def testTranslateUnifiedGRUAndLSTMClassName(self):
# The config JSON of a model consisting of a "UnifiedGRU" layer
# and a "UnifiedLSTM" layer.
# pylint: disable=line-too-long
json_object = json.loads(
'{"class_name": "Sequential", "keras_version": "2.2.4-tf", "config": {"layers": [{"class_name": "UnifiedGRU", "config": {"recurrent_activation": "sigmoid", "dtype": "float32", "trainable": true, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"seed": null, "gain": 1.0}}, "use_bias": true, "bias_regularizer": null, "return_state": false, "unroll": false, "activation": "tanh", "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4, 3], "activity_regularizer": null, "recurrent_dropout": 0.0, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "kernel_constraint": null, "time_major": false, "dropout": 0.0, "stateful": false, "reset_after": true, "recurrent_regularizer": null, "name": "unified_gru", "bias_constraint": null, "go_backwards": false, "implementation": 1, "kernel_regularizer": null, "return_sequences": true, "recurrent_constraint": null}}, {"class_name": "UnifiedLSTM", "config": {"recurrent_activation": "sigmoid", "dtype": "float32", "trainable": true, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"seed": null, "gain": 1.0}}, "use_bias": true, "bias_regularizer": null, "return_state": false, "unroll": false, "activation": "tanh", "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 2, "unit_forget_bias": true, "activity_regularizer": null, "recurrent_dropout": 0.0, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "kernel_constraint": null, "time_major": false, "dropout": 0.0, "stateful": false, "recurrent_regularizer": null, "name": "unified_lstm", "bias_constraint": null, "go_backwards": false, "implementation": 1, "kernel_regularizer": null, "return_sequences": false, "recurrent_constraint": null}}], "name": "sequential"}, "backend": "tensorflow"}'
)
# pylint: enable=line-too-long
conversion.translate_class_names(json_object)
# Some class names should not have been changed be translate_class_names().
self.assertEqual(json_object['class_name'], 'Sequential')
self.assertEqual(json_object['keras_version'], '2.2.4-tf')
# The translation should have happend:
# UnifiedGRU --> GRU.
# UnifiedLSTM --> LSTM.
self.assertEqual(json_object['config']['layers'][0]['class_name'],
'GRU')
self.assertEqual(json_object['config']['layers'][1]['class_name'],
'LSTM')
# Assert that converted JSON can be reconstituted as a model object.
model = keras.models.model_from_json(json.dumps(json_object))
self.assertIsInstance(model, keras.Sequential)
self.assertEqual(model.input_shape, (None, 4, 3))
self.assertEqual(model.output_shape, (None, 2))
def testTranslateBatchNormalizationV1ClassName(self):
# The config JSON of a model consisting of a "BatchNormalizationV1" layer.
# pylint: disable=line-too-long
json_object = json.loads(
'{"class_name": "Sequential", "keras_version": "2.2.4-tf", "config": {"layers": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "GlorotUniform", "config": {"dtype": "float32", "seed": null}}, "name": "dense", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 10, "batch_input_shape": [null, 3], "use_bias": true, "activity_regularizer": null}}, {"class_name": "BatchNormalizationV1", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {"dtype": "float32"}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "name": "batch_normalization_v1", "dtype": "float32", "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {"dtype": "float32"}}, "beta_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "scale": true, "axis": [1], "epsilon": 0.001, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "GlorotUniform", "config": {"dtype": "float32", "seed": null}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "linear", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {"dtype": "float32"}}, "units": 1, "use_bias": true, "activity_regularizer": null}}], "name": "sequential"}, "backend": "tensorflow"}'
)
# pylint: enable=line-too-long
conversion.translate_class_names(json_object)
# Some class names should not have been changed be translate_class_names().
self.assertEqual(json_object['class_name'], 'Sequential')
self.assertEqual(json_object['keras_version'], '2.2.4-tf')
self.assertEqual(json_object['config']['layers'][0]['class_name'],
'Dense')
# The translation should have happend:
# BatchNormalizationV1 --> BatchNormalization.
self.assertEqual(json_object['config']['layers'][1]['class_name'],
'BatchNormalization')
self.assertEqual(json_object['config']['layers'][2]['class_name'],
'Dense')
# Assert that converted JSON can be reconstituted as a model object.
model = keras.models.model_from_json(json.dumps(json_object))
self.assertIsInstance(model, keras.Sequential)
self.assertEqual(model.input_shape, (None, 3))
self.assertEqual(model.output_shape, (None, 1))
self.assertEqual(model.layers[0].units, 10)
self.assertEqual(model.layers[2].units, 1)