[MAJOR UPDATE] Added syllable neural networks
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@ -2,9 +2,9 @@
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@ -35,8 +35,8 @@
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@ -150,7 +150,6 @@
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<find>shuffle_inputs</find>
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<find>generator</find>
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<find>content, feature_dictionary</find>
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<find>decode</find>
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<find>create_feature_dictionary</find>
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<find>with</find>
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<find>read</find>
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@ -160,6 +159,10 @@
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<find>dictionary</find>
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<find>create_dict</find>
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<find>split_content</find>
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<find>decode_position</find>
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<find>'r'</find>
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<find>decode</find>
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<find>complete_feature_dict</find>
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@ -878,8 +881,8 @@
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216
prepare_data.py
216
prepare_data.py
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@ -124,7 +124,7 @@ def read_content():
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def is_vowel(word_list, position, vowels):
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if word_list[position] in vowels:
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return True
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if word_list[position] == u'r' and (position - 1 < 0 or word_list[position - 1] not in vowels) and (position + 1 >= len(word_list) or word_list[position + 1] not in vowels):
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if (word_list[position] == u'r' or word_list[position] == u'R') and (position - 1 < 0 or word_list[position - 1] not in vowels) and (position + 1 >= len(word_list) or word_list[position + 1] not in vowels):
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return True
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return False
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@ -310,7 +310,7 @@ def old_generate_full_matrix_inputs():
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# Generate each y as an array of 11 numbers (with possible values between 0 and 1)
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def generate_X_and_y(dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels, feature_dictionary, shuffle_vector_location):
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def generate_X_and_y(dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels, feature_dictionary, shuffle_vector_location, shuffle=True):
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y = np.zeros((len(content), max_num_vowels))
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X = np.zeros((len(content), max_word, len(dictionary)))
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print('CREATING OTHER FEATURES...')
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@ -350,10 +350,10 @@ def generate_X_and_y(dictionary, max_word, max_num_vowels, content, vowels, acce
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y[i][0] = 1
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# y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
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i += 1
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print('SHUFFELING INPUTS...')
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X, y, X_other_features = shuffle_inputs(X, y, shuffle_vector_location, X_pure=X_other_features)
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print('INPUTS SHUFFELED!')
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if shuffle:
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print('SHUFFELING INPUTS...')
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X, y, X_other_features = shuffle_inputs(X, y, shuffle_vector_location, X_pure=X_other_features)
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print('INPUTS SHUFFELED!')
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return X, X_other_features, y
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@ -484,6 +484,12 @@ def generate_X_and_y_RAM_efficient(name, split_number):
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# metric for calculation of correct results
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# test with:
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# print(mean_pred(y_validate[pos], predictions[pos]).eval())
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# print(mean_pred(np.array([[ 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
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# [ 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.]]),
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# np.array([[ 0., 0.51, 0., 0.51, 0., 0., 0., 0., 0., 0., 0.],
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# [ 0., 0.92, 0., 0.51, 0., 0., 0., 0., 0., 0., 0.]])).eval())
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def actual_accuracy(y_true, y_pred):
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return K.mean(K.equal(K.mean(K.equal(K.round(y_true), K.round(y_pred)), axis=-1), 1.0))
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@ -602,7 +608,17 @@ def decode_X_features(feature_dictionary, X_other_features):
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return u''.join(final_word)
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def decode_position(y, max_num_vowels):
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def decode_position(y):
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i = 0
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res = []
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for el in y:
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if el >= 0.5:
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res.append(i)
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i += 1
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return res
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def old_decode_position(y, max_num_vowels):
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max_el = 0
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i = 0
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pos = -1
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@ -785,7 +801,7 @@ def convert_to_MULTEXT_east_v4(old_features, feature_dictionary):
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new_features[2] = '-'
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return new_features[:len(feature_dictionary[3]) - 1]
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if old_features[0] == 'N':
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if len(old_features) > 5:
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if len(old_features) >= 7:
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new_features[5] = old_features[7]
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return new_features[:len(feature_dictionary[4]) - 1]
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if old_features[0] == 'P':
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@ -974,3 +990,187 @@ def dict_occurances_in_dataset_rate(content):
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case_numbers = np.sum(X_other_features, axis=0)
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print(case_numbers)
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def get_voiced_consonants():
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return ['m', 'n', 'v', 'l', 'r', 'j', 'y', 'w']
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def get_resonant_silent_consonants():
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return ['b', 'd', 'z', 'ž', 'g']
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def get_unresonant_silent_consonants():
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return ['p', 't', 's', 'š', 'č', 'k', 'f', 'h', 'c']
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def split_consonants(consonants):
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# def voiced_consonants():
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# return ['m', 'n', 'v', 'l', 'r', 'j', 'y', 'w']
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#
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# def resonant_silent_consonants():
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# return ['b', 'd', 'z', 'ž', 'g']
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#
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# def unresonant_silent_consonants():
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# return ['p', 't', 's', 'š', 'č', 'k', 'f', 'h', 'c']
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# test = get_voiced_consonants()
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voiced_consonants = get_voiced_consonants()
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resonant_silent_consonants = get_resonant_silent_consonants()
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unresonant_silent_consonants = get_unresonant_silent_consonants()
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if len(consonants) == 0:
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return [''], ['']
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elif len(consonants) == 1:
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return [''], consonants
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else:
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split_options = []
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for i in range(len(consonants)-1):
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if consonants[i] == '-' or consonants[i] == '_':
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split_options.append([i, -1])
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elif consonants[i] == consonants[i+1]:
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split_options.append([i, 0])
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elif consonants[i] in voiced_consonants:
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if consonants[i+1] in resonant_silent_consonants or consonants[i+1] in unresonant_silent_consonants:
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split_options.append([i, 2])
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elif consonants[i] in resonant_silent_consonants:
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if consonants[i+1] in resonant_silent_consonants:
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split_options.append([i, 1])
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elif consonants[i+1] in unresonant_silent_consonants:
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split_options.append([i, 3])
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elif consonants[i] in unresonant_silent_consonants:
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if consonants[i+1] in resonant_silent_consonants:
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split_options.append([i, 4])
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else:
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print(consonants)
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print('UNRECOGNIZED LETTERS!')
