Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Loading Data, Devices, and CUDA
It is time to start converting our Numpy code to PyTorch: We’ll start with the
training data; that is, our x_train and y_train arrays.
"How do we go from Numpy’s arrays to PyTorch’s tensors?"
That’s what as_tensor() is good for (which works like from_numpy()).
This operation preserves the type of the array:
x_train_tensor = torch.as_tensor(x_train)
x_train.dtype, x_train_tensor.dtype
Output
(dtype('float64'), torch.float64)
You can also easily cast it to a different type, like a lower-precision (32-bit) float,
which will occupy less space in memory, using float():
float_tensor = x_train_tensor.float()
float_tensor.dtype
Output
torch.float32
IMPORTANT: Both as_tensor() and from_numpy() return a
tensor that shares the underlying data with the original Numpy
array. Similar to what happened when we used view() in the last
section, if you modify the original Numpy array, you’re modifying
the corresponding PyTorch tensor too, and vice-versa.
76 | Chapter 1: A Simple Regression Problem