def __get_word2vec_saved_model(self):
model = None
path = get_complet_path('keras_models') + "/word2vec.model"
if (Path(path).is_file()):
model = Word2Vec.load(path)
return model, path
def get_saved_model(name):
model = None
path = get_complet_path('keras_models') + "/" + name + ".h5"
# We save/load model to improve performance
if (Path(path).is_file()):
model = load_model(path)
return model, path
def main():
corpus = get_file_content_with_br(get_complet_path("proverbes.txt"))
tests = json.loads(get_file_content(get_complet_path("test1.txt")))
tokens = tokenize_text(corpus)
tokens.append("UNK")
complet_proverbe_with_trigram(tokens, tests)
# First solution
complet_proverbe_with_unigram(tokens, tests)
complet_proverbe_with_bigram(tokens, tests)
complet_proverbe_with_trigram(tokens, tests)
# Second solution with laplace
complet_with_ngram(tokens, tests, ngram_number=1, laplace=1)
complet_with_ngram(tokens, tests, ngram_number=2, laplace=0)
complet_with_ngram(tokens, tests, ngram_number=3, laplace=0)
complet_with_ngram(tokens, tests, ngram_number=3, laplace=10)
# Perplexity
perplexity_of_sentence("a beau mentir qui vient de loin", tokens, 0)
perplexity_of_sentence("something not trained in our model", tokens, 0)
def main():
ingredients_text = get_file_content(get_complet_path("ingredients.txt"))
solutions_text = get_file_content(get_complet_path("ingredients_solutions.txt"))
quantity_correct = 0
quantity_wrong = 0
print("###################### SOLUTION")
for count,ingredient_text in ingredients_text.items():
ingredient = get_ingredient(ingredient_text)
ingredient_solution = solutions_text[count].split(" ")
solution_quantity = ingredient_solution[1].replace("QUANTITE:","")
solution_item = ingredient_solution[2].replace("INGREDIENT:","")
if (solution_quantity == ingredient.quantity and solution_item == ingredient.item):
quantity_correct +=1
else:
quantity_wrong +=1
print("NOT MATCH:\n {!r} \n Solution (item='{}', quantity='{}')\n".format(ingredient, solution_item,solution_quantity))
succes_rate = quantity_correct * 100 / len(ingredients_text)
print("\nResult: CORRECT({}) x WRONG({}) - {:.2f}%".format(quantity_correct, quantity_wrong, succes_rate))
print()
def remove_saved_keras_models(remove_models):
if (remove_models):
files = glob.glob(get_complet_path("keras_models") + '/*')
for f in files:
os.remove(f)