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if split_options == []:
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return [''], consonants
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else:
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split = min(split_options, key=lambda x:x[1])
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return consonants[:split[0]+1], consonants[split[0]+1:]
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# print(consonants)
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return [''], ['']
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def create_syllables(word, vowels):
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word_list = list(word)
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consonants = []
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syllables = []
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for i in range(len(word_list)):
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if is_vowel(word_list, i, vowels):
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if syllables == []:
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consonants.append(word_list[i])
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syllables.append(''.join(consonants))
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else:
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left_consonants, right_consonants = split_consonants(consonants)
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syllables[-1] += ''.join(left_consonants)
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right_consonants.append(word_list[i])
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syllables.append(''.join(right_consonants))
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consonants = []
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else:
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consonants.append(word_list[i])
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if len(syllables) < 1:
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return word
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syllables[-1] += ''.join(consonants)
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return syllables
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def create_syllables_dictionary(content, vowels):
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dictionary = []
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for el in content:
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syllables = create_syllables(el[0], vowels)
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for syllable in syllables:
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if syllable not in dictionary:
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dictionary.append(syllable)
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dictionary.append('')
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return sorted(dictionary)
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def generate_syllable_inputs(content_shuffle_vector_location, shuffle_vector_location):
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dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
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train_content, test_content, validate_content = split_content(content, 0.2, content_shuffle_vector_location)
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feature_dictionary = create_feature_dictionary()
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print('CREATING SYLLABLE DICTIONARY...')
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syllable_dictionary = create_syllables_dictionary(content, vowels)
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print('CREATION SUCCESSFUL!')
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# Generate X and y
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print('GENERATING X AND y...')
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X_train, X_other_features_train, y_train = generate_syllable_X_and_y(syllable_dictionary, max_word, max_num_vowels, train_content, vowels, accetuated_vowels, feature_dictionary, shuffle_vector_location + '_train.h5')
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X_test, X_other_features_test, y_test = generate_syllable_X_and_y(syllable_dictionary, max_word, max_num_vowels, test_content, vowels, accetuated_vowels, feature_dictionary, shuffle_vector_location + '_test.h5')
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X_validate, X_other_features_validate, y_validate = generate_syllable_X_and_y(syllable_dictionary, max_word, max_num_vowels, validate_content, vowels, accetuated_vowels, feature_dictionary, shuffle_vector_location + '_validate.h5')
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print('GENERATION SUCCESSFUL!')
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return X_train, X_other_features_train, y_train, X_test, X_other_features_test, y_test, X_validate, X_other_features_validate, y_validate
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# Generate each y as an array of 11 numbers (with possible values between 0 and 1)
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def generate_syllable_X_and_y(dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels, feature_dictionary, shuffle_vector_location, shuffle=True):
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y = np.zeros((len(content), max_num_vowels))
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X = np.zeros((len(content), max_num_vowels), dtype=int)
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# X = []
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print('CREATING OTHER FEATURES...')
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X_other_features = create_X_features(content, feature_dictionary)
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print('OTHER FEATURES CREATED!')
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i = 0
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for el in content:
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j = 0
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syllables = create_syllables(el[0], vowels)
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# X_el = [''] * max_num_vowels
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for syllable in syllables:
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index = dictionary.index(syllable)
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X[i][j] = index
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# X[i][j][index] = 1
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j += 1
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# X.append(X_el)
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j = 0
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word_accetuations = []
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num_vowels = 0
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for c in list(el[3]):
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index = 0
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if is_vowel(el[3], j, vowels):
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num_vowels += 1
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for d in accetuated_vowels:
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if c == d:
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word_accetuations.append(num_vowels)
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break
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index += 1
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j += 1
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if len(word_accetuations) > 0:
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y_value = 1/len(word_accetuations)
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for el in word_accetuations:
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# y[i][el] = y_value
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y[i][el] = 1
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else:
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y[i][0] = 1
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# y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
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i += 1
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# print(len(X))
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# print(X[0])
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X = np.array(X)
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# print(X.shape)
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# print(X[0])
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# print(len(X))
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if shuffle:
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print('SHUFFELING INPUTS...')
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X, y, X_other_features = shuffle_inputs(X, y, shuffle_vector_location, X_pure=X_other_features)
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print('INPUTS SHUFFELED!')
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return X, X_other_features, y
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# generator for inputs for tracking of data fitting
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def generate_fake_epoch_syllables(orig_X, orig_X_additional, orig_y, batch_size, dictionary_size=5168):
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size = orig_X.shape[0]
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eye = np.eye(dictionary_size, dtype=int)
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while 1:
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loc = 0
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while loc < size:
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if loc + batch_size >= size:
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# [eye[i] for i in range(size-loc)]
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# gen_orig_X = eye[orig_X[loc:size]]
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# gen_orig_X = [eye[i] for i in range(size-loc)]
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gen_orig_X = eye[orig_X[loc:size]]
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yield([gen_orig_X, orig_X_additional[loc:size]], orig_y[loc:size])
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else:
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gen_orig_X = eye[orig_X[loc:loc + batch_size]]
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yield([gen_orig_X, orig_X_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
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loc += batch_size
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