[-1] + _input_shape) _test_labels = to_categorical(test_pairs['label'].tolist()) # Build model _keras_model = VggishModel(class_weights=_class_weights, input_shape=_input_shape, num_classes=_num_classes, batch_size=64, learning_rate=0.0001, metric_baseline=0.5, num_epochs=30, load_pretrained=True, feature_type='raw', output_dir='outputs') # Build container. _container = DataContainer(data=_train_pairs, labeled_percent=0.1, num_classes=_num_classes, feature_shape=_keras_model.input_shape, label_shape=_keras_model.output_shape) print(_keras_model.get_model_summary()) run_entropy(data_container=_container, model=_keras_model, test_features=_test_features, test_lablels=_test_labels, stats_path='outputs/stats/al_stats.pd', max_round=60)
if flags.framework_type == 'entropy' or \ flags.framework_type == 'edpc' or \ flags.framework_type == 'hist_select': _embeddings = _keras_model.embedding( np.reshape(_pairs['feature'].tolist(), [-1] + _input_shape)) else: _embeddings = None # Reset weights params. _keras_model.reload_weights() # Build container. _container = DataContainer(data=_train_pairs, labeled_percent=flags.labeled_percent, num_classes=_num_classes, feature_shape=_keras_model.input_shape, label_shape=_keras_model.output_shape, embeddings=_embeddings) print(_keras_model.get_model_summary()) # Create framework object. framework = framework_creator( data_container=_container, model=_keras_model, test_features=_test_features, test_labels=_test_labels, stats_path='%s/al_stats_temp.csv' % cur_work_space, max_round=flags.max_round, num_select_per_round=flags.num_select_per_round, pre_train=flags.pre_train,