PyTorch: Difference between revisions

Revision as of 15:20, 6 March 2024

Revision as of 15:24, 6 March 2024

Line 66:

import torch

dtype = torch.float

device = torch.device("cpu") # This# executesExecute all calculations on the CPU

# device = torch.device("cuda:0") # This# executesExecutes all calculations on the GPU

# Creation ofCreate a tensor and fillingfill of a tensorit with random numbers

a = torch.randn(2, 3, device=device, dtype=dtype)

print(a) # Output of tensor A

# Output: tensor([[-1.1884, 0.8498, -1.7129],

# [-0.8816, 0.1944, 0.5847]])

# Creation of a tensor and filling of a tensor with random numbers

b = torch.randn(2, 3, device=device, dtype=dtype)

print(b) # Output of tensor B

# Output: tensor([[ 0.7178, -0.8453, -1.3403],

# [ 1.3262, 1.1512, -1.7070]])

print(a * b)

print(a*b) # Output of a multiplication of the two tensors

# Output: tensor([[-0.8530, -0.7183, 2.58],

# [-1.1692, 0.2238, -0.9981]])

print(a.sum()) # Output of the sum of all elements in tensor A

# Output: tensor(-2.1540)

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# Output: tensor(0.5847)

print(a.max()) # Output of the maximum value in tensor A

# Output: tensor(0.8498)

</syntaxhighlight>The following code-block shows an example of the higher level functionality provided <code>nn</code> module. A neural network with linear layers is defined in the example.<syntaxhighlight lang="python3" line="1">