distance and the dataset is really quite biased to the positive classes.
Thus we showed a simple learner that classifies data.
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That's really interesting performance and it worked but will it scale and continue to work?
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1-NN doesn't work for all problems. And it is dependent on linear relationships.
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What if our problem is non-linear?
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Neural networks are popular
- Creating AI for Go
- Labeling Images with cats and dogs
- Speech Recognition
- Text summarization
- Guitar Transcription
- Learn audio from video12
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Neural networks can not only classify, but they can create content, they can have complicated outputs.
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Neural networks are generative!
Neural networks or "Artificial Neural Networks" are a flexible class of non-linear machine learners. They have been found to be quite effective as of late.
Neural networks are composed of neurons. These neurons try to emulate biological neurons in the most metaphorical of senses. Given a set of inputs they produce an output.
Neurons have functions.
- Rectified Linear Units have been shown to train quite well and achieve good results. By they aren't easier to differentiate. f(x) = max(0,x)
- Sigmoid functions are slow and were the classical neural network neuron, but have fallen out of favour. They will work when nothing else will. f(x) = 1/(1 + e^-x)
- Softplus is a RELU that is slower to compute but differentiable. f(x) = ln(1 + e^x)
The inputs to a neural network? The outputs of connected nodes times their weight + a bias.
neuron(inputs) = neuron_f( sum(weights * inputs) + bias )
Single hidden layer neural network.
There's nothing particularly crazy about deep learning other than it has more hidden layers.
These hidden layers allow it to compute state and address the intricacies of complex functions. But each hidden layer adds a lot of search space.
How do we find the different weights?
Well we need to search a large space. A 2x3x2 network will have 232 weights + 5 biases (3 hidden, 2 output) resulting in 17 parameters. That's already a large search space.
Most search algorithms measure their error at a certain point (difference between prediction and actual) and then choose a direction in their search space to travel. They do this by sampling points around themselves in order to compute a gradient or slope and then follow the slope around.
Here's a 3D demo of different search algorithms.
Please open slice-classifier and a python interpreter such as bpython. Search for Part 3 around line 100.
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Scaling inputs: Scaling can sometimes help, so can standardization. This means constraining values or re-centering them. It depends on your problem and it is worth trying.
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E.g. min max scaling:
def min_max_scale(data):
'''scales data by minimum and maximum values between 0 and 1'''
dmin = np.min(data)
return (data - dmin)/(np.max(data) - dmin)-
posing.py tries to show the problem of taking random input data and determine what distribution it comes from. That is what function can produce these random values.
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Let's open up posing.py and get an interpreter going.
- Given 1 single sample what distribution does it come from?
- Given 40 samples what distribution does it come from?
- Given 40 sorted samples what distribution does it come from?
- Given 40 histogrammed samples what distribution does it come from?
- For discrete values consider discrete inputs neurons. E.g. if you have 3 letters are your input you should have 3 * 26 input numerous. Each neuron is "one-hot" -- 1 neuron is set to 1 to indicate that 1 discerete value. An input of AAA would be: 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
- ZZZ would be 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
- For groups of elements consider representing them as their counts.
- E.g. 3 cats, 4 dogs, 1 car as: 3 4 1 on 3 input neurons.
- Neural networks work well with distributions as inputs and distributions as outputs
- Words can be represented as word counts where by your vector is the count of each word per document -- you might have a large vocabulary so watch out!
- n-grams are popular too with one-hot encoding
- Each neuron can represent a pixel represented from 0 to 1
- You can have images as output too!
- Do not ask the neural network to distingush discrete values on 1 neuron. Don't expect 1 neuron to output 0.25 for A and 0.9 for B and 1.0 for C. Use 3 neurons!
- Distribution outputs are good
- Interpretting the output is fine for regression problems
- Theanets Documentation
- A Practical Guide to TrainingRestricted Boltzmann Machines
- MLP
- Deep Learning Tutorials
- Deep Learning Tutorials
- Coursera: Hinton's Neural Networks for Machine Learning
- The Next Generation of Neural Networks
- Geoffrey Hinton: "Introduction to Deep Learning & Deep Belief Nets"
- Bengio's Deep Learning (1)(2)
- Nvidia's Deep Learning tutorials
- Udacity Deep Learning MOOC