Machine learning is like highschool sex. Everyone says they do it, nobody really does, and no one knows what it actually is. -- @Mikettownsend.

Neural Redis is a Redis loadable module that implements feed forward neural networks as a native data type for Redis. The project goal is to provide Redis users with an extremely simple to use machine learning experience.

Normally machine learning is operated by collecting data, training some system, and finally executing the resulting program in order to solve actual problems. In Neural Redis all this phases are compressed into a single API: the data collection and training all happen inside the Redis server. Neural networks can be executed while there is an ongoing training, and can be re-trained multiple times as new data from the outside is collected (for instance user events).

The project starts from the observation that, while complex problems like computer vision need slow to train and complex neural networks setups, many regression and classification problems that are able to enhance the user experience in many applications, are approachable by feed forward fully connected small networks, that are very fast to train, very generic, and robust against non optimal parameters configurations.

Neural Redis implements:

• A very simple to use API.
• Automatic data normalization.
• Online training of neural networks in different threads.
• Ability to use the neural network while the system is training it (we train a copy and only later merge the weights).
• Fully connected neural networks using the RPROP (Resilient back propagation) learning algorithm.
• Automatic training with simple overtraining detection.

The goal is to help developers, especially of mobile and web applications, to have a simple access to machine learning, in order to answer questions like:

• What promotion could work most likely with this user?
• What AD should I display to obtain the best conversion?
• What template is the user likely to appreciate?
• What is a likely future trend for this data points?

Of course you can do more, since neural networks are pretty flexible. You can even have fun with computer visions datasets like MINST, however keep in mind that the neural networks implemented in Neural Redis are not optimized for complex computer visions tasks like convolutional networks (it will score 2.3%, very far from the state of art!), nor Neural Redis implements the wonders of recurrent neural networks.

However you'll be surpirsed by the number of tasks in which a simple neural network that can be trained in minutes, will be able to discover linear ad non linear correlations.

To run this extension you need Redis unstable, grab it from Github, it is the default branch. Then compile the extension, and load it starting Redis with:

redis-server --loadmodule /path/to/neuralredis.so

Alternatively add the following in your redis.conf file:

loadmodule /path/to/neuralredis.so

WARNING: this is alpha code. It is likely to contain bugs and may easily crash the Redis server. Also note that currently only RDB persistence is implemented in the module, while AOF rewrite is not implemented at all. Use at your own risk.

If you are not still scared enough, please consider that I wrote the more than 1000 lines of C code composing this extension, and this README file, in roughly two days.

Note that this implementation may be hugely improved. For instance currently only the sigmoid activaction function and the root mean square loss functions are supported: while for the problems this module is willing to address this limited neural network implementation is showing to be quite flexible, it is possible to do much better depending on the problem at hand.

In order to understand how the API works, here is an hello world example where we'll teach our neural network to do... additions :-)

To create a new neural network we use the following command:

> NR.CREATE net REGRESSOR 2 3 -> 1 NORMALIZE DATASET 50 TEST 10
(integer) 13

The command creates a neural network, configured for regression tasks (as opposed to classification: well'll explain what this means in the course of this tutorial).

Note that the command replied with "13". It means that the network has a total of 13 tunable parameters, considering all the weights that go from units or biases to other units. Larger networks will have a lot more parameters.

The neural network has 2 inputs, 3 hidden layers, and a single output. Regression means that given certain inputs and desired outputs, we want the neural network to be able to understand the function that given the inputs computes the outputs, and compute this function when new inputs are presented to it.

The NORMALIZE option means that it is up to Redis to normalize the data it receives, so there is no need to provide data in the -/+ 1 range. The options DATASET 50 and TEST 10 means that we want an internal memory for the dataset of 50 and 10 items respectively for the training dataset, and the testing dataset.

