to_sparse() tensor(indices=tensor([[1, 1], [0, 2]]), values=tensor([ 9, 10]), size=(3, 3), nnz=2, layout=torch.sparse_coo) >>> d.to_sparse(1) tensor(indices=tensor([[1]]), values=tensor([[ 9, 0, 10]]), size=(3,
TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'> to Tensor. Contents: SparseTensor(indices=Tensor("DeserializeSparse:0", shape=(?, 2), dtype=int64, device=/device:GPU:*), values=Tensor("DeserializeSparse:1", shape=(?,), dtype=int6...
to(other, non_blocking=False, copy=False) → Tensor to_mkldnn() → Tensor take(indices) → Tensor tan() → Tensor tan_() → Tensor tanh() → Tensor tanh_() → Tensor tolist() topk(k, dim=None, largest=True, sorted=True) -> (Tensor, LongTensor) to_sparse(sparseDims) → Tenso...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Add sparse tensors constructed via legacy constructor to _sparse_tensors_to_validate · pytorch/pytorch@0067c32
稀疏张量可以通过提供非零元素的索引和值来定义。在PyTorch中，稀疏张量通常使用torch.sparse_coo_tensor()函数来创建，该函数接受至少两个参数：索引和值。一旦定义了稀疏张量，可以通过调用.to_dense()方法将其转换为稠密张量。稠密张量会包含所有的零和非零值。
Technically, torch.sparse.{Foo}Tensor should not even be called by our rebuild process since afaict this was the first PR that added support for sparse tensor serialization #27062 and it already uses _rebuild_sparse_tensor (which would add the rebuilt tensor to the list to validate), but to...
[1, 2, 3, 4], dtype=np.int32) shape = np.array([5, 5], dtype=np.int32) x = tf.SparseTensor(values=values,indices=indices,dense_shape=shape) with tf.Session() as sess: result = sess.run(x) print(result) result_value = tf.sparse_tensor_to_dense(result) print('value:\n',...
要将稀疏张量(Sparse Tensor)对象取出来,可以使用 TensorFlow 提供的tf.sparse.to_dense函数将其转换为密集张量(Dense Tensor)。以下是一个示例: importtensorflowastf # 假设有一个稀疏张量 sparse_tensor sparse_tensor=tf.SparseTensor(indices=[[0,1],[2,3]],values=[4,5],dense_shape=[3,4]) ...
如果有单独的指标、值和dense_shape张量，在传递到下面的ops之前，将它们包装在sparse张量对象中。具体来...
Du, A sparse and discriminative tensor to vector projection for human gait feature representation, Signal Process., 106 (Jan 2015) 245-252.L. Zhang, L. Zhang, D. Tao, and B. Du, "A sparse and discriminative tensor to vector projection for human gait feature representation," Signal ...