def _batch_norm_ops(self, test=False):
name = "batch_norm"
g = tf.Graph()
with g.as_default():
tf.compat.v1.set_random_seed(self.name_to_seed(name))
input_tensor = tf.compat.v1.get_variable(
"input_tensor", dtype=tf.float32,
initializer=tf.random.uniform((32, 16, 16, 3), maxval=1)
)
layer = resnet_model.batch_norm(
inputs=input_tensor, data_format=DATA_FORMAT, training=True)
self._save_or_test_ops(
name=name, graph=g, ops_to_eval=[input_tensor, layer], test=test,
correctness_function=self.default_correctness_function
)
def _batch_norm_ops(self, test=False):
name = "batch_norm"
g = tf.Graph()
with g.as_default():
tf.set_random_seed(self.name_to_seed(name))
input_tensor = tf.get_variable(
"input_tensor", dtype=tf.float32,
initializer=tf.random_uniform((32, 16, 16, 3), maxval=1)
)
layer = resnet_model.batch_norm(
inputs=input_tensor, data_format=DATA_FORMAT, training=True)
self._save_or_test_ops(
name=name, graph=g, ops_to_eval=[input_tensor, layer], test=test,
correctness_function=self.default_correctness_function
)