From the metric and one Killing-Yano tensor of rank D-2 in any D-dimensional spacetime with such a principal Killing-Yano tensor, we show how to generate k = [(D+1)/2] Killing-Yano tensors, of rank D-2j for all 0 <= j <= k-1, and k rank-2 Killing tensors, giving k ...
r2_tensor = scatter(node_outer, data.batch, dim=0, reduce="sum") else: r2_tensor = scatter(node_outer, data.batch, dim=0, reduce="mean") return r2_tensor class Rank2DecompositionEdgeBlock(nn.Module): """ Output block for predicting rank-2 tensors (stress, dielectric tensor, etc)....
Tensor 是 TensorFlow 中基础的计算单元,它包括以下四个主要组成部分: name -- Tensor 的名字,在一个 Computation Graph 中作为 Tensor 的一个标识 type -- Tensor 中数据的类型, 例如 tf.float32, tf.int64, tf.string rank --- Tensor 中数据的维度, 类似矩阵中的rank (秩) 的概念 shape --- rank 定...
Shape (?, 128, 128, 2) is a rank 4 tensor, but the softmax operator apparently expected a rank 2 tensor. It looks to me like you are using an old version of Tensorflow. In version 0.10, tf.nn.softmax required that its input be rank 2: https://www.tensorflow.org/v...
As computing power increases, many more problems in engineering and data analysis involve computation with tensors, or multi-way data arrays. Most applications involve computing a decomposition of a tensor into a linear combination of rank-1 tensors. Ideally, the decomposition involves a minimal ...
In this paper we discuss a multilinear generalization of the best rank-R approximation problem for matrices, namely, the approximation of a given higher-order tensor, in an optimal least-squares sense, by a tensor that has prespecified column rank value, row rank value, etc. For matrices, the...
Describes which axis of the input Tensor to reduce across. For vectors, use axis = 0. You are getting this error because one of the argmax() have an input with rank <=1. Since you are passing axis=1, you need to pass tensors having rank > 1 to get a valid o...
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It is a group under tensor product. M(X) h... C Simpson - 《Annales Scientifiques De L École Normale Supérieure》 被引量: 323发表: 1993年 Moduli Spaces of Parabolic Higgs Bundles and Parabolic K(D) Pairs over Smooth Curves: I This paper concerns the moduli spaces of rank-two ...
inputs = Variable(torch.Tensor(inputs)) labels = Variable(torch.LongTensor(labels)) outputs = model(inputs) optimizer.zero_grad() loss = criterion(outputs, labels) #每100个回合打印一次loss if (epoch + 1)%100 == 0: print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'...