The learning happens using the training dataset, while the testing dataset is used in order to detect if the network is able to generalize, that is, is really able to understand how to approximate a given function. At the same time, the testing dataset is useful to avoid to train the network too much, a problem known as overfitting. Overfitting means that the network becomes too much specific, at the point to be only capable of replying correctly to the inputs and outputs it was presented with.

Now it is time to provide the network with some data, so that it can learn the function we want to approximate:

> NR.OBSERVE net 1 2 -> 3
1) (integer) 1
2) (integer) 0

We are saying: given the inputs 1 and 2, the output is 3. The reply to the NR.OBSERVE command is the number of data items stored in the neural network memory, respectively in the training and testing data sets.

We continue like that with other examples:

> NR.OBSERVE net 4 5 -> 9
> NR.OBSERVE net 3 4 -> 7
> NR.OBSERVE net 1 1 -> 2
> NR.OBSERVE net 2 2 -> 4
> NR.OBSERVE net 0 9 -> 9
> NR.OBSERVE net 7 5 -> 12
> NR.OBSERVE net 3 1 -> 4
> NR.OBSERVE net 5 6 -> 11

At this point we need to train the neural network, so that it can learn:

> NR.TRAIN net AUTOSTOP

The NR.TRAIN command starts a training thread. the AUTOSTOP option means that we want the training to stop before overfitting starts to happen.

Using the NR.INFO command you can see if the network is still training. However in this specific case, the network will take a few milliseconds to train, so we can immediately try if it actually learned how to add two numbers:

> NR.RUN net 1 1
1) "2.0776522297040843"

> NR.RUN net 3 2
1) "5.1765427204933099"

Well, more or less it works. Let's look at some internal info now:

> NR.INFO net
 1) id
 2) (integer) 1
 3) type
 4) regressor
 5) auto-normalization
 6) (integer) 1
 7) training
 8) (integer) 0
 9) layout
10) 1) (integer) 2
    2) (integer) 3
    3) (integer) 1
11) training-dataset-maxlen
12) (integer) 50
13) training-dataset-len
14) (integer) 6
15) test-dataset-maxlen
16) (integer) 10
17) test-dataset-len
18) (integer) 2
19) training-total-steps
20) (integer) 1344
21) training-total-seconds
22) 0.00
23) dataset-error
24) "7.5369825612397299e-05"
25) test-error
26) "0.00042670663615723583"
27) classification-errors-perc
28) 0.00

As you can see we have 6 dataset items and 2 test items. We configured the network at creation time to have space for 50 and 10 items. As you add items with NR.OBSERVE the network will put items evenly on both datasets, proportionally to their respective size. Finally when the datasets are full, old random entries are replaced with new ones.

We can also see that the network was trained with 1344 steps for 0 seconds (just a few milliseconds). Each step is the training performed with a single data item, so the same 6 items were presented to the network for 244 cycles in total.

If we try to use our network with values outside the range it learned with, we'll see it failing:

> NR.RUN net 10 10
1) "12.855978185382257"

This happens because the automatic normalization will consider the maximum values seen in the training dataset. So if you plan to use auto normalization, make sure to show the network samples with different values, including inputs at the maximum of the data you'll want to use the network with in the future.

Regression approximates a function having certain inputs and outputs in the training data set. Classification instead is the task of, given a set of inputs representing something, to label it with one of a fixed set of labels.

For example the inputs may be features of Greek jars, and the classification output could be one of the following three jar types:

• Type 0: Kylix type A
• Type 1: Kylix type B
• Type 2: Kassel cup

As a programmer you may think that, the output class, is just a single output number. However neural networks don't work well this way, for example classifying type 0 with an output between 0 and 0.33, type 1 with an output between 0.33 and 0.66, and finally type 2 with an output between 0.66 and 1, will not work well at all.

