Self Governing Neural Networks (SGNN): the Projection Layer

Related tags

Deep LearningSGNN-Self-Governing-Neural-Networks-Projection-Layer
Overview

Self Governing Neural Networks (SGNN): the Projection Layer

A SGNN's word projections preprocessing pipeline in scikit-learn

In this notebook, we'll use T=80 random hashing projection functions, each of dimensionnality d=14, for a total of 1120 features per projected word in the projection function P.

Next, we'll need feedforward neural network (dense) layers on top of that (as in the paper) to re-encode the projection into something better. This is not done in the current notebook and is left to you to implement in your own neural network to train the dense layers jointly with a learning objective. The SGNN projection created hereby is therefore only a preprocessing on the text to project words into the hashing space, which becomes spase 1120-dimensional word features created dynamically hereby. Only the CountVectorizer needs to be fitted, as it is a char n-gram term frequency prior to the hasher. This one could be computed dynamically too without any fit, as it would be possible to use the power set of the possible n-grams as sparse indices computed on the fly as (indices, count_value) tuples, too.

import sklearn
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.random_projection import SparseRandomProjection
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import cosine_similarity

from collections import Counter
from pprint import pprint

Preparing dummy data for demonstration:

SentenceTokenizer.MAXIMUM_SENTENCE_LENGTH: # clip too long sentences. sub_phrase = phrase[:SentenceTokenizer.MAXIMUM_SENTENCE_LENGTH].lstrip() splitted_string.append(sub_phrase) phrase = phrase[SentenceTokenizer.MAXIMUM_SENTENCE_LENGTH:].rstrip() if len(phrase) >= SentenceTokenizer.MINIMUM_SENTENCE_LENGTH: splitted_string.append(phrase) return splitted_string with open("./data/How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks.md") as f: raw_data = f.read() test_str_tokenized = SentenceTokenizer().fit_transform(raw_data) # Print text example: print(len(test_str_tokenized)) pprint(test_str_tokenized[3:9])"> 
class SentenceTokenizer(BaseEstimator, TransformerMixin):
    # char lengths:
    MINIMUM_SENTENCE_LENGTH = 10
    MAXIMUM_SENTENCE_LENGTH = 200
    
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        return self._split(X)
    
    def _split(self, string_):
        splitted_string = []
        
        sep = chr(29)  # special separator character to split sentences or phrases.
        string_ = string_.strip().replace(".", "." + sep).replace("?", "?" + sep).replace("!", "!" + sep).replace(";", ";" + sep).replace("\n", "\n" + sep)
        for phrase in string_.split(sep):
            phrase = phrase.strip()
            
            while len(phrase) > SentenceTokenizer.MAXIMUM_SENTENCE_LENGTH:
                # clip too long sentences.
                sub_phrase = phrase[:SentenceTokenizer.MAXIMUM_SENTENCE_LENGTH].lstrip()
                splitted_string.append(sub_phrase)
                phrase = phrase[SentenceTokenizer.MAXIMUM_SENTENCE_LENGTH:].rstrip()
            
            if len(phrase) >= SentenceTokenizer.MINIMUM_SENTENCE_LENGTH:
                splitted_string.append(phrase)

        return splitted_string


with open("./data/How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks.md") as f:
    raw_data = f.read()

test_str_tokenized = SentenceTokenizer().fit_transform(raw_data)

# Print text example:
print(len(test_str_tokenized))
pprint(test_str_tokenized[3:9])
168
["Have you ever been in the situation where you've got Jupyter notebooks "
 '(iPython notebooks) so huge that you were feeling stuck in your code?',
 'Or even worse: have you ever found yourself duplicating your notebook to do '
 'changes, and then ending up with lots of badly named notebooks?',
 "Well, we've all been here if using notebooks long enough.",
 'So how should we code with notebooks?',
 "First, let's see why we need to be careful with notebooks.",
 "Then, let's see how to do TDD inside notebook cells and how to grow a neat "
 'software architecture out of your notebooks.']

