Posts tagged with: machine learning
Apr 11, 2015    |      0 comments

Gradient Boosted Trees Notes

Gradient Boosted Trees (GBT) is an ensemble mechanism which learns incrementally new trees optimizing the present ensemble’s residual error.  This residual error is resemblance to a gradient step of a linear model. A GBT tries to estimate gradient steps by a new tree and update the present ensemble with this new tree so that whole model is updated in the optimizing direction. This is not very formal explanation but it gives my intuition.

One formal way to think about GBT is, there are all possible tree constructions and our algorithms is just selects the useful ones for the given data.  Hence, compared to all possible trees,  number of tress constructed in the model is very small. This is similar to constructing all these infinite  number of trees and averaging them with the weights estimated by  LASSO.

GBT includes different hyper parameters mostly for regularization.

  • Early Stopping : How many rounds your GBT continue.
  • Shrinkage : Limit the update of each tree with the coefficient

        \[0 < alpha < 1\]

  • Data subsampling: Do not use whole the data for each tree, instead sample instances. In general sample ration

        \[n = 0.5\]

    but it can be lower for larger datasets.

  • One side note: Subsampling without shrinkage performs poorly.

Then my initial setting is:

  • Run pretty long with many many round observing a validation data loss.
  • Use small shrinkage value

        \[alpha = 0.001\]

  • Sample 0.5 of the data
  • Sample 0.9 of the features as well or do the reverse.
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Mar 18, 2015    |      0 comments

Kaggle Plankton Challenge Winner's Approach

I recently attended Plankton Classification Challenge  on Kaggle. Even tough I used simpler (stupidly simpler compared to the winner) Deep NN model for my submissions and ended up at 192th position among  1046 participants. However, this was very good experiment area for me to test new comer ideas to Deep Learning community  and try some couple of novel things which I plan to explain later in my blog.

In this post, I share my notes about the winner’s approach (which is explained here extensively).

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Mar 13, 2015    |       2 comments

Recent Advances in Deep Learning

In this text, I would like to talk about some of the recent advances of Deep Learning models by no means complete. (Click heading for the reference)

  1. Parametric Rectifier Linear Unit (PReLU)
    • The idea is to allow negative activation in well-known ReLU units by controlling it with a learnable parameter. In other words, you learn how much negative activationsyou need for each unit to discriminate classes. In the work, it is proposed that PReLU unit is very useful for especially very deep models that lacks for gradient propagation to initial layers due to its depth. What is different is PReLU allows more gradient return by allowing negative activation.PReLU
  2. A new initialization method (MSRA for Caffe users)
    • Xavier initialization was proposed by Bengio’s team and it considers number of fan-in and fan-out to a certain unit to define the initial weights.  However, the work says that Xavier method and its alternations considers linear activation functions for the formulation of the method. Hence, they propose some changes related to ReLU activation that they empirically proved its effect in practice with better convergence rate.
  3. Batch Normalization 
    • This work serves data normalization as a structural part of the model. They say that the distribution of the training data changes as the model evolves and it priorities the initialization scheme and the learning schedule we use for the learning. Each mini-batch of the data is normalized with the described scheme just before its propagation through the network and it allows faster convergence  with larger learning rates and robust models to initialization scheme that we choose.  Each mini-batch is normalized by its mean and variance, then it is scaled and shifted by a learned coefficient and residual.

      From the paper

      From the paper

  4. Inception Layers
    • This is one of the ingredients of last year’s ImageNet winner GoogleNet. The trick is to use multi-scale filters all together in a layer and concatenating their responses for the next layer. In that way we are able to learn difference covariances per each layer by different sizes and structures. inception_module
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Feb 13, 2015    |       12 comments

Comparison: SGD vs Momentum vs RMSprop vs Momentum+RMSprop vs AdaGrad

In this post I’ll briefly introduce some update tricks for training of your ML model. Then, I will present my empirical findings with a linked NOTEBOOK that uses 2 layer Neural Network on CIFAR dataset.

