def testEvalOpAndFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'eval_ops_and_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
num_evals = 5
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
eval_ops = state_ops.assign_add(my_var, 1.0)
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=eval_ops,
final_ops={'value': final_ops},
hooks=[
evaluation.StopAfterNEvalsHook(num_evals),
])
self.assertEqual(final_ops_values['value'],
num_evals + final_increment)
def testEvaluatePerfectModel(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'evaluate_perfect_model_once')
# Train a Model to completion:
self._train_model(checkpoint_dir, num_steps=300)
# Run
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
labels = constant_op.constant(self._labels, dtype=dtypes.float32)
logits = logistic_classifier(inputs)
predictions = math_ops.round(logits)
accuracy, update_op = metric_ops.streaming_accuracy(
predictions, labels)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=update_op,
final_ops={'accuracy': accuracy},
hooks=[
evaluation.StopAfterNEvalsHook(1),
])
self.assertTrue(final_ops_values['accuracy'] > .99)
def testEvalOpAndFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'eval_ops_and_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
num_evals = 5
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
eval_ops = state_ops.assign_add(my_var, 1.0)
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=eval_ops,
final_ops={'value': final_ops},
hooks=[
evaluation.StopAfterNEvalsHook(num_evals),
])
self.assertEqual(final_ops_values['value'], num_evals + final_increment)
def testEvaluatePerfectModel(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'evaluate_perfect_model_once')
# Train a Model to completion:
self._train_model(checkpoint_dir, num_steps=300)
# Run
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
labels = constant_op.constant(self._labels, dtype=dtypes.float32)
logits = logistic_classifier(inputs)
predictions = math_ops.round(logits)
accuracy, update_op = metric_ops.streaming_accuracy(predictions, labels)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=update_op,
final_ops={'accuracy': accuracy},
hooks=[
evaluation.StopAfterNEvalsHook(1),
])
self.assertTrue(final_ops_values['accuracy'] > .99)
def testLatestCheckpointReturnsNoneAfterTimeout(self):
start = time.time()
ret = evaluation_lib.wait_for_new_checkpoint(
'/non-existent-dir', 'foo', timeout=1.0, seconds_to_sleep=0.5)
end = time.time()
self.assertIsNone(ret)
# We've waited one time.
self.assertGreater(end, start + 0.5)
# The timeout kicked in.
self.assertLess(end, start + 1.1)
def testLatestCheckpointReturnsNoneAfterTimeout(self):
start = time.time()
ret = evaluation_lib.wait_for_new_checkpoint(
'/non-existent-dir', 'foo', timeout=1.0, seconds_to_sleep=0.5)
end = time.time()
self.assertIsNone(ret)
# We've waited one time.
self.assertGreater(end, start + 0.5)
# The timeout kicked in.
self.assertLess(end, start + 1.1)
def testOnlyFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'only_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path, final_ops={'value': final_ops})
self.assertEqual(final_ops_values['value'], final_increment)
def testOnlyFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'only_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path, final_ops={'value': final_ops})
self.assertEqual(final_ops_values['value'], final_increment)