Creating a SGNN preprocessing pipeline's classes

<" end_of_word = ">" out = [ [ begin_of_word + word + end_of_word for word in sentence.replace("//", " /").replace("/", " /").replace("-", " -").replace(" ", " ").split(" ") if not len(word) == 0 ] for sentence in X ] return out "> 
class WordTokenizer(BaseEstimator, TransformerMixin):
    
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        begin_of_word = "<"
        end_of_word = ">"
        out = [
            [
                begin_of_word + word + end_of_word
                for word in sentence.replace("//", " /").replace("/", " /").replace("-", " -").replace("  ", " ").split(" ")
                if not len(word) == 0
            ]
            for sentence in X
        ]
        return out
char_ngram_range = (1, 4)

char_term_frequency_params = {
    'char_term_frequency__analyzer': 'char',
    'char_term_frequency__lowercase': False,
    'char_term_frequency__ngram_range': char_ngram_range,
    'char_term_frequency__strip_accents': None,
    'char_term_frequency__min_df': 2,
    'char_term_frequency__max_df': 0.99,
    'char_term_frequency__max_features': int(1e7),
}

class CountVectorizer3D(CountVectorizer):

    def fit(self, X, y=None):
        X_flattened_2D = sum(X.copy(), [])
        super(CountVectorizer3D, self).fit_transform(X_flattened_2D, y)  # can't simply call "fit"
        return self

    def transform(self, X):
        return [
            super(CountVectorizer3D, self).transform(x_2D)
            for x_2D in X
        ]
    
    def fit_transform(self, X, y=None):
        return self.fit(X, y).transform(X)
import scipy.sparse as sp

T = 80
d = 14

hashing_feature_union_params = {
    # T=80 projections for each of dimension d=14: 80 * 14 = 1120-dimensionnal word projections.
    **{'union__sparse_random_projection_hasher_{}__n_components'.format(t): d
       for t in range(T)
    },
    **{'union__sparse_random_projection_hasher_{}__dense_output'.format(t): False  # only AFTER hashing.
       for t in range(T)
    }
}

class FeatureUnion3D(FeatureUnion):
    
    def fit(self, X, y=None):
        X_flattened_2D = sp.vstack(X, format='csr')
        super(FeatureUnion3D, self).fit(X_flattened_2D, y)
        return self
    
    def transform(self, X): 
        return [
            super(FeatureUnion3D, self).transform(x_2D)
            for x_2D in X
        ]
    
    def fit_transform(self, X, y=None):
        return self.fit(X, y).transform(X)

Fitting the pipeline

Note: at fit time, the only thing done is to discard some unused char n-grams and to instanciate the random hash, the whole thing could be independent of the data, but here because of discarding the n-grams, we need to "fit" the data. Therefore, fitting could be avoided all along, but we fit here for simplicity of implementation using scikit-learn.

params = dict()
params.update(char_term_frequency_params)
params.update(hashing_feature_union_params)

pipeline = Pipeline([
    ("word_tokenizer", WordTokenizer()),
    ("char_term_frequency", CountVectorizer3D()),
    ('union', FeatureUnion3D([
        ('sparse_random_projection_hasher_{}'.format(t), SparseRandomProjection())
        for t in range(T)
    ]))
])
pipeline.set_params(**params)

result = pipeline.fit_transform(test_str_tokenized)

print(len(result), len(test_str_tokenized))
print(result[0].shape)
168 168
(12, 1120)

Let's see the output and its form.

print(result[0].toarray().shape)
print(result[0].toarray()[0].tolist())
print("")

# The whole thing is quite discrete:
print(set(result[0].toarray()[0].tolist()))

# We see that we could optimize by using integers here instead of floats by counting the occurence of every entry.
print(Counter(result[0].toarray()[0].tolist()))
(12, 1120)
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.005715251142432, 0.0, 2.005715251142432, 0.0, 0.0, 2.005715251142432, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

{0.0, 2.005715251142432, -2.005715251142432}
Counter({0.0: 1069, -2.005715251142432: 27, 2.005715251142432: 24})

Checking that the cosine similarity before and after word projection is kept

Note that this is a yet low-quality test, as the neural network layers above the projection are absent, so the similary is not yet semantic, it only looks at characters.