The way to go instead is to use three distinct outputs, where we set two always to 0, and a single one to 1, corresponding to the type the output represents, so:

• Type 0: [1, 0, 0]
• Type 1: [0, 1, 0]
• Type 2: [0, 0, 1]

When you create a neural network with the NR.CREATE command, and use as second argument CLASSIFIER instead of REGRESSOR, Neural Redis will do the above transformation for you, so when you train your network with NR.OBSERVE you'll just use, as output, as single number: 0, 1 or 2.

Of course, you need to create the network with three outputs like that:

> NR.CREATE mynet CLASSIFIER 5 10 -> 3
(integer) 93

Our network is currently untrained, but it can already be run, even if the replies it will provide are totally random:

> NR.RUN mynet 0 1 1 0 1
1) "0.50930603602918945"
2) "0.48879876200255651"
3) "0.49534453421381375"

As you can see, the network voted for type 0, since the first output is greater than the others. There is a Neural Redis command that saves you the work of finding the greatest output client side in order to interpret the result as a number between 0 and 2. It is identical to NR.RUN but just outputs directly the class ID, and is called NR.CLASS:

> NR.CLASS mynet 0 1 1 0 1
(integer) 0

However note that ofter NR.RUN is useful for classification problems. For example a blogging platform may want to train a neural network to predict the template that will appeal more to the user, based on the registration data we just obtained, that include the country, sex, age and category of the blog.

While the prediction of the network will be the output with the highest value, if we want to present different templates, it makes sense to present, in the listing, as the second one the one with the second highest output value and so forth.

Before diving into a practical classification example, there is a last thing to say. Networks of type CLASSIFIER are also trained in a different way: instead of giving as output a list of zeros and ones you directly provide to NR.OBSERVE the class ID as a number, so in the example of the jars, we don't need to write NR.OBSERVE 1 0.4 .2 0 1 -> 0 0 1 to specify as output of the provided data sample the third class, but we should just write:

> NR.OBSERVE mynet 1 0.4 .2 0 1 -> 2

The "2" will be translated into "0 0 1" automatically, as "1" would be translated to "0 1 0" and so forth.

Kaggle.com is hosting a machine learning competition. One of the datasets they use, is the list of the Titanic passengers, their ticket class, fair, number of relatives, age, sex, and other information, and... If they survived or not during the Titanic incident.

You can find both the code and a CSV with a reduced dataset of 891 entries in the examples directory of this Github repository.

In this example we are going to try to predict, given a few input variables, if a specific person is going to survive or not, so this is a classification task, where we label persons with two different labels: survived or died.

This problem is pretty similar, even if a bit more scaring, than the problem of labeling users or predicting their response in some web application according to their behavior and the other data we collected in the past (hint: machine learning is all about collecting data...).

In the CSV there are a number of information about each passenger, but here in order to make the example simpler we'll use just the following fields:

• Ticket class (1st, 2nd, 3rd).
• Sex.
• Age.
• Sibsp (Number of siblings, spouses aboard).
• Parch (Number of parents and children aboard).
• Ticket fare.

If there is a correlation between this input variables and the ability to survive, our neural network should find it.

Note that while we have six inputs, we'll need a total network with 9 total inputs, since sex and ticket class, are actually input classes, so like we did in the output, we'll need to do in the input. Each input will signal if the passenger is in one of the possible classes. This are our nine inputs:

• Is male? (0 or 1).
• Is Female? (0 or 1).
• Traveled in first class? (0 or 1).
• Traveled in second class? (0 or 1).
• Traveled in third class? (0 or 1).
• Age.
• Number of siblings / spouses.
• Number of parents / children.
• Ticket fare.

We have a bit less than 900 passengers (I'm using a reduced dataset here), however we want to take about 200 for verification at application side, without sending them to Redis at all.

The neural network will also use part of the dataset for verification, since here I'm planning to use the automatic training stop feature, in order to detect overfitting.

