argmax(np.bincount(area_ids[list(neis)])) return pimms.imm_array(np.asarray(area_ids, dtype=np.int)) Example #21Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 6 votes def test_generate_np_targeted_gives_adversarial_example(self):...
outputs: list of `str`, name of the output to predict. If `None`, returns all. as_iterable: If True, return an iterable which keeps yielding predictions for each example until inputs are exhausted. Note: The inputs must terminate if you want the iterable to terminate (e.g. be sure ...
action = torch.zeros([model.number_of_actions], dtype=torch.float32)iftorch.cuda.is_available():# put on GPU if CUDA is availableaction = action.cuda()# get actionaction_index = torch.argmax(output)iftorch.cuda.is_available():# put on GPU if CUDA is availableaction_index = action_i...
assertListEqual(logits.get_shape().as_list(), [train_batch_size, num_classes]) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform( (eval_batch_size, eval_height, eval_width, 3)) logits, _ = vgg.vgg_a(eval_inputs, is_training=False, spatial_squeeze=False)...
# 需要导入模块: from keras import backend [as 别名]# 或者: from keras.backend importargmax[as 别名]defcall(self, inputs, **kwargs):iftype(inputs)islist:# true label is provided with shape = [None, n_classes], i.e. one-hot code.assertlen(inputs) ==2inputs, mask = inputselse...
python.tensorflow 本文搜集整理了关于python中tensorflow argmax方法/函数的使用示例。 Namespace/Package: tensorflow Method/Function: argmax 导入包: tensorflow 每个示例代码都附有代码来源和完整的源代码,希望对您的程序开发有帮助。 示例1 def eval(self, data, label, lens): predictions = [] vals = [] ...
Python Numpy基础教程 在NumPy中,维度称为轴,轴的数目为rank。...对于切片而言,当你将一个标量值赋值给一个切片时,该值会自动传播到整个选区,跟Python list最重要的区别在于:Numpy中数组的切片作用的是原始数据的视图,也就是数据没有被复制,所有的修改都会直接作用到源数据...Python list类似,对于高维对象,花样...
按照这个思路,一直遍历完整个数组。...在哈希表中查找7,查到了元素7的下标是7,所以元素6(下标是2)和元素7(下标是7)是一对结果: 按照这个思路,一直遍历完整个数组即可。...= i) { resultList.add(Arrays.asList(i,map.get(other))); //为防止找到重复的元素对 ...
When we compute the mean across the first axis, multiple values are returned, and we can access the numeric values by transforming the output tensor into a Python list or a NumPy array. Advanced indexing and slicing With NumPy ndarray objects, we have a pretty robust set of operations for ...
Develop a working understand of linear algebra ...by writing lines of code in python Discover how in my new Ebook: Linear Algebra for Machine Learning It providesself-study tutorialson topics like: Vector Norms, Matrix Multiplication, Tensors, Eigendecomposition, SVD, PCAand much more... ...