本文整理汇总了Python中cleverhans.utils._ArgsWrapper方法的典型用法代码示例。如果您正苦于以下问题：Python utils._ArgsWrapper方法的具体用法？Python utils._ArgsWrapper怎么用？Python utils._ArgsWrapper使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cleverhans.utils的用法示例。
在下文中一共展示了utils._ArgsWrapper方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_ensemble_diversity_values
# 需要导入模块: from cleverhans import utils [as 别名]
# 或者: from cleverhans.utils import _ArgsWrapper [as 别名]
def get_ensemble_diversity_values(sess, x, y, predictions, number_model, X_test=None, Y_test=None,
feed=None, args=None):
"""
Compute the accuracy of a TF model on some data
:param sess: TF session to use
:param x: input placeholder
:param y: output placeholder (for labels)
:param predictions: model output predictions
:param X_test: numpy array with training inputs
:param Y_test: numpy array with training outputs
:param feed: An optional dictionary that is appended to the feeding
dictionary before the session runs. Can be used to feed
the learning phase of a Keras model for instance.
:param args: dict or argparse `Namespace` object.
Should contain `batch_size`
:return: a float with the accuracy value
"""
args = _ArgsWrapper(args or {})
assert args.batch_size, "Batch size was not given in args dict"
if X_test is None or Y_test is None:
raise ValueError("X_test argument and Y_test argument"
"must be supplied.")
ensemble_diversity_records = np.array([])
get_batch_ensemble_diversity = ensemble_diversity(y, predictions, number_model)
with sess.as_default():
# Compute number of batches
nb_batches = int(math.ceil(float(len(X_test)) / args.batch_size))
assert nb_batches * args.batch_size >= len(X_test)
X_cur = np.zeros((args.batch_size,) + X_test.shape[1:],
dtype=X_test.dtype)
Y_cur = np.zeros((args.batch_size,) + Y_test.shape[1:],
dtype=Y_test.dtype)
for batch in range(nb_batches):
if batch % 100 == 0 and batch > 0:
_logger.debug("Batch " + str(batch))
# Must not use the `batch_indices` function here, because it
# repeats some examples.
# It's acceptable to repeat during training, but not eval.
start = batch * args.batch_size
end = min(len(X_test), start + args.batch_size)
# The last batch may be smaller than all others. This should not
# affect the accuarcy disproportionately.
cur_batch_size = end - start
X_cur[:cur_batch_size] = X_test[start:end]
Y_cur[:cur_batch_size] = Y_test[start:end]
feed_dict = {x: X_cur, y: Y_cur}
if feed is not None:
feed_dict.update(feed)
ensemble_diversity_records_batch = get_batch_ensemble_diversity.eval(feed_dict=feed_dict)
ensemble_diversity_records = np.concatenate((ensemble_diversity_records, ensemble_diversity_records_batch), axis=0)
assert end >= len(X_test)
return ensemble_diversity_records #len(X_test) X 1