0.5 else "no") print("\t - similarity after :", cos_sim_after , "\t Are words similar?", "yes" if cos_sim_after > 0.5 else "no") print("")"> 
word_pairs_to_check_against_each_other = [
    # Similar:
    ["start", "started"],
    ["prioritize", "priority"],
    ["twitter", "tweet"],
    ["Great", "great"],
    # Dissimilar:
    ["boat", "cow"],
    ["orange", "chewbacca"],
    ["twitter", "coffee"],
    ["ab", "ae"],
]

before = pipeline.named_steps["char_term_frequency"].transform(word_pairs_to_check_against_each_other)
after = pipeline.named_steps["union"].transform(before)

for i, word_pair in enumerate(word_pairs_to_check_against_each_other):
    cos_sim_before = cosine_similarity(before[i][0], before[i][1])[0,0]
    cos_sim_after  = cosine_similarity( after[i][0],  after[i][1])[0,0]
    print("Word pair tested:", word_pair)
    print("\t - similarity before:", cos_sim_before, 
          "\t Are words similar?", "yes" if cos_sim_before > 0.5 else "no")
    print("\t - similarity after :", cos_sim_after , 
          "\t Are words similar?", "yes" if cos_sim_after  > 0.5 else "no")
    print("")
Word pair tested: ['start', 'started']
	 - similarity before: 0.8728715609439697 	 Are words similar? yes
	 - similarity after : 0.8542062410985866 	 Are words similar? yes

Word pair tested: ['prioritize', 'priority']
	 - similarity before: 0.8458888522202895 	 Are words similar? yes
	 - similarity after : 0.8495862181305898 	 Are words similar? yes

Word pair tested: ['twitter', 'tweet']
	 - similarity before: 0.5439282932204212 	 Are words similar? yes
	 - similarity after : 0.4826046482460216 	 Are words similar? no

Word pair tested: ['Great', 'great']
	 - similarity before: 0.8006407690254358 	 Are words similar? yes
	 - similarity after : 0.8175049752615363 	 Are words similar? yes

Word pair tested: ['boat', 'cow']
	 - similarity before: 0.1690308509457033 	 Are words similar? no
	 - similarity after : 0.10236537810666581 	 Are words similar? no

Word pair tested: ['orange', 'chewbacca']
	 - similarity before: 0.14907119849998599 	 Are words similar? no
	 - similarity after : 0.2019908169580899 	 Are words similar? no

Word pair tested: ['twitter', 'coffee']
	 - similarity before: 0.09513029883089882 	 Are words similar? no
	 - similarity after : 0.1016460166230715 	 Are words similar? no

Word pair tested: ['ab', 'ae']
	 - similarity before: 0.408248290463863 	 Are words similar? no
	 - similarity after : 0.42850530886130067 	 Are words similar? no

Next up

So we have created the sentence preprocessing pipeline and the sparse projection (random hashing) function. We now need a few feedforward layers on top of that.

Also, a few things could be optimized, such as using the power set of the possible n-gram values with a predefined character set instead of fitting it, and the Hashing's fit function could be avoided as well by passing the random seed earlier, because the Hasher doesn't even look at the data and it only needs to be created at some point. This would yield a truly embedding-free approach. Free to you to implement this. I wanted to have something that worked first, leaving optimization for later.

License

BSD 3-Clause License

Copyright (c) 2018, Guillaume Chevalier

All rights reserved.

Extra links

Connect with me

Liked this piece of code? Did it help you? Leave a star, fork and share the love!