I assume at least you know what is Stochastic Gradient Descent (SGD). If you don’t, you can follow this tutorial .  Beside, I’ll consider some improvements of SGD rule that result better performance and faster convergence.

SGD is basically a way of optimizing your model parameters based on the gradient information of your loss function (Means Square Error, Cross-Entropy Error … ). We can formulate this;

    \[w(t) = w(t-1) - epsilon * bigtriangleup w(t)\]

    \[w\]

is the model parameter,

    \[epsilon\]

is learning rate and

    \[bigtriangleup w(t)\]

is the gradient at the time

    \[t\]

.

SGD as itself  is solely depending on the given instance (or the batch of instances) of the present iteration. Therefore, it  tends to have unstable update steps per iteration and corollary convergence takes more time or even your model is akin to stuck into a poor local minima.

To solve this problem, we can use Momentum idea (Nesterov Momentum in literature). Intuitively, what momentum does is to keep the history of the previous update steps and combine this information with the next gradient step to keep the resulting updates stable and conforming the optimization history. It basically, prevents chaotic jumps.  We can formulate  Momentum technique as follows;

    \[v(t) = alpha v(t-1) - epsilon frac{partial E}{partial w}(t)\]

 (update velocity history with the new gradient)

    \[bigtriangleup w(t) = v(t)\]

(The weight change is equal to the current velocity)

    \[alpha\]

is the momentum coefficient and 0.9 is a value to start.

    \[frac{partial E}{partial w}(t)\]

is the derivative of

    \[w\]

wrt. the loss.

Okay we now soothe wild SGD updates with the moderation of Momentum lookup. But still nature of SGD proposes another potential problem. The idea behind SGD is to approximate the real update step by taking the average of the all given instances (or mini batches). Now think about a case where  a model parameter gets a gradient of +0.001 for each  instances then suddenly it gets -0.009 for a particular instance and this instance is possibly a outlier. Then it destroys all the gradient information before. The solution to such problem is suggested by G. Hinton in the Coursera course lecture 6 and this is an unpublished work even I believe it is worthy of.  This is called RMSprop. It keeps running average of its recent gradient magnitudes and divides the next gradient by this average so that loosely gradient values are normalized. RMSprop is performed as below;

    \[MeanSquare(w,t) =0.9 MeansSquare(w, t-1)+0.1frac{partial E}{partial w}(t)^2\]

    \[bigtriangleup w(t) = epsilonfrac{partial E}{partial w}(t) / (sqrt{MeanSquare(w,t)} + mu)\]

    \[mu\]

is a smoothing value for numerical convention.

You can also combine Momentum and RMSprop by applying successively and aggregating their update values.

Lets add AdaGrad before finish. AdaGrad is an Adaptive Gradient Method that implies different adaptive learning rates for each feature. Hence it is more intuitive for especially sparse problems and it is likely to find more discriminative features and filters for your Convolutional NN. Although you provide an initial learning rate, AdaGrad tunes it regarding the history of the gradients for each feature dimension. The formulation of AdaGrad is as below;

    \[w_i(t) = w_i(t-1) + frac{epsilon}{sum_{k=1}^{t}sqrt{{g_{ki}}^2}} * g_i\]

  where

    \[g_{i} = frac{partial E}{partial w_i}\]

So tihe upper formula states that, for each feature dimension, learning rate is divided by the all the squared root gradient history.

Now you completed my intro to the applied ideas in this NOTEBOOK and you can see the practical results of these applied ideas on CIFAR dataset. Of course this into does not mean complete by itself. If you need more refer to other resources. I really suggest the Coursera NN course by G. Hinton for RMSprop idea and this notes for AdaGrad.

For more information you can look this great lecture slide from Toronto Group.

Lately, I found this great visualization of optimization methods. I really suggest you to take a look at it.

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Feb 11, 2015    |       5 comments

Microsoft Research introduced a new NN model that beats Google and the others

MS researcher recently introduced a new deep ( indeed very deep 🙂 ) NN model (PReLU Net) [1] and they push the state of art in ImageNet 2012 dataset from 6.66% (GoogLeNet) to 4.94% top-5 error rate.

