def cifar10_model_fn_helper(self, mode):
features, labels = self.input_fn()
spec = cifar10_main.cifar10_model_fn(
features, labels, mode, {
'resnet_size': 32,
'data_format': 'channels_last',
'batch_size': _BATCH_SIZE,
})
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape,
(_BATCH_SIZE, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (_BATCH_SIZE, ))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
def cifar10_model_fn_helper(self, mode, multi_gpu=False):
input_fn = cifar10_main.get_synth_input_fn()
dataset = input_fn(True, '', _BATCH_SIZE)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
spec = cifar10_main.cifar10_model_fn(
features, labels, mode, {
'resnet_size': 32,
'data_format': 'channels_last',
'batch_size': _BATCH_SIZE,
'multi_gpu': multi_gpu
})
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape,
(_BATCH_SIZE, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (_BATCH_SIZE,))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
def cifar10_model_fn_helper(self, mode):
features, labels = self.input_fn()
spec = cifar10_main.cifar10_model_fn(
features, labels, mode, {
'resnet_size': 32,
'data_format': 'channels_last',
'batch_size': _BATCH_SIZE,
})
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape,
(_BATCH_SIZE, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (_BATCH_SIZE,))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
def cifar10_model_fn_helper(self, mode, version, multi_gpu=False):
input_fn = cifar10_main.get_synth_input_fn()
dataset = input_fn(True, '', _BATCH_SIZE)
iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
spec = cifar10_main.cifar10_model_fn(
features, labels, mode, {
'resnet_size': 32,
'data_format': 'channels_last',
'batch_size': _BATCH_SIZE,
'version': version,
'multi_gpu': multi_gpu
})
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape,
(_BATCH_SIZE, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (_BATCH_SIZE, ))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
def cifar10_model_fn_helper(self, mode):
features, labels = self.input_fn()
spec = cifar10_main.cifar10_model_fn(features, labels, mode)
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape,
(FLAGS.batch_size, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (FLAGS.batch_size,))
self.assertEqual(predictions['classes'].dtype, tf.int64)
if mode != tf.estimator.ModeKeys.PREDICT:
loss = spec.loss
self.assertAllEqual(loss.shape, ())
self.assertEqual(loss.dtype, tf.float32)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = spec.eval_metric_ops
self.assertAllEqual(eval_metric_ops['accuracy'][0].shape, ())
self.assertAllEqual(eval_metric_ops['accuracy'][1].shape, ())
self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)