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Test ready bidirectional architectural inputs

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lkrsnik 2018-03-27 10:35:14 +02:00
parent 9edad0ad07
commit 94ce159d44
3 changed files with 131 additions and 113 deletions
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93
.idea/workspace.xml
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@ -2,19 +2,10 @@
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@ -91,11 +83,11 @@
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<entry file="file://$PROJECT_DIR$/workbench.py"> <entry file="file://$PROJECT_DIR$/workbench.py">
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@ -182,14 +174,6 @@
</component> </component>
<component name="FindInProjectRecents"> <component name="FindInProjectRecents">
<findStrings> <findStrings>
<find>round</find>
<find>is_vow</find>
<find>self._input_type == 'l'</find>
<find>print</find>
<find>np.eye</find>
<find>allow_shuffle_vector_generation</find>
<find>accented_vowels</find>
<find>generate</find>
<find>generate_x_and</find> <find>generate_x_and</find>
<find>accentuate</find> <find>accentuate</find>
<find>_generator</find> <find>_generator</find>
@ -206,12 +190,20 @@
<find>convert_multext</find> <find>convert_multext</find>
<find>_syllable_generator</find> <find>_syllable_generator</find>
<find>generator</find> <find>generator</find>
<find>generate_data</find>
<find>_x</find> <find>_x</find>
<find>bidirectional_basic_input</find> <find>bidirectional_basic_input</find>
<find>_bidirectional_basic_input</find> <find>_bidirectional_basic_input</find>
<find>shuffeling</find> <find>shuffeling</find>
<find>generate_data</find>
<find>_generate_inputs</find>
<find>content_shuffle_vector_path</find>
<find>content_shuffle_vector_location</find>
<find>_shuffle_all_inputs</find>
<find>_generator_instance</find>
<find>_x_letter_input</find>
<find>_generate_x_and_y</find> <find>_generate_x_and_y</find>
<find>content</find>
<find>number_of_syllables</find>
</findStrings> </findStrings>
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@ -232,9 +224,9 @@
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@ -948,16 +930,27 @@
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92
prepare_data.py
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@ -22,7 +22,7 @@ from keras.models import load_model
class Data: class Data:
def __init__(self, input_type, allow_shuffle_vector_generation=False, save_generated_data=True, shuffle_all_inputs=True, def __init__(self, input_type, allow_shuffle_vector_generation=False, save_generated_data=True, shuffle_all_inputs=True,
additional_letter_attributes=True, reverse_inputs=True, accent_classification=False, number_of_syllables=False, additional_letter_attributes=True, reverse_inputs=True, accent_classification=False, number_of_syllables=False,
convert_multext=True, bidirectional_basic_input=False): convert_multext=True, bidirectional_basic_input=False, bidirectional_architectural_input=False):
self._input_type = input_type self._input_type = input_type
self._save_generated_data = save_generated_data self._save_generated_data = save_generated_data
self._allow_shuffle_vector_generation = allow_shuffle_vector_generation self._allow_shuffle_vector_generation = allow_shuffle_vector_generation
@ -33,14 +33,18 @@ class Data:
self._number_of_syllables = number_of_syllables self._number_of_syllables = number_of_syllables
self._convert_multext = convert_multext self._convert_multext = convert_multext
self._bidirectional_basic_input = bidirectional_basic_input self._bidirectional_basic_input = bidirectional_basic_input
self._bidirectional_architectural_input = bidirectional_architectural_input
self.x_train = None self.x_train = None
# self.x2_train = None
self.x_other_features_train = None self.x_other_features_train = None
self.y_train = None self.y_train = None
self.x_test = None self.x_test = None
# self.x2_test = None
self.x_other_features_test = None self.x_other_features_test = None
self.y_test = None self.y_test = None
self.x_validate = None self.x_validate = None
# self.x2_validate = None
self.x_other_features_validate = None self.x_other_features_validate = None
self.y_validate = None self.y_validate = None
@ -63,15 +67,11 @@ class Data:
shuffle_vector_path = '{}{}'.format(inputs_location, shuffle_vector) shuffle_vector_path = '{}{}'.format(inputs_location, shuffle_vector)
# actual generation of inputs # actual generation of inputs
self._generate_inputs(content_path, content_shuffle_vector_path, shuffle_vector_path, test_and_validation_size) self._generate_inputs(content_path, content_shuffle_vector_path, shuffle_vector_path, test_and_validation_size, train_path, test_path,
validate_path)
# save inputs def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size, train_path,
if self._save_generated_data: test_path, validate_path):
self._save_inputs(train_path, self.x_train, self.x_other_features_train, self.y_train)
self._save_inputs(test_path, self.x_test, self.x_other_features_test, self.y_test)
self._save_inputs(validate_path, self.x_validate, self.x_other_features_validate, self.y_validate)
def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size):
print('READING CONTENT...') print('READING CONTENT...')
