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Testing oversampling

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Luka 2018-04-20 21:13:31 +02:00
parent 9f6e5b2752
commit 2c129a348d
5 changed files with 228 additions and 180 deletions
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.gitignore vendored
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@ -97,3 +97,4 @@ new_sloleks.xml
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@ -978,30 +983,6 @@
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@ -1010,6 +991,14 @@
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@ -1018,58 +1007,30 @@
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<entry file="file://$PROJECT_DIR$/sloleks_accentuation.py"> <entry file="file://$PROJECT_DIR$/sloleks_accentuation.py">
<provider selected="true" editor-type-id="text-editor"> <provider selected="true" editor-type-id="text-editor">
<state relative-caret-position="180"> <state relative-caret-position="2214">
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@ -1078,13 +1039,71 @@
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23
prepare_data.py
View File

@ -580,20 +580,20 @@ class Data:
# generator for inputs for tracking of data fitting # generator for inputs for tracking of data fitting
def generator(self, data_type, batch_size, x=None, x_other_features_validate=None, y_validate=None, content_name='SlovarIJS_BESEDE_utf8.lex', def generator(self, data_type, batch_size, x=None, x_other_features_validate=None, y_validate=None, content_name='SlovarIJS_BESEDE_utf8.lex',
content_location='../../../data/'): content_location='../../../data/', oversampling=np.ones(13)):
content_path = '{}{}'.format(content_location, content_name) content_path = '{}{}'.format(content_location, content_name)
if data_type == 'train': if data_type == 'train':
return self._generator_instance(self.x_train, self.x_other_features_train, self.y_train, batch_size, content_path) return self._generator_instance(self.x_train, self.x_other_features_train, self.y_train, batch_size, content_path, oversampling)
elif data_type == 'test': elif data_type == 'test':
return self._generator_instance(self.x_test, self.x_other_features_test, self.y_test, batch_size, content_path) return self._generator_instance(self.x_test, self.x_other_features_test, self.y_test, batch_size, content_path, oversampling)
elif data_type == 'validate': elif data_type == 'validate':
return self._generator_instance(self.x_validate, self.x_other_features_validate, self.y_validate, batch_size, content_path) return self._generator_instance(self.x_validate, self.x_other_features_validate, self.y_validate, batch_size, content_path, oversampling)
else: else:
return self._generator_instance(x, x_other_features_validate, y_validate, batch_size) return self._generator_instance(x, x_other_features_validate, y_validate, batch_size)
# if self._input_type # if self._input_type
def _generator_instance(self, orig_x, orig_x_additional, orig_y, batch_size, content_path): def _generator_instance(self, orig_x, orig_x_additional, orig_y, batch_size, content_path, oversampling):
if self._input_type == 'l': if self._input_type == 'l':
content = self._read_content(content_path) content = self._read_content(content_path)
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content) dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
@ -603,14 +603,14 @@ class Data:
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content) dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
syllable_dictionary = self._create_syllables_dictionary(content, vowels) syllable_dictionary = self._create_syllables_dictionary(content, vowels)
eye = np.eye(len(syllable_dictionary), dtype=int) eye = np.eye(len(syllable_dictionary), dtype=int)
return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, eye, accented_vowels) return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, eye, accented_vowels, oversampling)
elif self._input_type == 'sl': elif self._input_type == 'sl':
content = self._read_content(content_path) content = self._read_content(content_path)
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content) dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
syllable_dictionary = self._create_syllables_dictionary(content, vowels) syllable_dictionary = self._create_syllables_dictionary(content, vowels)
max_syllable = self._get_max_syllable(syllable_dictionary) max_syllable = self._get_max_syllable(syllable_dictionary)
syllable_letters_translator = self._create_syllable_letters_translator(max_syllable, syllable_dictionary, dictionary, vowels) syllable_letters_translator = self._create_syllable_letters_translator(max_syllable, syllable_dictionary, dictionary, vowels)
return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, syllable_letters_translator, accented_vowels) return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, syllable_letters_translator, accented_vowels, oversampling)
# generator for inputs for tracking of data fitting # generator for inputs for tracking of data fitting
def _letter_generator(self, orig_x, orig_x_additional, orig_y, batch_size, accented_vowels): def _letter_generator(self, orig_x, orig_x_additional, orig_y, batch_size, accented_vowels):
@ -666,7 +666,7 @@ class Data:
loc += batch_size loc += batch_size
# generator for inputs for tracking of data fitting # generator for inputs for tracking of data fitting
def _syllable_generator(self, orig_x, orig_x_additional, orig_y, batch_size, translator, accented_vowels): def _syllable_generator(self, orig_x, orig_x_additional, orig_y, batch_size, translator, accented_vowels, oversampling):
size = orig_x.shape[0] size = orig_x.shape[0]
while 1: while 1:
loc = 0 loc = 0
@ -683,9 +683,10 @@ class Data:
if accent > 0: if accent > 0:
new_orig_x_additional = orig_x_additional[loc] new_orig_x_additional = orig_x_additional[loc]
new_orig_x_additional = np.concatenate((new_orig_x_additional, eye_input_accent[accent_loc])) new_orig_x_additional = np.concatenate((new_orig_x_additional, eye_input_accent[accent_loc]))
input_x_stack.append(orig_x[loc]) for i in range(oversampling[int(accent)]):
input_x_other_features_stack.append(new_orig_x_additional) input_x_stack.append(orig_x[loc])
input_y_stack.append(eye[int(accent)]) input_x_other_features_stack.append(new_orig_x_additional)
input_y_stack.append(eye[int(accent)])
accent_loc += 1 accent_loc += 1
loc += 1 loc += 1
if len(input_x_stack) > batch_size: if len(input_x_stack) > batch_size:

6
run_multiple_files.py
View File

@ -9,6 +9,6 @@
#import cnn.accent_classification.letters.v3_0.workbench #import cnn.accent_classification.letters.v3_0.workbench
#import cnn.accent_classification.syllables.v2_0.workbench #import cnn.accent_classification.syllables.v2_0.workbench
#import cnn.accent_classification.syllabled_letters.v2_0.workbench #import cnn.accent_classification.syllabled_letters.v2_0.workbench
import cnn.accent_classification.letters.v3_1.workbench #import cnn.accent_classification.letters.v3_1.workbench
import cnn.accent_classification.syllables.v2_1.workbench import cnn.accent_classification.syllables.v2_2.workbench
import cnn.accent_classification.syllabled_letters.v2_1.workbench #import cnn.accent_classification.syllabled_letters.v2_1.workbench

31
sloleks_accetuation2.ipynb
View File

@ -201,9 +201,36 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 79,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing data from 0 onward.\n",
"Elapsed time: 46.20 minutes\n",
"Writing data from 100000 onward.\n",
"Elapsed time: 89.81 minutes\n",
"Writing data from 200000 onward.\n",
"Elapsed time: 134.45 minutes\n"
]
},
{
"ename": "IndexError",
"evalue": "index 10 is out of bounds for axis 0 with size 10",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-79-3dd0379916f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mletter_type_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllable_type_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllabled_letter_type_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mletter_type_co_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllable_type_co_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllabled_letter_type_co_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary, syllable_dictionary)\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Developement/accetuation/prepare_data.py\u001b[0m in \u001b[0;36maccentuate_word\u001b[0;34m(self, input_words, letter_location_model, syllable_location_model, syllabled_letters_location_model, letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model, letter_type_model, syllable_type_model, syllabled_letter_type_model, letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model, dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary, syllable_dictionary)\u001b[0m\n\u001b[1;32m 1635\u001b[0m \u001b[0msyllabled_letters_location_co_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1636\u001b[0m \u001b[0mdictionary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_word\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_num_vowels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccented_vowels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_dictionary\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1637\u001b[0;31m syllable_dictionary)\n\u001b[0m\u001b[1;32m 1638\u001b[0m \u001b[0;31m#print(predictions)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1639\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'A'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Developement/accetuation/prepare_data.py\u001b[0m in \u001b[0;36mget_ensemble_location_predictions\u001b[0;34m(input_words, letter_location_model, syllable_location_model, syllabled_letters_location_model, letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model, dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary, syllable_dictionary)\u001b[0m\n\u001b[1;32m 1465\u001b[0m \u001b[0mletter_location_co_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mletter_location_co_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1467\u001b[0;31m \u001b[0mletter_location_co_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mletter_location_co_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1469\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m's'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle_all_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_multext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Developement/accetuation/prepare_data.py\u001b[0m in \u001b[0;36mreverse_predictions\u001b[0;34m(self, predictions, words, vowels)\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1504\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1505\u001b[0;31m \u001b[0mnew_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword_len\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1507\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: index 10 is out of bounds for axis 0 with size 10"
]
}
],
"source": [ "source": [
"#Words proccesed: 650250\n", "#Words proccesed: 650250\n",
"#Word indeks: 50023\n", "#Word indeks: 50023\n",