In this work, they introduce an alternation of well-known ReLU activation function. They call it PReLu (Parametric Rectifier Linear Unit). The idea behind is to allow negative activations on the ReLU function with a control parameter

    \[a\]

which is also learned over the training phase. Therefore, PReLU allows negative activations and in the paper they argue and emprically show that PReLU is better to resolve diminishing gradient problem for very deep neural networks  (> 13 layers) due to allowance of negative activations. That means more activations per layer, hence more gradient feedback at the backpropagation stage.

PReLU

all figures are from the paper

Continue Reading

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Dec 30, 2014    |      0 comments

Intro. to Contractive Auto-Encoders

Contractive Auto-Encoder is a variation of well-known Auto-Encoder algorithm that has a solid background in the information theory and lately deep learning community. The simple Auto-Encoder targets to compress information of the given data as keeping the reconstruction cost lower as much as possible. However another use is to enlarge the given input’s representation. In that case, you learn over-complete representation of the given data instead of compressing it. Most common implication is Sparse Auto-Encoder that learns over-complete representation but in a sparse (smart) manner. That means, for a given instance only informative set of units are activated, therefore you are able to capture more discriminative representation, especially if you use AE for pre-training of your deep neural network.

After this intro. what is special about Contraction Auto-Encoder (CAE)?  CAE simply targets to learn invariant representations to unimportant transformations for the given data. It only learns transformations that are exactly in the given dataset and try to avoid more. For instance, if you have set of car images and they have left and right view points in the dataset, then CAE is sensitive to those changes but it is insensitive to frontal view point. What it means that if you give a frontal car image to CAE after the training phase, it tries to contract its representation to one of the left or right view point car representation at the hidden layer. In that way you obtain some level of view point in-variance. (I know, this is not very good example for a cannier guy but I only try to give some intuition for CAE).

From the mathematical point of view, it gives the effect of contraction by adding an additional term to reconstruction cost. This addition is the Sqrt Frobenius norm of Jacobian of the hidden layer representation with respect to input values. If this value is zero, it means, as we change input values, we don’t observe any change on the learned hidden representations. If we get very large values then the learned representation is unstable as the input values change.

This was just a small intro to CAE, if you like the idea please follow the below videos of Hugo Larochelle’s lecture and Pascal Vincent’s talk at ICML 2011 for the paper.

 

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Dec 29, 2014    |      0 comments

Here is the G. Hinton’s talk at MIT about t inabilities of Convolutional Neural Networks and 4 basic arguments to solve these.

I just watched it with a slight distraction and I need to reiterate. However these are the basic arguments in which G. Hinton is proposed whilst the speech.

1.  CNN + Max Pooling is not the way of handling visual information as the human brain does. Yes, it works in practice for the current state of the art but, especially view point changes of the target objects are still unsolved.

2. Apply Equivariance instead of Invariance. Instead of learning invariant representations to the view point changes, learn changing representations correlated with the view point changes.

3. In the space of CNN weight matrices, view point changes are totally non-linear and therefore hard to learn. However, if we transfer instances into a space where the view point changes are globally linear, we can ease the problem. ( Use graphics representation uses explicit pose coordinates)

4. Route information to right set of neurons instead of unguided forward and backward passes. Define certain neuron groups ( called capsules ) that are receptive to  particular set of data clusters in the instance space and each of these capsules contributes to the whole model as much as the given instance’s membership to neuron’s cluster.

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Nov 19, 2014    |      0 comments

ML Work-Flow (Part 5) – Feature Preprocessing

We already discussed first four steps of ML work-flow. So far, we preprocessed crude data by DICTR (Discretization, Integration, Cleaning, Transformation, Reduction), then applied a way of feature extraction procedure to convert data into machine understandable representation, and finally divided data into different bunches like train and test sets . Now, it is time to preprocess feature values and make them ready for the state of art ML model ;).

