AI News, gianlucabertani/MAChineLearning

gianlucabertani/MAChineLearning

Setting up a network is a matter of two lines: These lines create a single layer perceptron with 3 inputs and 1 output, with step activation function, and randomize its initial weights.

In this case, you can make use of macros defined in MLReal.h, to avoid changing function names in case you later move from single to double precision: Once the input buffer is filled, computing the output is simple: If the output is not satisfactory, you can set the expected output in its specific buffer and ask the network to backpropagate the error.

Once your training batch is complete, update weights in the following way: During training, you typically feed the full sample set to the network multiple times, so that it increases its predictive capabilities.

typical training loop is the following: While a training loop with batch updates is the following: The network enforces the correct calling sequence by using a simple state machine.

It is based on an vector of numbers where each element represents a word in the text, and (in its simplest form) is either set to 1 o 0 if that word occurs or not in the text to be represented.

Given a text, it is then split in separate words (a process called tokenization) and, for every word, they are looked up in the dictionary and their corresponding element on the array is set accordingly.

number of improvements may be applied to this process, including the removal of frequently words (called stop words), more or less sophisticated tokenization, normalization of the bag of words vector, et.c The Bag of Words toolkit in MAChineLearning currently supports: With MAChineLearning, the dictionary for the Bag of Words is built progressively as texts are tokenized, you just need to fix its maximum size from the beginning.

For language guessing there are two utility methods available, employing either the macOS integrated linguistic tagger or an alternative algorithm that counts occurrences of stop words: The language is expresses as a ISO-639-1 code, such as 'en' for English, 'fr' for French, etc.

The most extended version of the MLBagOfWords factory method includes parameters to specify both the tokenizer and its tokenization options: Default configurations are the following: You may need to experiment a bit to find the correct configuration for your task.

In fact, Word Vectors can be summed and subtracted to form new meanings, such as the following well know examples: The Word Vectors toolkit in MAChineLearning supports loading pre-computed word vector dictionaries of the following models: Note: While a tentative at building a Word Vectors dictionary from a text corpus has been made, using the neural networks of MAChineLearning, it resulted impractically slow.

Computing Word Vectors from scratch, in fact, requires code specifically optimized for the task, since each text is a sparse vector (a Bag of Words, actually) and a general purpose neural network wastes lots of time computing values for zeroed elements.

Each vector provides methods to sum and subtract to/from other vectors, and the dictionary provides methods to search for the nearest word to a vector: Each Word Vector exposes its full vector as a C buffer (array), ready to be feeded to a neural network: The framework contains some unit tests that show how to use it, see WordVectorTests.m.

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Bag of Words (BoW) is an algorithm that counts how many times a word appears in a document.

Those word counts allow us to compare documents and gauge their similarities for applications like search, document classification and topic modeling.

In the table where the words and documents effectively become vectors are stored, each row is a word, each column is a document and each cell is a word count.

Before they’re fed to the neural net, each vector of wordcounts is normalized such that all elements of the vector add up to one.

First, TF-IDF measures the number of times that words appear in a given document (that’s term frequency).

Those marker words are then fed to the neural net as features in order to determine the topic covered by the document that contains them.

A Gentle Introduction to the Bag-of-Words Model

The bag-of-words model is a way of representing text data when modeling text with machine learning algorithms.

The bag-of-words model is simple to understand and implement and has seen great success in problems such as language modeling and document classification.

problem with modeling text is that it is messy, and techniques like machine learning algorithms prefer well defined fixed-length inputs and outputs.

In language processing, the vectors x are derived from textual data, in order to reflect various linguistic properties of the text.

The unique words here (ignoring case and punctuation) are: That is a vocabulary of 10 words from a corpus containing 24 words.

Because we know the vocabulary has 10 words, we can use a fixed-length document representation of 10, with one position in the vector to score each word.

The simplest scoring method is to mark the presence of words as a boolean value, 0 for absent, 1 for present.

Using the arbitrary ordering of words listed above in our vocabulary, we can step through the first document (“It was the best of times“) and convert it into a binary vector.

The scoring of the document would look as follows: As a binary vector, this would look as follows: The other three documents would look as follows: All ordering of the words is nominally discarded and we have a consistent way of extracting features from any document in our corpus, ready for use in modeling.

New documents that overlap with the vocabulary of known words, but may contain words outside of the vocabulary, can still be encoded, where only the occurrence of known words are scored and unknown words are ignored.

Sparse vectors require more memory and computational resources when modeling and the vast number of positions or dimensions can make the modeling process very challenging for traditional algorithms.

An N-gram is an N-token sequence of words: a 2-gram (more commonly called a bigram) is a two-word sequence of words like “please turn”, “turn your”, or “your homework”, and a 3-gram (more commonly called a trigram) is a three-word sequence of words like “please turn your”, or “turn your homework”.

vocabulary then tracks triplets of words is called a trigram model and the general approach is called the n-gram model, where n refers to the number of grouped words.

Some additional simple scoring methods include: You may remember from computer science that a hash function is a bit of math that maps data to a fixed size set of numbers.

This addresses the problem of having a very large vocabulary for a large text corpus because we can choose the size of the hash space, which is in turn the size of the vector representation of the document.

The challenge is to choose a hash space to accommodate the chosen vocabulary size to minimize the probability of collisions and trade-off sparsity.

Text Classification using Neural Networks

We’ll use 2 layers of neurons (1 hidden layer) and a “bag of words” approach to organizing our training data.

