def testAssertModelCloneSameObjectsIgnoreOptimizer(self):
input_arr = np.random.random((1, 3))
target_arr = np.random.random((1, 3))
model_graph = ops.Graph()
clone_graph = ops.Graph()
# Create two models with the same layers but different optimizers.
with session.Session(graph=model_graph):
inputs = keras.layers.Input(shape=(3,))
x = keras.layers.Dense(2)(inputs)
x = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, x)
model.compile(loss='mse', optimizer=training_module.AdadeltaOptimizer())
model.train_on_batch(input_arr, target_arr)
with session.Session(graph=clone_graph):
inputs = keras.layers.Input(shape=(3,))
x = keras.layers.Dense(2)(inputs)
x = keras.layers.Dense(3)(x)
clone = keras.models.Model(inputs, x)
clone.compile(loss='mse', optimizer=keras.optimizers.RMSprop(lr=0.0001))
clone.train_on_batch(input_arr, target_arr)
keras_saved_model._assert_same_non_optimizer_objects(
model, model_graph, clone, clone_graph)
def testAssertModelCloneSameObjectsThrowError(self):
input_arr = np.random.random((1, 3))
target_arr = np.random.random((1, 3))
model_graph = ops.Graph()
clone_graph = ops.Graph()
# Create two models with the same layers but different optimizers.
with session.Session(graph=model_graph):
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
x = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, x)
model.compile(loss='mse',
optimizer=training_module.AdadeltaOptimizer())
model.train_on_batch(input_arr, target_arr)
with session.Session(graph=clone_graph):
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
x = keras.layers.Dense(4)(x)
x = keras.layers.Dense(3)(x)
clone = keras.models.Model(inputs, x)
clone.compile(loss='mse',
optimizer=keras.optimizers.RMSprop(lr=0.0001))
clone.train_on_batch(input_arr, target_arr)
with self.assertRaisesRegexp(
errors.InternalError,
'Model and clone must use the same variables.'):
keras_saved_model._assert_same_non_optimizer_objects(
model, model_graph, clone, clone_graph)