content = self._read_content(content_location) content = self._read_content(content_location)
print('CONTENT READ SUCCESSFULLY') print('CONTENT READ SUCCESSFULLY')
@ -97,6 +97,13 @@ class Data:
accented_vowels, feature_dictionary, accented_vowels, feature_dictionary,
shuffle_vector_location + '_validate.h5') shuffle_vector_location + '_validate.h5')
print('GENERATION SUCCESSFUL!') print('GENERATION SUCCESSFUL!')
# save inputs
if self._save_generated_data:
self._save_inputs(train_path, self.x_train, self.x_other_features_train, self.y_train)
self._save_inputs(test_path, self.x_test, self.x_other_features_test, self.y_test)
self._save_inputs(validate_path, self.x_validate, self.x_other_features_validate, self.y_validate)
# 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 # 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
# functions for creating X and y from content # functions for creating X and y from content
@ -179,7 +186,7 @@ class Data:
h5f.close() h5f.close()
return shuffle_vector return shuffle_vector
def _x_letter_input(self, content, dictionary, max_word, vowels): def _x_letter_input(self, content, dictionary, max_word, vowels, shuffle_vector_location):
if self._additional_letter_attributes: if self._additional_letter_attributes:
if not self._bidirectional_basic_input: if not self._bidirectional_basic_input:
x = np.zeros((len(content), max_word, len(dictionary) + 6), dtype=int) x = np.zeros((len(content), max_word, len(dictionary) + 6), dtype=int)
@ -196,9 +203,18 @@ class Data:
else: else:
x = np.zeros((len(content), 2 * max_word, len(dictionary)), dtype=int) x = np.zeros((len(content), 2 * max_word, len(dictionary)), dtype=int)
i = 0 if self._shuffle_all_inputs:
for el in content: s = self._load_shuffle_vector(shuffle_vector_location, len(content))
word = el[0] else:
s = None
# i = 0
for i in range(len(content)):
if self._shuffle_all_inputs:
mod_i = s[i]
else:
mod_i = i
word = content[mod_i][0]
if self._reverse_inputs: if self._reverse_inputs:
word = word[::-1] word = word[::-1]
j = 0 j = 0
@ -242,7 +258,7 @@ class Data:
if self._bidirectional_basic_input: if self._bidirectional_basic_input:
x[i][j2][len(dictionary) + 5] = 1 x[i][j2][len(dictionary) + 5] = 1
j += 1 j += 1
i += 1 #i += 1
return x return x
def _x_syllable_input(self, content, dictionary, max_num_vowels, vowels): def _x_syllable_input(self, content, dictionary, max_num_vowels, vowels):
@ -266,11 +282,19 @@ class Data:
i += 1 i += 1
return x return x
def _y_output(self, content, max_num_vowels, vowels, accentuated_vowels): def _y_output(self, content, max_num_vowels, vowels, accentuated_vowels, shuffle_vector_location):
y = np.zeros((len(content), max_num_vowels)) y = np.zeros((len(content), max_num_vowels))
i = 0 i = 0
if self._shuffle_all_inputs:
for el in content: s = self._load_shuffle_vector(shuffle_vector_location, len(content))
else:
s = None
for i in range(len(content)):
if self._shuffle_all_inputs:
mod_i = s[i]
else:
mod_i = i
el = content[mod_i]
word = el[3] word = el[3]
if self._reverse_inputs: if self._reverse_inputs:
word = word[::-1] word = word[::-1]
@ -292,27 +316,26 @@ class Data:
if self._is_vowel(word, j, vowels): if self._is_vowel(word, j, vowels):
num_vowels += 1 num_vowels += 1
j += 1 j += 1
i += 1
return y return y
# Generate each y as an array of 11 numbers (with possible values between 0 and 1) # Generate each y as an array of 11 numbers (with possible values between 0 and 1)
def _generate_x_and_y(self, dictionary, max_word, max_num_vowels, content, vowels, accentuated_vowels, feature_dictionary, def _generate_x_and_y(self, dictionary, max_word, max_num_vowels, content, vowels, accentuated_vowels, feature_dictionary,
shuffle_vector_location): shuffle_vector_location):
if self._input_type == 'l': if self._input_type == 'l':
x = self._x_letter_input(content, dictionary, max_word, vowels) x = self._x_letter_input(content, dictionary, max_word, vowels, shuffle_vector_location)
elif self._input_type == 's' or self._input_type == 'sl': elif self._input_type == 's' or self._input_type == 'sl':
x = self._x_syllable_input(content, dictionary, max_num_vowels, vowels) x = self._x_syllable_input(content, dictionary, max_num_vowels, vowels)
else: else:
raise ValueError('No input_type provided. It could be \'l\', \'s\' or \'sl\'.') raise ValueError('No input_type provided. It could be \'l\', \'s\' or \'sl\'.')