We need Feature Preprocessing in order to:

  1. Evade scale differences between dimensions.
  2. Convey instances into a bounded region in the space.
  3. Remove correlations between different dimensions.

You may ask “Why are we so concerned about these?” Because

  1. Evading scale differences reduces unit differences between particular feature dimensions. Think about Age and Height of your customers. Age is scaled in years and Height is scaled in cm’s. Therefore, these two dimension values are distributed in different manners. We need to resolve this and convert data into a scale invariant representation before training your ML algorithm, especially if you are using one of the linear models like Logistic Regression or SVM (Tree based models are more robust to scale differences).
  2. Conveying instances into a bounded region in the space resolves the representation biases between instances. For instance, if you work on a document classification problem with bag of words representation then you should care about document length since longer documents include more words which result in more crowded feature histograms. One of the reasonable ways to solve this issue is to divide each word frequency by the total word frequency in the document so that we can convert each histogram value into a probability of seeing that word in the document. As a result, document is represented with a feature vector that is 1 in total of its elements. This new space is called vector space model in the literature.
  3. Removing correlations between dimensions cleans your data from redundant information exposed by multiple feature dimensions. Hence data is projected into a new space where each dimension explains something independently important from the other feature dimensions.

Okay, I hope now we are clear why we are concerned about these. Henceforth, I’ll try to emphasis some basic stuff in our toolkit for feature preprocessing.

Standardization

  • Can be applied to both feature dimensions or data instances.
  • If we apply to dimensions, it reduces unit effect and if we apply to instances then we solve instance biases as in the case of the document classification problem.
  • The result of standardization is that each feature dimension (instance) is scaled into defined mean and variance so that we fix the unit differences between dimensions.

  •     \[z = (x-mu)/alpha\]

     : for each dimension (instance),  subtract the mean and divide by the variance of that dimension (instance) so that each dimension is kept inside a mean = 0 , variance = 1 curve.

Min Max Scaling

  • Personally, I’ve not applied Min-Max Scaling to instances,
  • It is still useful for unit difference problem.
  • Instead of distributional consideration, it hinges the values in the range  [0,1].

  •     \[x_{norm} = (x - x_{min})/(x_{max} - x_{min})\]

    :  Find max and min values of the feature dimension and apply the formula.

Caveat 1: One common problem of Scaling and Standardization is you need to keep min and max for Scaling, mean and variance values for Standardization for the novel data and the test time. We estimate these values from only the training data and assume that these are still valid for the test and real world data. This assumption might be true for small problems but especially for online environment this caveat should be dealt with a great importance.

Sigmoid Functions

  • Sigmoid function naturally fetches given values into a [0, 1] range
  • Does not need any assumption about the data like mean and variance
  • It penalizes large values  more than the small ones.
  • You can use other activation functions like tanh.

Sigmoid function

Caveat 2: How to choose and what to choose are very problem dependent questions. However, if you have a clustering problem then standardization seems more reasonable for better similarity measure between instance and if you intend to use Neural Networks then some particular kind of NN demands [0,1] scaled data (or even more interesting scale ranges for better gradient propagation on the NN model). Also, I personally use sigmoid function for simple problems in order to get fast result by SVM without complex investigation.

Zero Phase Component Analysis (ZCA Whitening)

  • As I explained before, whitening is a process to reduce redundant information by decorrelating data with a final diagonal correlation matrix with preferable all diagonals are one.
  • It has especially very important implications in Image Recognition and Feature Learning  so as to make visual cues more concrete on images.
  • Instead of formula, it is more intuitive to wire some code

Covariance Matrices before and after ZCA

I tried to touch some methods and common concerns of feature preprocessing, by no means  complete. Nevertheless, a couple of takeaways from this post are; do not ignore normalizing your feature values before going into training phase and choose the correct method by investigating the values painstakingly.

PS: I actually promised to write a post per week but I am as busy as a bee right now and I barely find some time to write a new stuff. Sorry about it 🙁

 

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