def build_subnetwork(self,
features,
logits_dimension,
training,
iteration_step,
summary,
previous_ensemble=None):
return Subnetwork(last_layer=tu.dummy_tensor(),
logits=tu.dummy_tensor([2, logits_dimension]),
complexity=tu.dummy_tensor(),
persisted_tensors={"random_seed": self._random_seed})
def build_subnetwork(self,
features,
logits_dimension,
training,
iteration_step,
summary,
previous_ensemble=None):
assert features is not None
assert training is not None
assert iteration_step is not None
assert summary is not None
# Trainable variables collection should always be empty when
# build_subnetwork is called.
assert not tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
# Subnetworks get iteration steps instead of global steps.
global_step = tf.train.get_global_step()
assert "ensemble_test/iteration_step" == global_step.op.name
# Subnetworks get scoped summaries.
assert "fake_scalar" == tf.summary.scalar("scalar", 1.)
assert "fake_image" == tf.summary.image("image", 1.)
assert "fake_histogram" == tf.summary.histogram("histogram", 1.)
assert "fake_audio" == tf.summary.audio("audio", 1., 1.)
last_layer = tu.dummy_tensor(shape=(2, 3))
logits = tf.layers.dense(
last_layer,
units=logits_dimension,
kernel_initializer=tf.glorot_uniform_initializer(seed=self._seed))
return Subnetwork(
last_layer=logits if self._use_logits_last_layer else last_layer,
logits=logits,
complexity=2,
persisted_tensors={})
def build_subnetwork(self,
features,
logits_dimension,
training,
iteration_step,
summary,
previous_ensemble=None):
assert features is not None
assert training is not None
assert iteration_step is not None
assert summary is not None
# Trainable variables collection should always be empty when
# build_subnetwork is called.
assert not tf_compat.v1.get_collection(
tf_compat.v1.GraphKeys.TRAINABLE_VARIABLES)
# Subnetworks get iteration steps instead of global steps.
step_name = "subnetwork_test/iteration_step"
assert step_name == tf_compat.tensor_name(
tf_compat.v1.train.get_global_step())
assert step_name == tf_compat.tensor_name(train.get_global_step())
assert step_name == tf_compat.tensor_name(training_util.get_global_step())
assert step_name == tf_compat.tensor_name(tf_v1.train.get_global_step())
assert step_name == tf_compat.tensor_name(
tf_compat.v1.train.get_or_create_global_step())
assert step_name == tf_compat.tensor_name(train.get_or_create_global_step())
assert step_name == tf_compat.tensor_name(
training_util.get_or_create_global_step())
assert step_name == tf_compat.tensor_name(
tf_v1.train.get_or_create_global_step())
# Subnetworks get scoped summaries.
assert "fake_scalar" == tf_compat.v1.summary.scalar("scalar", 1.)
assert "fake_image" == tf_compat.v1.summary.image("image", 1.)
assert "fake_histogram" == tf_compat.v1.summary.histogram("histogram", 1.)
assert "fake_audio" == tf_compat.v1.summary.audio("audio", 1., 1.)
last_layer = tu.dummy_tensor(shape=(2, 3))
def logits_fn(logits_dim):
return tf_compat.v1.layers.dense(
last_layer,
units=logits_dim,
kernel_initializer=tf_compat.v1.glorot_uniform_initializer(
seed=self._seed))
if self._multi_head:
logits = {
"head1": logits_fn(logits_dimension / 2),
"head2": logits_fn(logits_dimension / 2)
}
last_layer = {"head1": last_layer, "head2": last_layer}
else:
logits = logits_fn(logits_dimension)
return Subnetwork(
last_layer=logits if self._use_logits_last_layer else last_layer,
logits=logits,
complexity=2,
persisted_tensors={})
def build_subnetwork(self,
features,
logits_dimension,
training,
iteration_step,
summary,
previous_ensemble=None):
assert features is not None
assert training is not None
assert iteration_step is not None
assert summary is not None
last_layer = tu.dummy_tensor(shape=(2, 3))
logits = tf.layers.dense(
last_layer,
units=logits_dimension,
kernel_initializer=tf.glorot_uniform_initializer(seed=self._seed))
return Subnetwork(
last_layer=logits if self._use_logits_last_layer else last_layer,
logits=logits,
complexity=2,
persisted_tensors={})