Such a network can be created with:

> NR.CREATE mynet CLASSIFIER 9 15 -> 2 DATASET 1000 TEST 500 NORMALIZE

Also note that we are using a neural network with a single hidden layer (the layers between inputs and outputs are called hidden, in case you are new to neural networks). The hidden layer has 15 units. This is still a pretty small network, but we expect that for the amount of data and the kind of correlations that there could be in this data, this could be enough. It's possible to test with different parameters, and I plan to implement a NR.CONFIGURE command so that it will be possible to change this things on the fly.

Also note that since we defined a testing dataset maximum size to be half the one of the training dataset (1000 vs 500), NR.OBSERVE will automatically put one third of the entires in the testing dataset.

If you check the Ruby program that implements this example inside the source distribution, you'll see how data is fed directly as it is to the network, since we asked for auto normalization:

def feed_data(r,dataset,mode)
    errors = 0
    dataset.each{|d|
        pclass = [0,0,0]
        pclass[d[:pclass]-1] = 1
        inputs = pclass +
                 [d[:male],d[:female]] +
                 [d[:age],
                  d[:sibsp],
                  d[:parch],
                  d[:fare]]
        outputs = d[:survived]
        if mode == :observe
            r.send('nr.observe',:mynet,*inputs,'->',outputs)
        elsif mode == :test
            res = r.send('nr.class',:mynet,*inputs)
            if res != outputs
                errors += 1
            end
        end
    }
    if mode == :test
        puts "#{errors} prediction errors on #{dataset.length} items"
    end
end

The function is able to both send data or evaluate the error rate.

After we load 601 entries from the dataset, before any training, the output of NR.INFO will look like this:

> NR.INFO mynet
 1) id
 2) (integer) 5
 3) type
 4) classifier
 5) auto-normalization
 6) (integer) 1
 7) training
 8) (integer) 0
 9) layout
10) 1) (integer) 9
    2) (integer) 15
    3) (integer) 2
11) training-dataset-maxlen
12) (integer) 1000
13) training-dataset-len
14) (integer) 401
15) test-dataset-maxlen
16) (integer) 500
17) test-dataset-len
18) (integer) 200
19) training-total-steps
20) (integer) 0
21) training-total-seconds
22) 0.00
23) dataset-error
24) "0"
25) test-error
26) "0"
27) classification-errors-perc
28) 0.00
29) overfitting-detected
30) no

As expected, we have 401 training items and 200 testing dataset. Note that for networks declared as classifiers, we have an additional field in the info output, which is classification-errors-perc. Once we train the network this field will be populated with the percentage (from 0% to 100%) of items in the testing dataset which were misclassified by the neural network. It's time to train our network:

> NR.TRAIN mynet AUTOSTOP
Training has started

If we check the NR.INFO output after the training, we'll discover a few interesting things (only quoting the relevant part of the output):

19) training-total-steps
20) (integer) 64160
21) training-total-seconds
22) 0.29
23) dataset-error
24) "0.1264141065389438"
25) test-error
26) "0.13803731074639586"
27) classification-errors-perc
28) 19.00
29) overfitting-detected
30) yes

The network was trained for 0.29 seconds. At the end of the training, that was stopped for overfitting, the error rate in the testing dataset was 19%.

You can also specify to train for a given amonut of seconds or cycles. For now we just use the AUTOSTOP feature since it is simpler. However we'll dig into more details in the next section.

We can now show the output of the Ruby program after its full execution:

47 prediction errors on 290 items

Does not look too bad, considering how simple is our model and the fact we trained with just 401 data points. Modeling just on the percentage of people that survived VS the ones that died, we could miss-predict more than 100 passengers.

We can also play with a few variables interactively in order to check what are the inputs that make a difference according to our trained neural network.

Let's start asking the probable outcome for a woman, 30 years old, first class, without siblings and parents:

> NR.RUN mynet 1 0 0 0 1 30 0 0 200
1) "0.093071778873849084"
2) "0.90242156736283008"

The network is positive she survived, with 90% of probabilities. What if she is a lot older than 30 years old, let's say 70?

