def _measure_disentanglement(self, autoencoder):
beta = autoencoder.get_beta()
train_classifier_inputs, train_classifier_labels = \
Evaluator.inputs_and_labels(autoencoder, self.classifier_train_json)
logger.info('Beta = {0} | Classifier training data processed.'.format(beta))
test_classifier_inputs, test_classifier_labels = \
Evaluator.inputs_and_labels(autoencoder, self.classifier_test_json)
logger.info('Beta = {0} | Classifier test data processed.'.format(beta))
valid_classifier_inputs, valid_classifier_labels = \
Evaluator.inputs_and_labels(autoencoder, self.classifier_validation_json)
logger.info('Beta = {0} | Classifier validation data processed.'.format(beta))
autoencoder.close_session()
# Constants.
CLASSIFIER_INPUT_DIMENSION = autoencoder.get_code_dimension()
logger.info('Beta = {0} | Create classifier.'.format(beta))
classifier = LinearClassifier(CLASSIFIER_INPUT_DIMENSION, CLASSIFIER_POSSIBLE_CLASSES_COUNT)
epoch = 0
best_validation_score = np.inf
consecutive_decreases = 0
LEN_TRAIN = len(train_classifier_inputs)
while True:
# Train with mini-batches.
random_permutation = np.random.permutation(np.arange(LEN_TRAIN))
shuffled_train_inputs = train_classifier_inputs[random_permutation]
shuffled_train_labels = train_classifier_labels[random_permutation]
classifier.partial_fit(shuffled_train_inputs, shuffled_train_labels)
# for start in range(0, LEN_TRAIN, BATCH_SIZE):
# end = LEN_TRAIN if LEN_TRAIN - start < 2 * BATCH_SIZE else start + BATCH_SIZE
# classifier.partial_fit(shuffled_train_inputs[start:end],
# shuffled_train_labels[start:end])
# if end == LEN_TRAIN:
# break
# Early stopping.
validation_score = classifier.get_cost(valid_classifier_inputs, valid_classifier_labels)
logger.info('Beta = {0} | Classifier epoch {1} validation cost: {2}'.format(
beta, epoch, validation_score))
if best_validation_score > validation_score:
logger.info('Beta = {0} | *** OPTIMAL SO FAR ***'.format(beta))
best_validation_score = validation_score
consecutive_decreases = 0
else:
consecutive_decreases += 1
if consecutive_decreases > PATIENCE:
break
epoch += 1
logger.info('Beta = {0} | Classifier training completed.'.format(beta))
accuracy = classifier.accuracy(test_classifier_inputs, test_classifier_labels)
logger.info('Beta = {0} | Classifier accuracy: {1}'.format(beta, accuracy))
with open(self.accuracy_output_file, 'w') as f:
f.write('Beta = {0} | Classifier accuracy: {1}\n'.format(beta, accuracy))
self.classifier_accuracy_all.append((beta, accuracy))
classifier.close_session()
