def train_test(features_vector: FeatureVector, classifier: Classifier,
data: ClassifierData):
# Build train features vector
with Timer('building train features', VERBOSE):
x_train_features = features_vector.convert_to_features(
data.x_train, VERBOSE)
# Train
with Timer('training', VERBOSE):
classifier.train(x_train_features, data.y_train)
# Build test features vector
with Timer('building test features', VERBOSE):
x_test_features = features_vector.convert_to_features(
data.x_test, VERBOSE)
# Test
if VERBOSE:
print(features_vector.name)
print(classifier.report(x_test_features, data.y_test))
return classifier.f1_micro(x_test_features, data.y_test)
# anlz = Analyzer(tr_path,
# te_path,
# cfg['data']['classes_list'],
# cfg['analysis']['figures_path'])
# anlz.run()
##############################################################
# CNN MODEL #
##############################################################
train_path = os.path.join(cfg['data']['sorted_path'], 'training')
test_path = os.path.join(cfg['data']['sorted_path'], 'test')
fig_full_path = os.path.abspath(cfg['model']['figures_path'])
model_full_path = os.path.abspath(cfg['model']['models_path'])
weights_full_path = os.path.abspath(cfg['model']['weights_path'])
training_size = 18966
test_size = 4742
cnn = Classifier(train_path,
test_path,
training_size,
test_size,
cfg['data']['classes_list'],
cfg['model'],
fig_path=fig_full_path)
cnn.compile()
cnn.plot_model()
cnn.train()
cnn.show_training_history()
cnn.plot_confusion_matrix((4742 // 32 + 1))
cnn.save_model(model_full_path)