def testAdditionalHooks(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
self._prepareCheckpoint(checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(
self._predictions, self._labels)
dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir')
dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False)
try:
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once('',
checkpoint_path,
log_dir,
eval_op=update_op,
final_op=value_op,
hooks=[dumping_hook])
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
dump = debug_data.DebugDumpDir(
glob.glob(os.path.join(dumping_root, 'run_*'))[0])
# Here we simply assert that the dumped data has been loaded and is
# non-empty. We do not care about the detailed model-internal tensors or
# their values.
self.assertTrue(dump.dumped_tensor_data)
finally:
if os.path.isdir(dumping_root):
shutil.rmtree(dumping_root)
def testAdditionalHooks(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
self._prepareCheckpoint(checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(self._predictions,
self._labels)
dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir')
dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False)
try:
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once(
'', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op,
hooks=[dumping_hook])
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
dump = debug_data.DebugDumpDir(
glob.glob(os.path.join(dumping_root, 'run_*'))[0])
# Here we simply assert that the dumped data has been loaded and is
# non-empty. We do not care about the detailed model-internal tensors or
# their values.
self.assertTrue(dump.dumped_tensor_data)
finally:
if os.path.isdir(dumping_root):
shutil.rmtree(dumping_root)
def testRestoredModelPerformance(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
self._prepareCheckpoint(checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(self._predictions,
self._labels)
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once(
'', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op)
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def testRestoredModelPerformance(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
self._prepareCheckpoint(checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(self._predictions,
self._labels)
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once(
'', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op)
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def testRestoredModelPerformance(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
init_op = control_flow_ops.group(variables.global_variables_initializer(),
variables.local_variables_initializer())
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
with self.test_session() as sess:
sess.run(init_op)
saver.save(sess, checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(self._predictions,
self._labels)
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once(
'', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op)
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def testRestoredModelPerformance(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
init_op = control_flow_ops.group(
variables.global_variables_initializer(),
variables.local_variables_initializer())
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
with self.test_session() as sess:
sess.run(init_op)
saver.save(sess, checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(
self._predictions, self._labels)
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once('',
checkpoint_path,
log_dir,
eval_op=update_op,
final_op=value_op)
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def testErrorRaisedIfCheckpointDoesntExist(self):
checkpoint_path = os.path.join(self.get_temp_dir(),
'this_file_doesnt_exist')
log_dir = os.path.join(self.get_temp_dir(), 'error_raised')
with self.assertRaises(errors.NotFoundError):
evaluation.evaluate_once('', checkpoint_path, log_dir)
def testErrorRaisedIfCheckpointDoesntExist(self):
checkpoint_path = os.path.join(self.get_temp_dir(),
'this_file_doesnt_exist')
log_dir = os.path.join(self.get_temp_dir(), 'error_raised')
with self.assertRaises(errors.NotFoundError):
evaluation.evaluate_once('', checkpoint_path, log_dir)