Owner
Guillaume Chevalier
e^(πi) + 1 = 0
Guillaume Chevalier
GitHub Repository https://github.com/guillaume-chevalier/NLP-TP3
sense-py-AnishaBaishya created by GitHub Classroom

Compute Statistics Here we compute statistics for a bunch of numbers. This project uses the unittest framework to test functionality. Pass the tests T

1 Oct 21, 2021
An implementation of the paper "A Neural Algorithm of Artistic Style"

A Neural Algorithm of Artistic Style implementation - Neural Style Transfer This is an implementation of the research paper "A Neural Algorithm of Art

Srijarko Roy 27 Sep 20, 2022
Implementation of GGB color space

GGB Color Space This package is implementation of GGB color space from Development of a Robust Algorithm for Detection of Nuclei and Classification of

Resha Dwika Hefni Al-Fahsi 2 Oct 06, 2021
implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"

MarginGAN This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning". 1."preliminary" is the imp

Van 7 Dec 23, 2022
Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward? Models Playground is here to help you do that. Models playground allows you to train your models right from the browser.

Models Playground 🗂️ Upload a Preprocessed Dataset 🌠 Choose whether to perform Classification or Regression 🦹 Enter the Dependent Variable ?

19 Dec 10, 2022
Tensorflow implementation of "Learning Deconvolution Network for Semantic Segmentation"

Tensorflow implementation of Learning Deconvolution Network for Semantic Segmentation. Install Instructions Works with tensorflow 1.11.0 and uses the

Fabian Bormann 224 Apr 15, 2022
Ppq - A powerful offline neural network quantization tool with custimized IR

PPL Quantization Tool(PPL 量化工具) PPL Quantization Tool (PPQ) is a powerful offlin

605 Jan 03, 2023
Text-Based Ideal Points

Text-Based Ideal Points Source code for the paper: Text-Based Ideal Points by Keyon Vafa, Suresh Naidu, and David Blei (ACL 2020). Update (June 29, 20

Keyon Vafa 37 Oct 09, 2022
Steer OpenAI's Jukebox with Music Taggers

TagBox Steer OpenAI's Jukebox with Music Taggers! The closest thing we have to VQGAN+CLIP for music! Unsupervised Source Separation By Steering Pretra

Ethan Manilow 34 Nov 02, 2022
DCA - Official Python implementation of Delaunay Component Analysis algorithm

Delaunay Component Analysis (DCA) Official Python implementation of the Delaunay

Petra Poklukar 9 Sep 06, 2022
Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"

NTIRE2017 Super-resolution Challenge: SNU_CVLab Introduction This is our project repository for CVPR 2017 Workshop (2nd NTIRE). We, Team SNU_CVLab, (B

Bee Lim 625 Dec 30, 2022
Ganilla - Official Pytorch implementation of GANILLA

GANILLA We provide PyTorch implementation for: GANILLA: Generative Adversarial Networks for Image to Illustration Translation. Paper Arxiv Updates (Fe

Samet Hi 462 Dec 05, 2022
The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021) Project Page | Paper Xudong Xu, Xingang Pan, Dahua Lin and Bo Dai GOF

xuxudong 97 Nov 10, 2022
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized

VQGAN-CLIP-Docker About Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized This is a stripped and minimal dependency repository for running loca

Kevin Costa 73 Sep 11, 2022
Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

This is the official repository of my book "Deep Learning with PyTorch Step-by-Step". Here you will find one Jupyter notebook for every chapter in the book.

Daniel Voigt Godoy 340 Jan 01, 2023
Image Segmentation Evaluation

Image Segmentation Evaluation Martin Keršner, [email protected] Evaluation

Martin Kersner 273 Oct 28, 2022
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi

Rakuten Group, Inc. 0 Nov 19, 2021
Tutoriais publicados nas nossas redes sociais para obtenção de dados, análises simples e outras tarefas relevantes no mercado financeiro.

Tutoriais Públicos Tutoriais publicados nas nossas redes sociais para obtenção de dados, análises simples e outras tarefas relevantes no mercado finan

Trading com Dados 68 Oct 15, 2022
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Real-Time Seizure Detection using Electroencephalogram (EEG) This is the repository for "Real-Time Seizure Detection using EEG: A Comprehensive Compar

AITRICS 30 Dec 17, 2022
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks (Scientific Reports)

SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks Molecular interaction networks are powerful resources for the discovery. While dee

Kexin Huang 49 Oct 15, 2022