Test ready bidirectional architectural inputs
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parent
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@ -2,19 +2,10 @@
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@ -91,11 +83,11 @@
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@ -182,14 +174,6 @@
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</component>
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<component name="FindInProjectRecents">
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<findStrings>
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<find>round</find>
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<find>is_vow</find>
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<find>self._input_type == 'l'</find>
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<find>print</find>
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<find>np.eye</find>
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<find>allow_shuffle_vector_generation</find>
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<find>accented_vowels</find>
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<find>generate</find>
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<find>generate_x_and</find>
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<find>accentuate</find>
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<find>_generator</find>
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@ -206,12 +190,20 @@
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<find>convert_multext</find>
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<find>_syllable_generator</find>
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<find>generator</find>
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<find>generate_data</find>
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<find>_x</find>
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<find>bidirectional_basic_input</find>
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<find>_bidirectional_basic_input</find>
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<find>shuffeling</find>
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<find>generate_data</find>
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<find>_generate_inputs</find>
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<find>content_shuffle_vector_path</find>
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<find>content_shuffle_vector_location</find>
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<find>_shuffle_all_inputs</find>
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<find>_generator_instance</find>
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<find>_x_letter_input</find>
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<find>_generate_x_and_y</find>
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<find>content</find>
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<find>number_of_syllables</find>
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@ -232,9 +224,9 @@
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@ -948,16 +930,27 @@
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@ -22,7 +22,7 @@ from keras.models import load_model
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class Data:
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def __init__(self, input_type, allow_shuffle_vector_generation=False, save_generated_data=True, shuffle_all_inputs=True,
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additional_letter_attributes=True, reverse_inputs=True, accent_classification=False, number_of_syllables=False,
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convert_multext=True, bidirectional_basic_input=False):
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convert_multext=True, bidirectional_basic_input=False, bidirectional_architectural_input=False):
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self._input_type = input_type
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self._save_generated_data = save_generated_data
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self._allow_shuffle_vector_generation = allow_shuffle_vector_generation
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@ -33,14 +33,18 @@ class Data:
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self._number_of_syllables = number_of_syllables
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self._convert_multext = convert_multext
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self._bidirectional_basic_input = bidirectional_basic_input
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self._bidirectional_architectural_input = bidirectional_architectural_input
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self.x_train = None
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# self.x2_train = None
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self.x_other_features_train = None
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self.y_train = None
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self.x_test = None
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# self.x2_test = None
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self.x_other_features_test = None
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self.y_test = None
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self.x_validate = None
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# self.x2_validate = None
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self.x_other_features_validate = None
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self.y_validate = None
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@ -63,15 +67,11 @@ class Data:
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shuffle_vector_path = '{}{}'.format(inputs_location, shuffle_vector)
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# actual generation of inputs
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self._generate_inputs(content_path, content_shuffle_vector_path, shuffle_vector_path, test_and_validation_size)
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self._generate_inputs(content_path, content_shuffle_vector_path, shuffle_vector_path, test_and_validation_size, train_path, test_path,
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validate_path)
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# save inputs
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if self._save_generated_data:
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self._save_inputs(train_path, self.x_train, self.x_other_features_train, self.y_train)
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self._save_inputs(test_path, self.x_test, self.x_other_features_test, self.y_test)
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self._save_inputs(validate_path, self.x_validate, self.x_other_features_validate, self.y_validate)
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def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size):
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def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size, train_path,
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test_path, validate_path):
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print('READING CONTENT...')
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content = self._read_content(content_location)
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print('CONTENT READ SUCCESSFULLY')
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@ -97,6 +97,13 @@ class Data:
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accented_vowels, feature_dictionary,
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shuffle_vector_location + '_validate.h5')
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print('GENERATION SUCCESSFUL!')
