def testEvaluatePerfectModel(self):
if tf.executing_eagerly():
# tf.metrics.accuracy is not supported when eager execution is enabled.
return
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 = tf.constant(self._inputs, dtype=tf.float32)
labels = tf.constant(self._labels, dtype=tf.float32)
logits = logistic_classifier(inputs)
predictions = tf.round(logits)
accuracy, update_op = tf.compat.v1.metrics.accuracy(
predictions=predictions, labels=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):
if tf.executing_eagerly():
return
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 = tf.constant(self._inputs, dtype=tf.float32)
logistic_classifier(inputs)
num_evals = 5
final_increment = 9.0
try:
my_var = _local_variable(0.0, name='MyVar')
except TypeError: # `collections` doesn't exist in TF2.
return
eval_ops = tf.compat.v1.assign_add(my_var, 1.0)
final_ops = tf.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 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 = tf.constant(self._inputs, dtype=tf.float32)
logistic_classifier(inputs)
final_increment = 9.0
try:
my_var = _local_variable(0.0, name='MyVar')
except TypeError: # `collections` doesn't exist in TF2.
return
final_ops = tf.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)