While the algorithmic approach using Multinomial Naive Bayes is surprisingly effective, it suffers from 3 fundamental flaws: As with its ‘Naive’ counterpart, this classifier isn’t attempting to understand the meaning of a sentence, it’s trying to classify it.

We will take the following steps: The code is here, we’re using iPython notebook which is a super productive way of working on data science projects.

The above step is a classic in text classification: each training sentence is reduced to an array of 0’s and 1’s against the array of unique words in the corpus.

We are now ready to build our neural network model, we will save this as a json structure to represent our synaptic weights.

This parameter helps our error adjustment find the lowest error rate: synapse_0 += alpha * synapse_0_weight_update We use 20 neurons in our hidden layer, you can adjust this easily.

These parameters will vary depending on the dimensions and shape of your training data, tune them down to ~10^-3 as a reasonable error rate.

low-probability classification is easily shown by providing a sentence where ‘a’ (common word) is the only match, for example: Here you have a fundamental piece of machinery for building a chat-bot, capable of handling a large # of classes (‘intents’) and suitable for classes with limited or extensive training data (‘patterns’).

Another Twitter sentiment analysis with Python — Part 10 (Neural Network with Doc2Vec/Word2Vec/GloVe)

After I got document vectors from each model, I have tried concatenating these (so the concatenated document vectors have 200 dimensions) in combination: DBOW + DMM, DBOW + DMC, and saw an improvement to the performance when compared with models with any single pure method.

Finally, I have applied phrase modelling to detect bigram phrase and trigram phrase as a pre-step of Doc2Vec training and tried different combination across n-grams.

When fed to a simple logistic regression, the concatenated document vectors (unigram DBOW + trigram DMM) yields 75.90% training set accuracy, and 75.76% validation set accuracy.

I explicitly specified backend as Theano by launching Jupyter Notebook in the command line as follows: “KERAS_BACKEND=theano jupyter notebook” Please note that not all of the dependencies loaded in the below cell has been used for this post, but imported for later use.

After trying 12 different models with a range of hidden layers (from 1 to 3) and a range of hidden nodes for each hidden layer (64, 128, 256, 512), below is the result I got.

Best validation accuracy (79.93%) is from “model_d2v_09” at epoch 7, which has 3 hidden layers of 256 hidden nodes for each hidden layer.

You can set the “checkpoint” function with options, and with the below parameter setting, “checkpoint” will save the best performing model up until the point of running, and only if a new epoch outperforms the saved model it will save it as a new model.

And “early_stop” I defined it as to monitor validation accuracy, and if it doesn’t outperform the best validation accuracy so far for 5 epochs, it will stop.

If you remember the validation accuracy with the same vector representation of the tweets with a logistic regression model (75.76%), you can see that feeding the same information to neural networks yields a significantly better result.

It’s amazing to see how neural network can boost the performance of dense vectors, but the best validation accuracy is still lower than the Tfidf vectors + logistic regression model, which gave me 82.92% validation accuracy.

What I will do first before I try neural networks with document representations computed from word vectors is that I will fit logistic regressions with various methods of document representation and with the one that gives me the best validation accuracy, I will finally define a neural network model.

For every word in a tweet, see if trained Doc2Vec has word vector representation of the word, if so, sum them up throughout the document while counting how many words were detected as having word vectors, and finally by dividing the summed vector by the count you get the averaged word vector for the whole document which will have the same dimension (200 in this case) as the individual word vectors.

The validation accuracy with averaged word vectors of unigram DBOW + unigram DMM is 71.74%, which is significantly lower than document vectors extracted from unigram DBOW + trigram DMM (75.76%), and also from the results I got from the 6th part of this series, I know that document vectors extracted from unigram DBOW + unigram DMM will give me 75.51% validation accuracy.

also tried scaling the vectors using ScikitLearn’s scale function and saw significant improvement in computation time and a slight improvement of the accuracy.

et al (2017) has implemented this Tf-idf weighting in their paper “NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis” In order to get the Tfidf value for each word, I first fit and transform the training set with TfidfVectorizer and create a dictionary containing “word”, “tfidf value” pairs.

To be honest, I am still not sure why it took so long to compute the Tfidf weighting of the word vectors, but after 5 hours it finally finished computing.

To give you a high-level intuition, by calculating harmonic mean of CDF(Cumulative Distribution Function) transformed values of term frequency rate within the whole document and the term frequency within a class, you can get a meaningful metric which shows how each word is related to a certain class.

have used this metric to visualise tokens in the 3rd part of the series, and also used this again to create custom lexicon to be used for classification purpose in the 5th part.

In Word2Vec, the word vectors you are getting is a kind of a by-product of a shallow neural network, when it tries to predict either centre word given surrounding words or vice versa.

So far the best validation accuracy was from the averaged word vectors with custom weighting, which gave me 73.27% accuracy, and compared to this, GloVe vectors yield 76.27%, 76.60% for average and sum respectively.

The concatenated vectors of unigram CBOW and unigram Skip Gram models has yielded 76.50%, 76.75% validation accuracy respectively with mean and sum method.

Based on what I have observed during trials of different architectures with Doc2Vec document vectors, the best performing architecture was one with 3 hidden layers with 256 hidden nodes at each hidden layer.I will finally fit a neural network with early stopping and checkpoint so that I can save the best performing weights on validation accuracy.

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