def testWeightsOnly(self):
input_tensor = keras.layers.Input((3, ))
dense1 = keras.layers.Dense(4,
use_bias=True,
kernel_initializer='ones',
bias_initializer='zeros',
name='MyDense1')(input_tensor)
output = keras.layers.Dense(2,
use_bias=False,
kernel_initializer='ones',
name='MyDense2')(dense1)
model = keras.models.Model(inputs=[input_tensor], outputs=[output])
h5_path = os.path.join(self._tmp_dir, 'MyModel.h5')
model.save_weights(h5_path)
# Load the saved weights as a JSON string.
model_json, groups = converter.dispatch_pykeras_conversion(
h5_path, output_dir=self._tmp_dir)
self.assertIsNone(model_json)
# Check the loaded weights.
weights1 = groups[0]
self.assertEqual(2, len(weights1))
# contents of weights are verified in tests of the library code
# Check the content of the output directory.
output_json = json.load(
open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
self.assertEqual(model_json, output_json['modelTopology'])
self.assertIsInstance(output_json['weightsManifest'], list)
self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
def testConvertWeightsFromSequentialModel(self):
sequential_model = keras.models.Sequential([
keras.layers.Dense(3,
input_shape=(2, ),
use_bias=True,
kernel_initializer='ones',
name='Dense1'),
keras.layers.Dense(1,
use_bias=False,
kernel_initializer='ones',
name='Dense2')
])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save_weights(h5_path)
# Load the saved weights as a JSON string.
model_json, groups = converter.dispatch_pykeras_conversion(
h5_path, output_dir=self._tmp_dir)
self.assertIsNone(model_json)
# Check the loaded weights.
weights1 = groups[0]
self.assertEqual(2, len(weights1))
# contents of weights are verified in tests of the library code
# Check the content of the output directory.
output_json = json.load(
open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
self.assertEqual(model_json, output_json['modelTopology'])
self.assertIsInstance(output_json['weightsManifest'], list)
self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
def testOutpuDirAsAnExistingFileLeadsToValueError(self):
output_path = os.path.join(self._tmp_dir, 'foo_model')
with open(output_path, 'wt') as f:
f.write('\n')
sequential_model = keras.models.Sequential([
keras.layers.Dense(
3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
name='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save_weights(h5_path)
with self.assertRaisesRegexp( # pylint: disable=deprecated-method
ValueError, r'already exists as a file'):
converter.dispatch_pykeras_conversion(
h5_path, output_dir=output_path)
def testConvertSavedModel(self):
input_tensor = keras.layers.Input((3,))
dense1 = keras.layers.Dense(
4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros',
name='MergedDense1')(input_tensor)
output = keras.layers.Dense(
2, use_bias=False,
kernel_initializer='ones', name='MergedDense2')(dense1)
model = keras.models.Model(inputs=[input_tensor], outputs=[output])
h5_path = os.path.join(self._tmp_dir, 'MyModelMerged.h5')
model.save(h5_path)
# Load the saved weights as a JSON string.
model_json, groups = converter.dispatch_pykeras_conversion(
h5_path, output_dir=self._tmp_dir)
# check the model topology was stored
self.assertIsInstance(model_json['model_config'], dict)
self.assertIsInstance(model_json['model_config']['config'], dict)
self.assertIn('layers', model_json['model_config']['config'])
# Check the loaded weights.
self.assertEqual(keras.__version__, model_json['keras_version'])
self.assertEqual('tensorflow', model_json['backend'])
weights1 = groups[0]
self.assertEqual(2, len(weights1))
# contents of weights are verified in tests of the library code
# Check the content of the output directory.
output_json = json.load(
open(os.path.join(self._tmp_dir, 'model.json'), 'rt'))
self.assertEqual(model_json, output_json['modelTopology'])
self.assertIsInstance(output_json['weightsManifest'], list)
self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*')))
def testConvertModelForNonexistentDirCreatesDir(self):
output_dir = os.path.join(self._tmp_dir, 'foo_model')
sequential_model = keras.models.Sequential([
keras.layers.Dense(
3, input_shape=(2,), use_bias=True, kernel_initializer='ones',
name='Dense1')])
h5_path = os.path.join(self._tmp_dir, 'SequentialModel.h5')
sequential_model.save_weights(h5_path)
converter.dispatch_pykeras_conversion(
h5_path, output_dir=output_dir)
# Check the content of the output directory.
output_json = json.load(
open(os.path.join(output_dir, 'model.json'), 'rt'))
self.assertIsNone(output_json['modelTopology'])
self.assertIsInstance(output_json['weightsManifest'], list)
self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*')))