> NR.RUN mynet 1 0 0 0 1 70 0 0 200
1) "0.11650946245068818"
2) "0.88784839170875851"

This lowers her probability to 88.7%. And if she traveled in third class with a very cheap ticket?

> NR.RUN mynet 0 0 1 0 1 70 0 0 20
1) "0.53693405013043127"
2) "0.51547605838387811"

This time is 50% and 50%... Throw your coin.

The gist of this example is that, many problems you face as a developer in order to optimize your application and do better choices in the interaction with your users, are Titanic problems, but not in their size, just in the fact that a simple model can "solve" them.

One thing that makes neural networks hard to use in an interactive way like the one they are proposed in this Redis module, is for sure overfitting. If you train too much, the neural network ends to be like that one student that can exactly tell you all the words in the lesson, but if you ask a more generic question about the argument she or he just wonders and can't reply.

So the NR.TRAIN command AUTOSTOP option attempts to detect overfitting to stop the training before it's too late. How is this performed? Well the current solution is pretty trivial: as the training happens, we check the current error of the neural network between the training dataset and the testing dataset.

When overfitting kicks in, usually what we see is that the network error rate starts to be lower and lower in the training dataset, but instead of also reducing in the testing dataset it inverts the tendency and starts to grow. To detect this turning point is not simple for two reasons:

1. The error may fluctuates as the network learns.
2. The network error may just go higher in the testing dataset since the learning is trapped into a local minima, but then a better solution may be found.

So while AUTOSTOP kinda does what it advertises (but I'll work on improving it in the future, and there are neural network experts that know much better than me and can submit a kind Pull Request :-), there are also means to manually train the network, and see how the error changes with training.

For instance, this is the error rate in the Titanic dataset after the automatic stop:

21) training-total-seconds
22) 0.17
23) dataset-error
24) "0.13170509045457734"
25) test-error
26) "0.13433443241900492"
27) classification-errors-perc
28) 18.50

We can use the MAXTIME and MAXCYCLES options in order to train for a specific amount of time (note that these options are also applicable when AUTOSTOP is specified). Normally MAXTIME is set to 10000, which are milliseconds, so to 10 seconds of total training before killing the training thread. Let's train our network for 30 seconds, without auto stop.

> NR.TRAIN mynet MAXTIME 30000
Training has started

As a side note, while one or more trainings are in progress, we can list them:

> NR.THREADS
1) nn_id=9 key=mynet db=0 maxtime=30000 maxcycles=0

After the training stops, let's show info again:

21) training-total-seconds
22) 30.17
23) dataset-error
24) "0.0674554189303056"
25) test-error
26) "0.20468644603795394"
27) classification-errors-perc
28) 21.50

You can see that our network overtrained: the error rate of the training dataset is now lower: 0.06. But actually the performances in data it never saw before, that is the testing dataset, is greater at 0.20!

And indeed, it classifies in a wrong way 21% of entries instead of 18.50%.

However it's not always like that, so to test things manually is a good idea when working at machine learning experiments, especially with this module that is experimental.

An interesting example is the iris.rb program inside the examples directory: it will load the Iris.csv dataset into Redis, which is a very popular dataset with three variants of Iris flowers with their sepal and petal features. If you run the program, the percentage of entries classified in a wrong way will be 4%, however if you train the network a few more cycles with:

NR.TRAIN iris MAXCYCLES 100

You'll see that often the error will drop to 2%.

When using AUTOSTOP, there is an additional option that can be specified (it has no effects alone), that is: BACKTRACK. When backtracking is enabled, while the network is trained, every time there is some hint that the network may start to overfit, the current version of the network is saved. At the end of the training, if the saved network is better (has a smaller error) compared to the current one, it is used instead of the final version of the trained network.

This avoids certain pathological runs when AUTOSTOP is used but overfitting is not detected. However, it adds running time since we need to clone the NN from time to time during the training.

