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I have a list (or, a numpy array) of float values. I want to create a 1d torch tensor that will contain all those values. I can create the torch tensor and run a loop to store the values.
But I want to know is there any way, I can create a torch tensor with initial values from a list or array? Also suggest me if there is any pythonic way to achieve this as I am working in pytorch.
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To convert a Python list to a tensor, we are going to use the tf. convert_to_tensor() function and this function will help the user to convert the given object into a tensor. In this example, the object can be a Python list and by using the function will return a tensor.
Creating one-dimensional Tensor tensor() method. Syntax of creating one dimensional tensor is as follows: n= torch. tensor([Tensor elements])
There are a few main ways to create a tensor, depending on your use case. To create a tensor with pre-existing data, use torch.tensor() . To create a tensor with specific size, use torch.* tensor creation ops (see Creation Ops).
These are general operations in pytorch and available in the documentation. PyTorch allows easy interfacing with numpy. There is a method called from_numpy and the documentation is available here
import numpy as np
import torch
array = np.arange(1, 11)
tensor = torch.from_numpy(array)
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