def setup_tpu_session(master):
"""Initializes and returns a Keras/TF session connected the TPU `master`."""
session = tf_session.Session(
target=master, config=config_pb2.ConfigProto(isolate_session_state=True))
K.set_session(session)
K.get_session().run(tpu.initialize_system())
return session
def train():
sess = tf.Session()
K.set_session(sess)
model = neuralnetwork()
model_dir = os.path.join(os.getcwd(), "./models/%s" % (model_name))
os.makedirs(model_dir, exist_ok=True)
print("model_dir: ", model_dir)
the_estimator = tf.keras.estimator.model_to_estimator(keras_model=model,
model_dir=model_dir)
#the_estimator = KerasRegressor(build_fn=model, epochs=n_epochs, batch_size=batch_size, verbose=1)
train_spec = tf.estimator.TrainSpec(
input_fn=lambda: the_input_iterator(train_path,
perform_shuffle=True,
repeat_count=n_epochs,
batch_size=batch_size),
max_steps=n_steps)
valid_spec = tf.estimator.EvalSpec(input_fn=lambda: the_input_iterator(
valid_path, perform_shuffle=False, batch_size=1))
tf.estimator.train_and_evaluate(the_estimator, train_spec, valid_spec)
def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None):
"""Constructs an `Estimator` instance from given keras model.
For usage example, please see
@{$programmers_guide/estimators$creating_estimators_from_keras_models}.
Args:
keras_model: Keras model in memory.
keras_model_path: Directory to a keras model on disk.
custom_objects: Dictionary for custom objects.
model_dir: Directory to save Estimator model parameters, graph and etc.
config: Configuration object.
Returns:
An Estimator from given keras model.
Raises:
ValueError: if neither keras_model nor keras_model_path was given.
ValueError: if both keras_model and keras_model_path was given.
ValueError: if the keras_model_path is a GCS URI.
ValueError: if keras_model has not been compiled.
"""
if (not keras_model) and (not keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
raise ValueError(
'%s is not a local path. Please copy the model locally first.'
% keras_model_path)
logging.info('Loading models from %s', keras_model_path)
keras_model = models.load_model(keras_model_path)
else:
logging.info('Using the Keras model provided.')
keras_model = keras_model
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError(
'The given keras model has not been compiled yet. Please compile first '
'before calling `model_to_estimator`.')
if isinstance(config, dict):
config = run_config_lib.RunConfig(**config)
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects)
estimator = estimator_lib.Estimator(keras_model_fn,
model_dir=model_dir,
config=config)
# Pass the config into keras backend's default session.
with session.Session(config=estimator._session_config) as sess:
K.set_session(sess)
keras_weights = keras_model.get_weights()
if keras_model._is_graph_network:
# TODO(yifeif): move checkpoint initialization to scaffold.init_fn
_save_first_checkpoint(keras_model, estimator, custom_objects,
keras_weights)
elif keras_model.built:
logging.warning('You are creating an Estimator from a Keras model '
'manually subclassed from `Model`, that was '
'already called on some inputs (and thus already had '
'weights). We are currently unable to preserve '
'the model\'s state (its weights) '
'as part of the estimator '
'in this case. Be warned that the estimator '
'has been created using '
'a freshly initialized version of your model.\n'
'Note that this doesn\'t affect the state of the '
'model instance you passed as `keras_model` argument.')
return estimator
def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None):
"""Constructs an `Estimator` instance from given keras model.
For usage example, please see
@{$programmers_guide/estimators$creating_estimators_from_keras_models}.
Args:
keras_model: Keras model in memory.
keras_model_path: Directory to a keras model on disk.
custom_objects: Dictionary for custom objects.
model_dir: Directory to save Estimator model parameters, graph and etc.
config: Configuration object.
Returns:
An Estimator from given keras model.
Raises:
ValueError: if neither keras_model nor keras_model_path was given.
ValueError: if both keras_model and keras_model_path was given.
ValueError: if the keras_model_path is a GCS URI.
ValueError: if keras_model has not been compiled.
"""
if (not keras_model) and (not keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
raise ValueError(
'%s is not a local path. Please copy the model locally first.' %
keras_model_path)
logging.info('Loading models from %s', keras_model_path)
keras_model = models.load_model(keras_model_path)
else:
logging.info('Using the Keras model provided.')
keras_model = keras_model
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError(
'The given keras model has not been compiled yet. Please compile first '
'before calling `model_to_estimator`.')
if isinstance(config, dict):
config = run_config_lib.RunConfig(**config)
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects)
estimator = estimator_lib.Estimator(
keras_model_fn, model_dir=model_dir, config=config)
# Check if we need to call get_weights:
if _any_variable_initalized():
keras_weights = keras_model.get_weights()
# Warn if config passed to estimator tries to update GPUOptions. If a
# session has already been created, the GPUOptions passed to the first
# session sticks.
if estimator._session_config.HasField('gpu_options'):
logging.warning(
'The Keras backend session has already been set. '
'The _session_config passed to model_to_estimator will not be used.')
else:
# Pass the config into keras backend's default session.
sess = session.Session(config=estimator._session_config)
K.set_session(sess)
keras_weights = None
if keras_model._is_graph_network:
# TODO(yifeif): move checkpoint initialization to scaffold.init_fn
_save_first_checkpoint(keras_model,
estimator,
custom_objects,
keras_weights)
elif keras_model.built:
logging.warning('You are creating an Estimator from a Keras model '
'manually subclassed from `Model`, that was '
'already called on some inputs (and thus already had '
'weights). We are currently unable to preserve '
'the model\'s state (its weights) '
'as part of the estimator '
'in this case. Be warned that the estimator '
'has been created using '
'a freshly initialized version of your model.\n'
'Note that this doesn\'t affect the state of the '
'model instance you passed as `keras_model` argument.')
return estimator