For example using BACKTRACK with the Iris dataset (see the iris.rb file inside the examples directory) it never overtrains, while without about 2% of the runs may overtrain.

The Titanic example is surely more interesting, however it is possible that most relations between inputs and outputs are linear, so we'll now try a non linear classification task, just for the sake of showing the capabilities of a small neural network.

In the examples directory of this source distribution there is an example called circles.rb, we'll use it as a reference.

We'll just setup a classification problem where the neural network will be asked to classify two inputs, which are from our point of view two coordinates in a 2D space, into three different classes: 0, 1 and 2.

While the neural network does not know this, we'll generate the data so that different classes actually map to three different circles in the 2D space: the circles also contain intersections. The function that generates the dataset is the following:

    point_class = rand(3) # Class will be 0, 1 or 2
    if point_class == 0
        x = Math.sin(k)/2+rand()/10;
        y = Math.cos(k)/2+rand()/10;
    elsif point_class == 1
        x = Math.sin(k)/3+0.4+rand()/8;
        y = Math.cos(k)/4+0.4+rand()/6;
    else
        x = Math.sin(k)/3-0.5+rand()/30;
        y = Math.cos(k)/3+rand()/40;
    end

The basic trigonometric function:

x = Math.sin(k)
y = Math.cos(k)

With k going from 0 to 2*PI, is just a circle, so the above functions are just circles, plus the rand() calls in order to introduce noise. Basically if I trace the above three classes of points in a graphical way with load81, I obtain the following image:

The program circles.rb, it will generate the same set of points and will feed them into the neural network configured to accept 2 inputs and output one of three possible classes.

After about 2 seconds of training, we try to visualize what the neural network has learned (also part of the circles.rb command) in this way: for each point in an 80x80 grid, we ask the network to classify the point. This is the ASCII-artist result:

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/...............................................................................
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///////////////////.........../////////.........................................
///////////////////..........//////////..OOOOOOOOO..............................
///////////////////..........//////////.OOOOOOOOOOOOOO..........................
///////////////////..........//////////OOOOOOOOOOOOOOOOOOOO.....................
///////////////////........../////////OOOOOOOOOOOOOOOOOOOOOOOOO.................
////////////////////......../////////OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO..
////////////////////........///////.OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
/////////////////////......///////.OOOOOOOOOOOOOOOOOOOOOOOOO..OOOOOOOOOOOOOOOOOO
/////////////////////......//////..OOOOOOOOOOOOOOOOOOOOOOO......OOOOOOOOOOOOOOOO
//////////////////////....//////...OOOOOOOOOOOOOOOOOOOOOO........OOOOOOOOOOOOOOO
//////////////////////////////......OOOOOOOOOOOOOOOOOOOO.........OOOOOOOOOOOOOOO
////////////////////////////........OOOOOOOOOOOOOOOOOOO.........OOOOOOOOOOOOOOOO
//////////////////////////...........OOOOOOOOOOOOOOOOO.........OOOOOOOOOOOOOOOOO
////////////////////////..............OOOOOOOOOOOOOO..........OOOOOOOOOOOOOOOOOO
///////////////////////....................OOOOOOOO..........OOOOOOOOOOOOOOOOOOO
//////////////////////.....................OOOOOOOO.........OOOOOOOOOOOOOOOOOOOO
/////////////////////......................OOOOOOO........OOOOOOOOOOOOOOOOOOOOOO
////////////////////........................OOOOO........OOOOOOOOOOOOOOOOOOOOOOO
///////////////////.........................OOOO.......OOOOOOOOOOOOOOOOOOOOOOOOO
//////////////////..........................OOOO.....OOOOOOOOOOOOOOOOOOOOOOOOOOO
/////////////////............................OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
////////////////.............................OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
///////////////........................../...OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
//////////////..........................//...OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
/////////////..........................////..OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
////////////.........................//////..OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
//////////..........................///////..OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
/////////..........................////////..OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
////////..........................//////////.OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
///////..........................///////////.OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
/////........................../////////////.OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
////..........................//////////////.OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
///..........................///////////////.OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
//........................../////////////////OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
...........................//////////////////OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
..........................///////////////////OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
.........................////////////////////OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
......................../////////////////////OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

As you can see, while the problem had no linear solution, the neural network was able to split the 2D space into areas, with the holes where there is the intersection between the circles areas, and thiner surfaces where the circles actually cross each other (in the intersection between the two circumferences there are points of two different classes).

