torch.from_numpy

uint8 , and numpy.bool . Warning. Writing to a tensor created from a read-only NumPy array is not supported and will result in undefined behavior.

torch.as_tensor

If data is a NumPy array (an ndarray) with the same dtype and device then a ...

torch.asarray

Converts obj to a tensor. obj can be one of: a tensor. a NumPy array. a DLPack capsule. an object that implements Python's buffer protocol. a scalar.

torch.flatten

Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, or copy. If no dimensions are flattened, ...

torch.nan_to_num

By default, NaN s are replaced with zero, positive infinity is replaced with the greatest finite value representable by input 's dtype, and negative infinity is ...

torch.tensor_split

This function is based on NumPy's numpy.array_split() . Parameters. input (Tensor) – the tensor to split. indices_or_sections (Tensor, int or list or tuple ...

torch.meshgrid

meshgrid(*tensors) currently has the same behavior as calling numpy.meshgrid(*arrays, indexing='ij') . In the future torch.meshgrid will transition to indexing= ...

torch.reshape

Returns a tensor with the same data and number of elements as input , but with the specified shape.

torch.set_printoptions

threshold – Total number of array elements which trigger summarization rather than full repr (default = 1000).

torch.nn.Softmax

Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and ...