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Added some predictions and some learners

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Luka 2018-04-12 10:52:12 +02:00
parent 7ca1fd7c23
commit caa463c8d2
3 changed files with 213 additions and 207 deletions
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.gitignore vendored
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@ -20,6 +20,7 @@ lib64/
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@ -883,7 +875,7 @@
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@ -893,26 +885,18 @@
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@ -921,7 +905,6 @@
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@ -929,7 +912,6 @@
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<folding />
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@ -945,7 +927,7 @@
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<entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/v5_1/workbench.py"> <entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/v5_1/workbench.py">
<provider selected="true" editor-type-id="text-editor"> <provider selected="true" editor-type-id="text-editor">
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@ -953,79 +935,30 @@
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<entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/v5_0/workbench.py"> <entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/v5_0/workbench.py">
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<entry file="file://$PROJECT_DIR$/cnn/accent_classification/syllables/v2_0/workbench.py"> <entry file="file://$PROJECT_DIR$/cnn/accent_classification/syllables/v2_0/workbench.py">
<provider selected="true" editor-type-id="text-editor"> <provider selected="true" editor-type-id="text-editor">
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@ -1033,7 +966,7 @@
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24
prepare_data.py
View File

@ -51,7 +51,8 @@ class Data:
def generate_data(self, train_inputs_name, test_inputs_name, validate_inputs_name, test_and_validation_size=0.1, def generate_data(self, train_inputs_name, test_inputs_name, validate_inputs_name, test_and_validation_size=0.1,
force_override=False, content_name='SlovarIJS_BESEDE_utf8.lex', force_override=False, content_name='SlovarIJS_BESEDE_utf8.lex',
content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector', content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
inputs_location='../../internal_representations/inputs/', content_location='../../../data/', test_set=False): inputs_location='../../internal_representations/inputs/', content_location='../../../data/',
test_set=False, complete_set=False):
content_path = '{}{}'.format(content_location, content_name) content_path = '{}{}'.format(content_location, content_name)
train_path = '{}{}.h5'.format(inputs_location, train_inputs_name) train_path = '{}{}.h5'.format(inputs_location, train_inputs_name)
test_path = '{}{}.h5'.format(inputs_location, test_inputs_name) test_path = '{}{}.h5'.format(inputs_location, test_inputs_name)
@ -78,6 +79,16 @@ class Data:
self.x_other_features_test = self.x_other_features_validate self.x_other_features_test = self.x_other_features_validate
self.y_test = self.y_validate self.y_test = self.y_validate
if complete_set:
self.x_train = np.concatenate((self.x_train, self.x_test, self.x_validate), axis=0)
self.x_other_features_train = np.concatenate((self.x_other_features_train, self.x_other_features_test, self.x_other_features_validate),
axis=0)
self.y_train = np.concatenate((self.y_train, self.y_test, self.y_validate), axis=0)
self.x_test = self.x_validate
self.x_other_features_test = self.x_other_features_validate
self.y_test = self.y_validate
def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size, train_path, def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size, train_path,
test_path, validate_path): test_path, validate_path):
print('READING CONTENT...') print('READING CONTENT...')
@ -956,12 +967,19 @@ class Data:
i += 1 i += 1
return res return res
def test_accuracy(self, predictions, x, x_other_features, y, dictionary, feature_dictionary, vowels, syllable_dictionary=None): def test_accuracy(self, predictions, x, x_other_features, y, dictionary, feature_dictionary, vowels, syllable_dictionary=None,
threshold=0.4999955):
errors = [] errors = []
num_of_pred = len(predictions) num_of_pred = len(predictions)
num_of_correct_pred = 0 num_of_correct_pred = 0
for i in range(predictions.shape[0]): for i in range(predictions.shape[0]):
if (np.around(predictions[i]) == y[i]).all(): correct_prediction = True
for j in range(len(y[i])):
if (predictions[i][j] < threshold and y[i][j] == 1.0) or (predictions[i][j] >= threshold and y[i][j] == 0.0):
correct_prediction = False
break
# if (np.around(predictions[i]) == y[i]).all():
if correct_prediction:
num_of_correct_pred += 1 num_of_correct_pred += 1
else: else:
if self._input_type == 'l': if self._input_type == 'l':