This example was not practical perhaps but shows well the power of the neural network in non linear tasks.

Neural Redis is not the right tool for advanced NLP tasks, and for sentiment analysis, which is a very hard problem, there are RNNs and other, more complex tools, that can provide state of art results.

However exactly for the same reason, SA is a very good example to show how to model problems, and that even the simplest of the intuitions can allow Neural Redis to handle problems in a decent way (even if far from the top specialized systems) after a training of 5 minutes or so.

This case study is based on the source code inside the examples directory called sentiment.rb. It uses a very popular dataset used for sentiment analysis benchmarking, composed of 2000 movies reviews, 1000 which are positive, and 1000 negative.

The reviews are like the following:

It must be some sort of warped critical nightmare: the best movie of
the year would be a summer vehicle, a jim carrey vehicle at that.
And so it is. The truman show is the most perplexing, crazed, paranoid
and rib-tickling morality play i've seen since i-don't-know-when.

Normally we should try to do the best we can do in order to pre-process the data, but we are lazy dogs, so we don't do anything at all. However we still need to map our inputs and outputs to meaningful parameters. For the outputs, it's trivial, is a categorization task: negative or positive. But how do we map words to inputs?

Normally you assign different words to different IDs, and then use such IDs as indexes. This creates two problems in our case:

1. We need to select a vocabulary. Usually this is done in a pre-processing stage where we potentially examine a non-annotated large corpus of text. But remember that we are lazy?
2. "very good" and "not good" have very different meanings, we can't stop to single words, otherwise our result is likely be disappointing.

So I did the following. Let's say our network is composed of 3000 inputs, 100 hidden units, and the 2 outputs for the classification.

We split the initial inputs into two sides: 1500 of inputs just take the single words. The other 1500 inputs, we use for combinations of two words. What I did was to just use hashing to map the words in the text to the input units:

INDEX_1 = HASH(word) % 1500
INDEX_2 = 1500 + (HASH(word + next_word) % 1500)

This is a bit crazy, I'm curious to know if it's something that people tried in the past, since different words and different combinations of words will hash to the same, so we'll get a bit less precise results, however it is unlikely that words highly polarized in the opposite direction (positive VS negative) will hash to the same bucket, if we use enough inputs.

So each single word and combination of words is a "vote" in the input unit. As we scan the sentences to give the votes, we also sum all the single votes we gave, so that we finally normalize to make sure all our inputs summed will give "1". This way the sentiment analysis does not depend by the length of the sentence.

While this approach is very simple, it works and produces a NN in a matter of seconds that can score 80% in the 2000 movies dataset. I just spent a couple of hours on it, probably it's possible to do much better with a more advanced scheme. However the gist of this use case is: be creative when trying to map your data to the neural network.

If you run sentiment.rb you'll see the network quickly converging and at the end, you'll be able to type sentences that the NN will classify as positive or negative:

nn_id=7 cycle=61 key=sentiment ... classerr=21.500000
nn_id=7 cycle=62 key=sentiment ... classerr=20.333334

Best net so far can predict sentiment polarity 78.17 of times

Imagine and type a film review sentence:

> WTF this movie was terrible
Negativity: 0.99966669082641602
Positivity: 0.00037576013710349798

> Good one
Negativity: 0.28475716710090637
Positivity: 0.73368257284164429

> This is a masterpiece
Negativity: 2.219095662781001e-08
Positivity: 0.99999994039535522

Of course you'll find a number of sentences that the net will classify in the wrong way... However the longer sentence you type and more similar to an actual movie review, the more likely it can predict it correctly.

