def test_export_keras_estimator(self, checkpoint_format):
keras_model, (x_train, y_train), (
_, _), train_input_fn, _ = get_resource_for_simple_model(
model_type='sequential', is_evaluate=False)
keras_model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
keras_model.fit(x_train, y_train, epochs=1)
bias_value = keras.backend.get_value(keras_model.layers[0].bias)
est_keras = keras_lib.model_to_estimator(
keras_model=keras_model,
model_dir=tempfile.mkdtemp(dir=self._base_dir),
checkpoint_format=checkpoint_format)
def serving_input_receiver_fn():
feature_spec = {
'dense_input':
parsing_ops.FixedLenFeature([1], dtype=dtypes.float32)
}
return export_lib.build_parsing_serving_input_receiver_fn(
feature_spec)
# Try immediately exporting, testing that (1) exported values are the same,
# and (2) estimator can be exported without saving a checkpoint into the
# model directory.
saved_model_dir = est_keras.export_saved_model(
tempfile.mkdtemp(dir=self._base_dir), serving_input_receiver_fn())
variables_path = saved_model_utils.get_variables_path(saved_model_dir)
variable_name = 'dense/bias'
if checkpoint_format == 'checkpoint':
names_to_keys = saver_lib.object_graph_key_mapping(variables_path)
variable_name = names_to_keys[variable_name]
self.assertAllClose(
bias_value, training.load_variable(variables_path, variable_name))
# Export the estimator after training a bit.
est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16)
saved_model_dir = est_keras.export_saved_model(
tempfile.mkdtemp(dir=self._base_dir), serving_input_receiver_fn())
variables_path = saved_model_utils.get_variables_path(saved_model_dir)
self.assertNotAllClose(
bias_value, training.load_variable(variables_path, variable_name))
def get_variable_value(self, name):
"""Returns value of the variable given by name.
Args:
name: string or a list of string, name of the tensor.
Returns:
Numpy array - value of the tensor.
Raises:
ValueError: If the Estimator has not produced a checkpoint yet.
"""
_check_checkpoint_available(self.model_dir)
return training.load_variable(self.model_dir, name)
def get_variable_value(self, name):
"""Returns value of the variable given by name.
Args:
name: string or a list of string, name of the tensor.
Returns:
Numpy array - value of the tensor.
Raises:
ValueError: If the Estimator has not produced a checkpoint yet.
"""
_check_checkpoint_available(self.model_dir)
return training.load_variable(self.model_dir, name)