y = self._y_output(content, max_num_vowels, vowels, accentuated_vowels) y = self._y_output(content, max_num_vowels, vowels, accentuated_vowels, shuffle_vector_location)
# print('CREATING OTHER FEATURES...') # print('CREATING OTHER FEATURES...')
x_other_features = self._create_x_features(content, feature_dictionary, vowels) x_other_features = self._create_x_features(content, feature_dictionary, vowels, shuffle_vector_location)
# print('OTHER FEATURES CREATED!') # print('OTHER FEATURES CREATED!')
if self._shuffle_all_inputs: if self._shuffle_all_inputs:
print('SHUFFELING INPUTS...') print('SHUFFELING INPUTS...')
x, x_other_features, y = self._shuffle_inputs(x, x_other_features, y, shuffle_vector_location) #x, x_other_features, y = self._shuffle_inputs(x, x_other_features, y, shuffle_vector_location)
print('INPUTS SHUFFELED!') print('INPUTS SHUFFELED!')
return x, x_other_features, y return x, x_other_features, y
@ -390,10 +413,19 @@ class Data:
split = min(split_options, key=lambda x: x[1]) split = min(split_options, key=lambda x: x[1])
return consonants[:split[0] + 1], consonants[split[0] + 1:] return consonants[:split[0] + 1], consonants[split[0] + 1:]
def _create_x_features(self, content, feature_dictionary, vowels): def _create_x_features(self, content, feature_dictionary, vowels, shuffle_vector_location):
content = content content = content
x_other_features = [] x_other_features = []
for el in content: if self._shuffle_all_inputs:
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 = [] x_el_other_features = []
if self._convert_multext: if self._convert_multext:
converted_el = ''.join(self._convert_to_multext_east_v4(list(el[2]), feature_dictionary)) converted_el = ''.join(self._convert_to_multext_east_v4(list(el[2]), feature_dictionary))
@ -587,9 +619,17 @@ class Data:
else: else:
while loc < size: while loc < size:
if loc + batch_size >= size: if loc + batch_size >= size:
yield ([orig_x[loc:size], orig_x_additional[loc:size]], orig_y[loc: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: else:
yield ([orig_x[loc:loc + batch_size], orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size]) 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 loc += batch_size
# generator for inputs for tracking of data fitting # generator for inputs for tracking of data fitting

59
workbench.py
View File

@ -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) # 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_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) # 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', bidirectional_basic_input=True, bidirectional_architectural_input=True)
# data = Data('l', save_generated_data=False, number_of_syllables=True) data.generate_data('letters_word_accetuation_bidirectional_train',
'letters_word_accetuation_bidirectional_test',
# syllabled letters 'letters_word_accetuation_bidirectional_validate', content_name='SlovarIJS_BESEDE_utf8.lex',
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',
content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector', content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
inputs_location='', content_location='') 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 num_examples = len(data.x_train) # training set size
nn_output_dim = 13 nn_output_dim = 10
nn_hdim = 516 nn_hdim = 516
batch_size = 16 batch_size = 16
# actual_epoch = 1 # actual_epoch = 1
@ -57,32 +44,28 @@ actual_epoch = 20
# num_fake_epoch = 2 # num_fake_epoch = 2
num_fake_epoch = 20 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') 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(115, (3), padding='same', activation='relu')(conv_input) x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
# 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 = MaxPooling1D(pool_size=2)(x_conv) x_conv = MaxPooling1D(pool_size=2)(x_conv)
x_conv = Flatten()(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') 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(1024, input_dim=(516 + 256), activation='relu')(x)
x = Dense(256, activation='relu')(x) x = Dense(256, activation='relu')(x)
x = Dropout(0.3)(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) 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='binary_crossentropy', optimizer=opt, metrics=[actual_accuracy,])
# model.compile(loss='mean_squared_error', optimizer='adam', metrics=['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=''), 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), data.x_train.shape[0]/(batch_size * num_fake_epoch),
epochs=actual_epoch*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 verbose=2
) )
name = '40_epoch' name = '20_epoch'
model.save(name + '.h5') model.save(name + '.h5')
output = open(name + '_history.pkl', 'wb') output = open(name + '_history.pkl', 'wb')
pickle.dump(history.history, output) pickle.dump(history.history, output)