In the above tutorial not all the options of all the commands may be covered, so here there is a small reference with all the commands supported by this extension and associated options.

Create a new neural network if the target key is empty, or returns an error.

• key - The key name holding the neural network.
• CLASSIFIER or REGRESSOR is the network type, read this tutorial for more info.
• inputs - Number of input units
• hidden-layer-units zero or more arguments indicating the number of hidden units, one number for each layer.
• outputs - Number of outputs units
• NORMALIZE - Specify if you want the network to normalize your inputs. Use this if you don't know what we are talking about.
• DATASET maxlen - Max number of data samples in the training dataset.
• TEST maxlen - Max number of data samples in the testing dataset.

Example:

NR.CREATE mynet CLASSIFIER 64 100 -> 10 NORMALIZE DATASET 1000 TEST 500

Add a data sample into the training or testing dataset (if specified as last argument) or evenly into one or the other, according to their respective sizes, if no target is specified.

For neural networks of type CLASSIFIER the output must be just one, in the range from 0 to number-of-outputs - 1. It's up to the network to translate the class ID into a set of zeros and ones.

The command returns the number of data samples inside the training and testing dataset. If the target datasets are already full, a random entry is evicted and substituted with the new data.

Run the network stored at key, returning an array of outputs.

Like NR.RUN but can be used only with NNs of type CLASSIFIER. Instead of outputting the raw neural network outputs, the command returns the output class directly, which is, the index of the output with the greatest value.

Train a network in a background thread. When the training finishes automatically updates the weights of the trained networks with the new ones and updates the training statistics.

The command works with a copy of the network, so it is possible to use the network while it is undergoing a training.

If no AUTOSTOP is specified, trains the network till the maximum number of cycles or milliseconds are reached. If no maximum number of cycles is specified there are no cycles limits. If no milliseconds are specified, the limit is set to 10000 milliseconds (10 seconds).

If AUTOSTOP is specified, the training will still stop when the maximum umber of cycles or milliseconds is specified, but will also try to stop the training if overfitting is detected. Check the previous sections for a description of the (still naive) algorithm the implementation uses in order to stop.

If BACKTRACK is specified, and AUTOSTOP is also specified, while the network is trained, the trainer thread saves a copy of the neural network every time it has a better score compared to the previously saved one and there are hints suggesting that overfitting may happen soon. This network is used later if it is found to have a smaller error.

Show many internal information about the neural network. Just try it :-)

Show all the active training threads.

Set the neural network weights to random ones (that is, the network will completely unlearn what it learned so far), and reset training statistics. However the datasets are not touched at all. This is useful when you want to retrain a network from scratch.

The main aim of Neural Redis, which is currently just a 48h personal hackatlon, is to show the potential that there is in an accessible API that provides a simple to use machine learning tool, that can be used and trained interactively.

However the neural network implementation can be surely improved in different ways, so if you are an expert in this field, feel free to submit changes or ideas. One thing that I want to retain is the simplicity of the outer layer: the API. However the techniques used in the internals can be more complex in order to improve the results.

There is to note that, given the API exported, the implementation of the neural network should be, more than state of art in solving a specific problem, more designed in order to work well enough in a large set of conditions. While the current fully connected network has its limits, it together with BPROP learning shows to be quite resistant to misuses. So an improved version should be able to retain, and extend this quality. The simplest way to guarantee this is to have a set of benchmarks of different types using open datasets, and to score different implementations against it.

• Better overfitting detection.
• Implement RNNs with a simpler to use API.
• Use a different loss function for classification NNs.
• Get some ML expert which is sensible to simple APIs involved.

Have fun with machine learning,

Salvatore