Orthogonal连接方式 2. Diagonal连接方式 3. FULL_CONTACT连接方式 4. 8_WAY连接方式 ---- 1....Orthogonal连接方式 Orthogonal：特点是焊盘到铜皮的Cline最多4条，且互相垂直。...Smd pins设置为Orthogonal，Minimum connects =4，Maximum connects =4，效果如下。...研究这个东西意义不大，只需要关注结论就好了，Smd pins设置为Orthogonal时，Minimum connects 设置为2，Maximum connects 设置为4，无需勾选best contact...FULL_CONTACT连接方式 似乎也没什么好解释的... 4. 8_WAY连接方式 8_WAY就是最多8条cline连接焊盘和铜皮，实际是Orthogonal和Diagonal组合成的一种连接方式
1简介 本文提出了orthogonal-Padé激活函数，它是可以训练的激活函数，在标准深度学习数据集和模型中具有更快的学习能力，同时可以提高模型的准确率。...根据实验，在六种orthogonal-Padé激活中找到了2种最佳的候选函数，作者称之为 safe Hermite-Pade(HP)激活函数，即HP-1和HP-2。...本文提出了Orthogonal-Padé激活函数。Orthogonal-Padé函数可以近似大多数连续函数。...3.2 Orthogonal-Padé activation Unit (OPAU) g(x)由有理函数G(x)的orthogonal-Padé近似定义为： 其中 属于正交多项式集合。...对输入x和参数 、 计算公式(6)的梯度如下: 4具有orthogonal-Padé激活以及函数近似的网络 Orthogonal-Padé网络类似于Padé网络，即将具有PAU或safe PAU的网络替换为
此外，本文还提出了正交(orthogonal) 和差分正则化 (differential regularizers)，通过限制SemGCN模块中的注意分数来精确捕获单词之间的语义相关性： 正交正则化鼓励...提出正交(orthogonal) 和差分正则化 (differential regularizers)来优化。 3. 在数据集上验证了有效性。...这里使用了BiAffine映射方法： 最后将二者的隐层pooling后再concat起来过softmax做预测： Regularizer 为了提高语义表达，本文为SemGCN提出两个正则方法：正交(orthogonal...Orthogonal Regularizer 直观上，每个词的相关项应该在句子的不同区域，所以注意力分数的分布应该要尽量少的重叠。
lose much information without care; takes too long for large dataset Second Principal Component: orthogonal...First dimension chosen to capture as much of the variability as possible Second dimension is orthogonal...to the first & captures as much as the remaining variability as possible Third dimension is orthogonal...The second dimension is orthogonal to the first, and subject to that constraint, captures...as much of the remaining variability as possible, The third dimension is orthogonal to the first
tf.random_normal_initializer(mean=0.0, stddev=0.02) Truncated_normal : tf.truncated_normal_initializer(mean=0.0, stddev=0.02) Orthogonal...: tf.orthogonal_initializer(1.0) / # if relu = sqrt(2), the others = 1.0 正则化(Regularization) l2_decay...: tf.contrib.layers.l2_regularizer(0.0001) orthogonal_regularizer : orthogonal_regularizer(0.0001) &...orthogonal_regularizer_fully(0.0001) 卷积(Convolution) basic conv x = conv(x, channels=64, kernel=3, stride
self.pixel_shuffle(self.conv4(x)) return x def _initialize_weights(self): init.orthogonal..._(self.conv1.weight, init.calculate_gain('relu')) init.orthogonal_(self.conv2.weight, init.calculate_gain...('relu')) init.orthogonal_(self.conv3.weight, init.calculate_gain('relu')) init.orthogonal
Orthogonal Frequency-Division Multiplexing The next step in trying to move even more data over...wireless frequency signals came in the form of orthogonal frequency-division multiplexing (OFDM).
最后，orthogonal 请学好这个单字，未来有机会跟外国工程师谈话的时候，当他们提到一个很酷的论点，想办法念出这句话「Isn’t that orthogonal to this conversation...orthogonal 的原意是数学术语「正交、矩形」，延伸出「两个独立、无关的事件」之意，因此如果提出一个跳脱当下对话框架的点子，就可以用「orthogonal」来比喻，但这种用法应该仅限工程师世界啦，...所以如果一个局外人居然吐出 orthogonal 这个有点艰涩的单字又用得恰如其分，可想而知他们会多感动。
max_features))) #masking用于变长序列输入 model.add(GRU(units=n_hidden_units,activation='selu',kernel_initializer='orthogonal...', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.01...output_dim=64,mask_zero=True)) model.add(GRU(units=n_hidden_units,activation='selu',kernel_initializer='orthogonal...', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.01
Random initialization...'); centers = random_init(data, num_clusters); elseif isempty(centers) disp('Orthogonal...1, num_clusters)), :); function init_centers = orth_init(data, num_clusters) %ORTH_INIT Initialize orthogonal...init_centers : K-by-D matrix, where K is num_clusters % % Find the num_clusters centers which are orthogonal...centers init_centers(1, :) = Uniq(first, :); Uniq(first, :) = []; c = zeros(num-1, 1); % Accumalated orthogonal...:num_clusters c = c + abs(Uniq*init_centers(j-1, :)'); [minimum, i] = min(c); % Select the most orthogonal
= torch.empty(3, 5)>>> nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')torch.nn.init.orthogonal..._(tensor, gain=1)[source]Fills the input Tensor with a (semi) orthogonal matrix, as described in Exact..., where n≥2n \geq 2n≥2 gain – optional scaling factor Examples>>> w = torch.empty(3, 5)>>> nn.init.orthogonal
Orthogonal Random Forest for Causal Inference....Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels...Orthogonal Statistical Learning. arXiv preprint arXiv:1901.09036, 2019 [文章链接] Meta Learner C.
homogeneous 齐次 affine transform 仿射变换 linear transform 线性变换 polynomial(multinomial) 多项式 convex hull 凸包 orthogonal
numpy.dot(logspec, melcos.T) / nfilt def dctmat(N,K,freqstep,orthogonalize=True): """Return the orthogonal...numpy.sqrt(2) return cosmat def dct(input, K=13): """Convert log-power-spectrum to MFCC using the orthogonal...input, cosmat) * (2.0 / N) def idct(input, K=40): """Convert MFCC to log-power-spectrum using the orthogonal
Orthogonal Random Forest orthogonal random forest 只是在 generalized random forest 的基础上进行了两个改动： 加了 DML：...Orthogonal random forest for causal inference[C]//International Conference on Machine Learning.
本文的主要目的是将下图所示的铜皮与焊盘的连接方式由Orthogonal改为FULL_CONTACT。
IEOPF: An Active Contour Model for Image Segmentation with Inhomogeneities Estimated by Orthogonal Primary...inhomogeneities, a bias correction embedded level set model is proposed where Inhomogeneities are Estimated by Orthogonal...the proposed model, the smoothly varying bias is estimated by a linear combination of a given set of orthogonal
_initialize(y) for i in range(self.max_iter): x = linear_model.orthogonal_mp(self.dictionary..._update_dict(y, self.dictionary, x) self.sparsecode = linear_model.orthogonal_mp(self.dictionary
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