Tensor Manipulation

Learn to manipulate PyTorch tensors.

In the coming lessons, we’ll explore neural networks with the PyTorch library. Images are a form of data easily consumable by a neural network. Therefore, in many situations, neural networks can help us in automated inspection.

Before diving into the internal operators of a neural network, we must take a closer look at the data format that each component will process as input and output: the tensor.

What is a tensor?

A tensor is a multidimensional array.

Note: If you have used the NumPy module, you’ve probably manipulated numpy.array objects, which are tensors.

You can create a numpy.array object and convert it to a torch.Tensor object with the function torch.from_numpy().

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import torch # import PyTorch
import numpy as np
arr1 = np.array([[1., 2., 3.], [4., 5., 6.]])
arr1_tsr = torch.from_numpy(arr1)
print(f"arr1: \n{arr1}\n")
print(f"arr1_tsr: \n{arr1_tsr}")

In line 5, we create a torch.Tensor object from a numpy.array object.

Conversely, we can create a numpy.array object from a ...

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