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# save inputs
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if self._save_generated_data:
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self._save_inputs(train_path, self.x_train, self.x_other_features_train, self.y_train)
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self._save_inputs(test_path, self.x_test, self.x_other_features_test, self.y_test)
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self._save_inputs(validate_path, self.x_validate, self.x_other_features_validate, self.y_validate)
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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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# functions for creating X and y from content
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@ -179,7 +186,7 @@ class Data:
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h5f.close()
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return shuffle_vector
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def _x_letter_input(self, content, dictionary, max_word, vowels):
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def _x_letter_input(self, content, dictionary, max_word, vowels, shuffle_vector_location):
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if self._additional_letter_attributes:
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if not self._bidirectional_basic_input:
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x = np.zeros((len(content), max_word, len(dictionary) + 6), dtype=int)
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@ -196,9 +203,18 @@ class Data:
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else:
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x = np.zeros((len(content), 2 * max_word, len(dictionary)), dtype=int)
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i = 0
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for el in content:
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word = el[0]
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if self._shuffle_all_inputs:
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s = self._load_shuffle_vector(shuffle_vector_location, len(content))
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else:
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s = None
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# i = 0
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for i in range(len(content)):
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if self._shuffle_all_inputs:
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mod_i = s[i]
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else:
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mod_i = i
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word = content[mod_i][0]
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if self._reverse_inputs:
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word = word[::-1]
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j = 0
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@ -242,7 +258,7 @@ class Data:
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if self._bidirectional_basic_input:
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x[i][j2][len(dictionary) + 5] = 1
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j += 1
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i += 1
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#i += 1
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return x
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def _x_syllable_input(self, content, dictionary, max_num_vowels, vowels):
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@ -266,11 +282,19 @@ class Data:
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i += 1
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return x
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def _y_output(self, content, max_num_vowels, vowels, accentuated_vowels):
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def _y_output(self, content, max_num_vowels, vowels, accentuated_vowels, shuffle_vector_location):
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y = np.zeros((len(content), max_num_vowels))
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i = 0
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for el in content:
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if self._shuffle_all_inputs:
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s = self._load_shuffle_vector(shuffle_vector_location, len(content))
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else:
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s = None
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for i in range(len(content)):
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if self._shuffle_all_inputs:
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mod_i = s[i]
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else:
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mod_i = i
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el = content[mod_i]
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word = el[3]
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if self._reverse_inputs:
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word = word[::-1]
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@ -292,27 +316,26 @@ class Data:
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if self._is_vowel(word, j, vowels):
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num_vowels += 1
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j += 1
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i += 1
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return y
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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(self, dictionary, max_word, max_num_vowels, content, vowels, accentuated_vowels, feature_dictionary,
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shuffle_vector_location):
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if self._input_type == 'l':
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x = self._x_letter_input(content, dictionary, max_word, vowels)
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x = self._x_letter_input(content, dictionary, max_word, vowels, shuffle_vector_location)
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elif self._input_type == 's' or self._input_type == 'sl':
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x = self._x_syllable_input(content, dictionary, max_num_vowels, vowels)
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else:
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raise ValueError('No input_type provided. It could be \'l\', \'s\' or \'sl\'.')
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y = self._y_output(content, max_num_vowels, vowels, accentuated_vowels)
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y = self._y_output(content, max_num_vowels, vowels, accentuated_vowels, shuffle_vector_location)
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# print('CREATING OTHER FEATURES...')
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x_other_features = self._create_x_features(content, feature_dictionary, vowels)
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x_other_features = self._create_x_features(content, feature_dictionary, vowels, shuffle_vector_location)
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# print('OTHER FEATURES CREATED!')
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if self._shuffle_all_inputs:
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print('SHUFFELING INPUTS...')
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x, x_other_features, y = self._shuffle_inputs(x, x_other_features, y, shuffle_vector_location)
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#x, x_other_features, y = self._shuffle_inputs(x, x_other_features, y, shuffle_vector_location)
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print('INPUTS SHUFFELED!')
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return x, x_other_features, y
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@ -390,10 +413,19 @@ class Data:
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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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def _create_x_features(self, content, feature_dictionary, vowels):
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def _create_x_features(self, content, feature_dictionary, vowels, shuffle_vector_location):
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content = content
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x_other_features = []
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for el in content:
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if self._shuffle_all_inputs:
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s = self._load_shuffle_vector(shuffle_vector_location, len(content))
|
||||
else:
|
||||
s = None
|
||||
for index in range(len(content)):
|
||||
if self._shuffle_all_inputs:
|
||||
mod_i = s[index]
|
||||
else:
|
||||
mod_i = index
|
||||
el = content[mod_i]
|
||||
x_el_other_features = []
|
||||
if self._convert_multext:
|
||||
converted_el = ''.join(self._convert_to_multext_east_v4(list(el[2]), feature_dictionary))
|
||||
|
|
@ -587,7 +619,15 @@ class Data:
|
|||
else:
|
||||
while loc < size:
|
||||
if loc + batch_size >= size:
|
||||
if self._bidirectional_architectural_input:
|
||||
split_orig_x = np.hsplit(orig_x[loc:size], 2)
|
||||
yield ([split_orig_x[0], split_orig_x[1], orig_x_additional[loc:size]], orig_y[loc:size])
|
||||
else:
|
||||
yield ([orig_x[loc:size], orig_x_additional[loc:size]], orig_y[loc:size])
|
||||
else:
|
||||
if self._bidirectional_architectural_input:
|
||||
split_orig_x = np.hsplit(orig_x[loc:loc + batch_size], 2)
|
||||
yield ([split_orig_x[0], split_orig_x[1], orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
|
||||
else:
|
||||
yield ([orig_x[loc:loc + batch_size], orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
|
||||
loc += batch_size
|
||||
|
|
|
|||
59
workbench.py
59
workbench.py
|
|
@ -27,29 +27,16 @@ from prepare_data import *
|
|||
# save_inputs('../../internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', X_validate, y_validate, other_features = X_other_features_validate)
|
||||
# X_train, X_other_features_train, y_train = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', other_features=True)
|
||||
# X_validate, X_other_features_validate, y_validate = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', other_features=True)
|
||||
# letters
|
||||
# data = Data('l', save_generated_data=False, number_of_syllables=True)
|
||||
|
||||
# syllabled letters
|
||||
data = Data('s', save_generated_data=False, accent_classification=True)
|
||||
data.generate_data('letters_word_accetuation_train',
|
||||
'letters_word_accetuation_test',
|
||||
'letters_word_accetuation_validate', content_name='SlovarIJS_BESEDE_utf8.lex',
|
||||
data = Data('l', bidirectional_basic_input=True, bidirectional_architectural_input=True)
|
||||
data.generate_data('letters_word_accetuation_bidirectional_train',
|
||||
'letters_word_accetuation_bidirectional_test',
|
||||
'letters_word_accetuation_bidirectional_validate', content_name='SlovarIJS_BESEDE_utf8.lex',
|
||||
content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
|
||||
inputs_location='', content_location='')
|
||||
|
||||
# concatenate test and train data
|
||||
# data.x_train = np.concatenate((data.x_train, data.x_test), axis=0)
|
||||
# data.x_other_features_train = np.concatenate((data.x_other_features_train, data.x_other_features_test), axis=0)
|
||||
# data.y_train = np.concatenate((data.y_train, data.y_test), axis=0)
|
||||
|
||||
# concatenate all data
|
||||
data.x_train = np.concatenate((data.x_train, data.x_test, data.x_validate), axis=0)
|
||||
data.x_other_features_train = np.concatenate((data.x_other_features_train, data.x_other_features_test, data.x_other_features_validate), axis=0)
|
||||
data.y_train = np.concatenate((data.y_train, data.y_test, data.y_validate), axis=0)
|
||||
|
||||
num_examples = len(data.x_train) # training set size
|
||||
nn_output_dim = 13
|
||||
nn_output_dim = 10
|
||||
nn_hdim = 516
|
||||
batch_size = 16
|
||||
# actual_epoch = 1
|
||||
|
|
@ -57,32 +44,28 @@ actual_epoch = 20
|
|||
# num_fake_epoch = 2
|
||||
num_fake_epoch = 20
|
||||
|
||||
# letters
|
||||
# conv_input_shape=(23, 36)
|
||||
|
||||
# syllabled letters
|
||||
# conv_input_shape=(10, 252)
|
||||
|
||||
# syllables
|
||||
conv_input_shape=(10, 5168)
|
||||
|
||||
|
||||
# othr_input = (140, )
|
||||
othr_input = (150, )
|
||||
|
||||
conv_input_shape=(23, 36)
|
||||
othr_input = (140, )
|
||||
|
||||
conv_input = Input(shape=conv_input_shape, name='conv_input')
|
||||
# letters
|
||||
# x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
|
||||
# x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
|
||||
|
||||
# syllabled letters
|
||||
x_conv = Conv1D(200, (2), padding='same', activation='relu')(conv_input)
|
||||
x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
|
||||
x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
|
||||
x_conv = MaxPooling1D(pool_size=2)(x_conv)
|
||||
x_conv = Flatten()(x_conv)
|
||||
|
||||
conv_input2 = Input(shape=conv_input_shape, name='conv_input2')
|
||||
x_conv2 = Conv1D(115, (3), padding='same', activation='relu')(conv_input2)
|
||||
x_conv2 = Conv1D(46, (3), padding='same', activation='relu')(x_conv2)
|
||||
x_conv2 = MaxPooling1D(pool_size=2)(x_conv2)
|
||||
x_conv2 = Flatten()(x_conv2)
|
||||
# x_conv = Dense(516, activation='relu', kernel_constraint=maxnorm(3))(x_conv)
|
||||
|
||||
othr_input = Input(shape=othr_input, name='othr_input')
|
||||
|
||||
x = concatenate([x_conv, othr_input])
|
||||
x = concatenate([x_conv, x_conv2, othr_input])
|
||||
# x = Dense(1024, input_dim=(516 + 256), activation='relu')(x)
|
||||
x = Dense(256, activation='relu')(x)
|
||||
x = Dropout(0.3)(x)
|
||||
|
|
@ -95,7 +78,7 @@ x = Dense(nn_output_dim, activation='sigmoid')(x)
|
|||
|
||||
|
||||
|
||||
model = Model(inputs=[conv_input, othr_input], outputs=x)
|
||||
model = Model(inputs=[conv_input, conv_input2, othr_input], outputs=x)
|
||||
opt = optimizers.Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
|
||||
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[actual_accuracy,])
|
||||
# model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
|
||||
|
|
@ -104,10 +87,12 @@ model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[actual_accurac
|
|||
history = model.fit_generator(data.generator('train', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location=''),
|
||||
data.x_train.shape[0]/(batch_size * num_fake_epoch),
|
||||
epochs=actual_epoch*num_fake_epoch,
|
||||
validation_data=data.generator('test', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location=''),
|
||||
validation_steps=data.x_test.shape[0]/(batch_size * num_fake_epoch),
|
||||
verbose=2
|
||||
)
|
||||
|
||||
name = '40_epoch'
|
||||
name = '20_epoch'
|
||||
model.save(name + '.h5')
|
||||
output = open(name + '_history.pkl', 'wb')
|
||||
pickle.dump(history.history, output)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user