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What is the difference between Tensor.size and Tensor.shape in Pytorch?
I want to get the number of elements and the dimensions of Tensor. For example for a tensor with the dimensions of 2 by 3 by 4 I expect 24 for number of elements and (2,3,4) for dimension.
Thanks.
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A tensor with one dimension can be thought of as a vector, a tensor with two dimensions as a matrix and a tensor with three dimensions can be thought of as a cuboid. The number of dimensions a tensor has is called its rank and the length in each dimension describes its shape . For example, a 2 by 3 matrix: 1 2 3 4 5 6.
We access the size (or shape) of a tensor and the number of elements in the tensor as the metadata of the tensor. To access the size of a tensor, we use the . size() method and the shape of a tensor is accessed using .
A tensor is a vector or matrix of n-dimensions that represents all types of data. All values in a tensor hold identical data type with a known (or partially known) shape. The shape of the data is the dimensionality of the matrix or array. A tensor can be originated from the input data or the result of a computation.
shape gives a tuple of ints of dimensions of V. In tensorflow V. get_shape(). as_list() gives a list of integers of the dimensions of V. In pytorch, V.
.shape is an alias for .size(), and was added to more closely match numpy, see this discussion here.
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