def mnist_model_fn_helper(self, mode, multi_gpu=False):
print("mnist_model_fn_helper() in")
features, labels = dummy_input_fn()
image_count = features.shape[0]
spec = mnist.model_fn(features, labels, mode, {
'data_format': 'channels_last',
'multi_gpu': multi_gpu
})
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape, (image_count, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (image_count,))
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 mnist_model_fn_helper(self, mode, multi_gpu=False):
features, labels = dummy_input_fn()
image_count = features.shape[0]
spec = mnist.model_fn(features, labels, mode, {
'data_format': 'channels_last',
'multi_gpu': multi_gpu
})
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = spec.predictions
self.assertAllEqual(predictions['probabilities'].shape, (image_count, 10))
self.assertEqual(predictions['probabilities'].dtype, tf.float32)
self.assertAllEqual(predictions['classes'].shape, (image_count,))
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)