def bootstrap(working_dir, params):
"""Initialize a tf.Estimator run with random initial weights.
Args:
working_dir: The directory where tf.estimator will drop logs,
checkpoints, and so on
params: hyperparams of the model.
"""
# Forge an initial checkpoint with the name that subsequent Estimator will
# expect to find.
estimator_initial_checkpoint_name = 'model.ckpt-1'
save_file = os.path.join(working_dir, estimator_initial_checkpoint_name)
sess = tf.Session()
with sess.graph.as_default():
input_features, labels = get_inference_input(params)
dualnet_model.model_fn(input_features, labels,
tf.estimator.ModeKeys.PREDICT, params)
sess.run(tf.global_variables_initializer())
tf.train.Saver().save(sess, save_file)
def bootstrap(working_dir, params):
"""Initialize a tf.Estimator run with random initial weights.
Args:
working_dir: The directory where tf.estimator will drop logs,
checkpoints, and so on
params: hyperparams of the model.
"""
# Forge an initial checkpoint with the name that subsequent Estimator will
# expect to find.
estimator_initial_checkpoint_name = 'model.ckpt-1'
save_file = os.path.join(working_dir,
estimator_initial_checkpoint_name)
sess = tf.Session()
with sess.graph.as_default():
input_features, labels = get_inference_input(params)
dualnet_model.model_fn(
input_features, labels, tf.estimator.ModeKeys.PREDICT, params)
sess.run(tf.global_variables_initializer())
tf.train.Saver().save(sess, save_file)
def initialize_graph(self):
"""Initialize the graph with saved model."""
with self.sess.graph.as_default():
input_features, labels = get_inference_input(self.hparams)
estimator_spec = dualnet_model.model_fn(
input_features, labels, tf.estimator.ModeKeys.PREDICT, self.hparams)
self.inference_input = input_features
self.inference_output = estimator_spec.predictions
if self.save_file is not None:
self.initialize_weights(self.save_file)
else:
self.sess.run(tf.global_variables_initializer())
def initialize_graph(self):
"""Initialize the graph with saved model."""
with self.sess.graph.as_default():
input_features, labels = get_inference_input(self.hparams)
estimator_spec = dualnet_model.model_fn(
input_features, labels, tf.estimator.ModeKeys.PREDICT, self.hparams)
self.inference_input = input_features
self.inference_output = estimator_spec.predictions
if self.save_file is not None:
self.initialize_weights(self.save_file)
else:
self.sess.run(tf.global_variables_initializer())