代码: publicfloatmaxOfArray(float[] A) {floatmaxNum = -Float.MAX_VALUE;for(inti = 0; i < A.length; i++){ maxNum=Math.max(maxNum, A[i]); }returnmaxNum; }
输出数组中最大的值。 __EOF__
Create a 3-D array and compute the maximum over each page of data (rows and columns). A(:,:,1) = [2 4; -2 1]; A(:,:,2) = [9 13; -5 7]; A(:,:,3) = [4 4; 8 -3]; M1 = max(A,[],[1 2]) M1 = M1(:,:,1) = 4 M1(:,:,2) = 13 M1(:,:,3) = ...
To set up a Bilinear gradient, you need to specify colors2 array: let gradientView = GradientView(frame: CGRect(x: 100, y: 0, width: 100, height: 100)) gradientView.type = .bilinear gradientView.colors = [.red, .yellow] gradientView.colors2 = [.blue, .cyan] view.addSubview(grad...
(array) s_pmod_s_string[16] * float f_pmod_value * int32_t n_pmod_value * int32_t (array) an_pmod_value[16] * int16_t w_pmod_value * int16_t (array) aw_pmod_value[16] * uint16_t uw_pmod_value * uint16_t (array) auw_pmod_value[16] * uint8_t uch_pmod_value *...
a = np.array([3, 1, 2, 4, 6, 1]) print(np.argmax(a)) 当没有指定axis的时候,默认是0.所以最后输出的是4(也就是表示第四维值最大) 2.二维数组 import numpy as np a = np.array([[1, 5, 4, 2], [9, 6, 2, 8], [3, 7, 9, 1]]) ...
layers = 6×1 Layer array with layers: 1 '' Image Input 28×28×1 images with 'zerocenter' normalization 2 '' 2-D Convolution 20 5×5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' ReLU ReLU 4 '' 2-D Max Pooling 3×2 max pooling with stride [2 2] and padd...
The "sym" calculation is actually a fixed size, but since the max() macro uses some extensive tricks for safety, it ends up looking like a variable size. This replaces max() with a simple max macro which is sufficient for the calculation of the array size 他在声明一个定长数组的时候(看...
OfType OrderBy OrderByDescending Prepend Range Repeat Reverse Select SelectMany SequenceEqual Single SingleOrDefault Skip SkipWhile Sum Take TakeWhile ThenBy ThenByDescending ToArray ToDictionary ToHashSet ToList ToLookup Union Where Zip EnumerableExecutor EnumerableExecutor<T> EnumerableQuery EnumerableQuery<T>...
Sparse Softmax就是希望能处理这种不一致性,思路很简单,就是在训练的时候也把Top-k以外的Token概率置零: \beginarray}{cc|c \hline & Softmax & Sparse\text{ }Softmax \ \hline \text{基本定义} & p_i = \frac{e^{x_i}}{\sum\limits_{j=1}^n e^{x_j}} & p_i=\left{\begin{aligned...