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)