def test_saving_with_tf_optimizer(self):
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3, )))
model.add(keras.layers.Dense(3))
model.compile(loss='mse',
optimizer=training_module.RMSPropOptimizer(0.1),
metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
ref_y = model.predict(x)
temp_saved_model = self._save_model_dir()
output_path = keras_saved_model.export(model, temp_saved_model)
loaded_model = keras_saved_model.load_from_saved_model(output_path)
loaded_model.compile(
loss='mse',
optimizer=training_module.RMSPropOptimizer(0.1),
metrics=['acc'])
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 3))
y = np.random.random((1, 3))
ref_loss = model.train_on_batch(x, y)
loss = loaded_model.train_on_batch(x, y)
self.assertAllClose(ref_loss, loss, atol=1e-05)
ref_y = model.predict(x)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
# test saving/loading again
temp_saved_model2 = self._save_model_dir('saved_model_2')
output_path2 = keras_saved_model.export(loaded_model,
temp_saved_model2)
loaded_model = keras_saved_model.load_from_saved_model(
output_path2)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_with_tf_optimizer(self):
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.Dense(3))
model.compile(
loss='mse',
optimizer=training_module.RMSPropOptimizer(0.1),
metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
ref_y = model.predict(x)
saved_model_dir = self._save_model_dir()
keras_saved_model.export_saved_model(model, saved_model_dir)
loaded_model = keras_saved_model.load_from_saved_model(saved_model_dir)
loaded_model.compile(
loss='mse',
optimizer=training_module.RMSPropOptimizer(0.1),
metrics=['acc'])
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 3))
y = np.random.random((1, 3))
ref_loss = model.train_on_batch(x, y)
loss = loaded_model.train_on_batch(x, y)
self.assertAllClose(ref_loss, loss, atol=1e-05)
ref_y = model.predict(x)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
# test saving/loading again
saved_model_dir2 = self._save_model_dir('saved_model_2')
keras_saved_model.export_saved_model(loaded_model, saved_model_dir2)
loaded_model = keras_saved_model.load_from_saved_model(saved_model_dir2)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def _save_restore_saved_model(model):
tmpdir = tempfile.mkdtemp()
saved_model.export_saved_model(model, tmpdir)
with prune.prune_scope():
loaded_model = saved_model.load_from_saved_model(tmpdir)
loaded_model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
return loaded_model
def test_saving_sequential_model_without_compile(self):
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
x = np.random.random((1, 3))
ref_y = model.predict(x)
saved_model_dir = self._save_model_dir()
keras_saved_model.export_saved_model(model, saved_model_dir)
loaded_model = keras_saved_model.load_from_saved_model(saved_model_dir)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_sequential_model_without_compile(self):
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3, )))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
x = np.random.random((1, 3))
ref_y = model.predict(x)
temp_saved_model = self._save_model_dir()
output_path = keras_saved_model.export(model, temp_saved_model)
loaded_model = keras_saved_model.load_from_saved_model(output_path)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_functional_model_without_compile(self):
with self.cached_session():
inputs = keras.layers.Input(shape=(3,))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
x = np.random.random((1, 3))
y = np.random.random((1, 3))
ref_y = model.predict(x)
saved_model_dir = self._save_model_dir()
keras_saved_model.export_saved_model(model, saved_model_dir)
loaded_model = keras_saved_model.load_from_saved_model(saved_model_dir)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_functional_model_without_compile(self):
with self.cached_session():
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
x = np.random.random((1, 3))
y = np.random.random((1, 3))
ref_y = model.predict(x)
temp_saved_model = self._save_model_dir()
output_path = keras_saved_model.export(model, temp_saved_model)
loaded_model = keras_saved_model.load_from_saved_model(output_path)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_functional_model(self):
with self.cached_session():
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
model.compile(loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
ref_y = model.predict(x)
temp_saved_model = self._save_model_dir()
output_path = keras_saved_model.export(model, temp_saved_model)
loaded_model = keras_saved_model.load_from_saved_model(output_path)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_sequential_model(self):
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3, )))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
ref_y = model.predict(x)
temp_saved_model = self._save_model_dir()
output_path = keras_saved_model.export(model, temp_saved_model)
loaded_model = keras_saved_model.load_from_saved_model(output_path)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin
"""Loads a model saved via `save_model`.
Arguments:
filepath: One of the following:
- String, path to the saved model
- `h5py.File` object from which to load the model
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
compile: Boolean, whether to compile the model
after loading.
Returns:
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed. When `compile` is set
to False, the compilation is omitted without any
warning.
Raises:
ImportError: if loading from an hdf5 file and h5py is not available.
IOError: In case of an invalid savefile.
"""
if not tf2.enabled() or (h5py is not None and
(isinstance(filepath, h5py.File)
or h5py.is_hdf5(filepath))):
return hdf5_format.load_model_from_hdf5(filepath, custom_objects,
compile)
if isinstance(filepath, six.string_types):
loader_impl.parse_saved_model(filepath)
return saved_model.load_from_saved_model(filepath)
raise IOError(
'Unable to load model. Filepath is not an hdf5 file (or h5py is not '
'available) or SavedModel.')
def test_saving_functional_model(self):
with self.cached_session():
inputs = keras.layers.Input(shape=(3,))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
model.compile(
loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
ref_y = model.predict(x)
saved_model_dir = self._save_model_dir()
keras_saved_model.export_saved_model(model, saved_model_dir)
loaded_model = keras_saved_model.load_from_saved_model(saved_model_dir)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def test_saving_sequential_model(self):
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(
loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
ref_y = model.predict(x)
saved_model_dir = self._save_model_dir()
keras_saved_model.export_saved_model(model, saved_model_dir)
loaded_model = keras_saved_model.load_from_saved_model(saved_model_dir)
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)
def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin
"""Loads a model saved via `save_model`.
Arguments:
filepath: One of the following:
- String, path to the saved model
- `h5py.File` object from which to load the model
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
compile: Boolean, whether to compile the model
after loading.
Returns:
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed. When `compile` is set
to False, the compilation is omitted without any
warning.
Raises:
ImportError: if loading from an hdf5 file and h5py is not available.
IOError: In case of an invalid savefile.
"""
if not tf2.enabled() or (
h5py is not None and (
isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
if isinstance(filepath, six.string_types):
loader_impl.parse_saved_model(filepath)
return saved_model.load_from_saved_model(filepath)
raise IOError(
'Unable to load model. Filepath is not an hdf5 file (or h5py is not '
'available) or SavedModel.')
def _load_and_run_model(self, distribution, saved_dir, predict_dataset,
output_name):
restored_keras_model = saved_model.load_from_saved_model(saved_dir)
return restored_keras_model.predict(
predict_dataset, steps=test_base.PREDICT_STEPS)
def _load_and_run_model(self, distribution, saved_dir, predict_dataset,
output_name):
restored_keras_model = saved_model.load_from_saved_model(saved_dir)
return restored_keras_model.predict(
predict_dataset, steps=test_base.PREDICT_STEPS)
