def test_model_construct(self): # Default Model Construct cnn_model = CharacterLevelCnnModel(label_mapping=self.label_mapping) cnn_model._construct_model() # Test Details cnn_model.details() expected_layers = [ "input_1", "lambda", "embedding", "conv1d", "dropout", "batch_normalization", "conv1d_1", "dropout_1", "batch_normalization_1", "conv1d_2", "dropout_2", "batch_normalization_2", "conv1d_3", "dropout_3", "batch_normalization_3", "dense", "dropout_4", "dense_1", "dropout_5", "dense_2", "tf_op_layer_ArgMax", "thresh_arg_max_layer", ] model_layers = [layer.name for layer in cnn_model._model.layers] self.assertEqual(len(expected_layers), len(model_layers)) self.assertEqual(17, cnn_model.num_labels)
def test_model_construct(self): # Default Model Construct cnn_model = CharacterLevelCnnModel(label_mapping=self.label_mapping) cnn_model._construct_model() # Test Details cnn_model.details() expected_layers = [ 'input_1', 'lambda', 'embedding', 'conv1d', 'dropout', 'batch_normalization', 'conv1d_1', 'dropout_1', 'batch_normalization_1', 'conv1d_2', 'dropout_2', 'batch_normalization_2', 'conv1d_3', 'dropout_3', 'batch_normalization_3', 'dense', 'dropout_4', 'dense_1', 'dropout_5', 'dense_2', 'tf_op_layer_ArgMax', 'thresh_arg_max_layer' ] model_layers = [layer.name for layer in cnn_model._model.layers] self.assertEqual(len(expected_layers), len(model_layers)) self.assertEqual(17, cnn_model.num_labels)