def _make_train_function(self):
self._assert_compiled()
if self.train_function is None:
logging.info("=>Creating training function...")
inputs = self._feed_inputs + self._feed_targets
if self.uses_learning_phase:
inputs += [F.learning_phase()]
with ops.name_scope('training'):
with ops.name_scope(self.optimizer.__class__.__name__):
if not hasattr(self.optimizer, 'get_updates'):
self.optimizer = Optimizer(
optimizer=self.optimizer,
global_step=training_util.get_global_step())
# extra updates (e.g. slim.batch_norm)
update_ops = fops.get_collection(fops.GraphKeys.UPDATE_OPS)
training_updates = self.optimizer.get_updates(
params=list(self.trainable_weights), loss=self.loss)
self.train_function = Function(
inputs=inputs,
outputs=[self.loss] + self.metric_tensors,
updates=training_updates + update_ops,
name='train_function',
hooks=self.train_hooks,
**self._function_kwargs)
logging.info("=>Finish creating training function...")
def forward(self, inputs, training=None):
if training is None:
training = F.learning_phase()
def dropped_inputs():
return nn.dropout(
inputs,
noise_shape=self._get_noise_shape(inputs),
seed=self.seed,
rate=self.rate)
outputs = F.smart_cond(training,
dropped_inputs,
lambda: array_ops.identity(inputs))
return outputs
def _make_predict_function(self):
self._assert_compiled()
if self.predict_function is None:
logging.info("=>Creating predict function...")
inputs = self._feed_inputs
if self.uses_learning_phase:
inputs += [F.learning_phase()]
with ops.name_scope('predict'):
self.predict_function = Function(
inputs=inputs,
outputs=self.outputs,
hooks=self._predict_hooks,
name='predict_function',
**self._function_kwargs)
logging.info("=>Finish creating predict function...")
def _make_eval_function(self):
self._assert_compiled()
if self.eval_function is None:
logging.info("=>Creating evaluation function...")
inputs = self._feed_inputs + self._feed_targets
if self.uses_learning_phase:
inputs += [F.learning_phase()]
with ops.name_scope('evaluation'):
self.eval_function = Function(
inputs=inputs,
outputs=[self.loss] + self.metric_tensors,
name='eval_function',
hooks=self.val_hooks,
**self._function_kwargs)
logging.info("=>Finish creating evaluation function...")