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Updated 5 years ago
import numpy as np
import torch
import jovian
inputs = np.array([[73, 67, 43],
[91, 88, 64],
[87, 134, 58],
[102, 43, 37],
[69, 96, 70]], dtype='float32')
targets = np.array([[56, 70],
[81, 101],
[119, 133],
[22, 37],
[103, 119]], dtype='float32')
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
print(inputs)
print(targets)
tensor([[ 73., 67., 43.],
[ 91., 88., 64.],
[ 87., 134., 58.],
[102., 43., 37.],
[ 69., 96., 70.]])
tensor([[ 56., 70.],
[ 81., 101.],
[119., 133.],
[ 22., 37.],
[103., 119.]])
w = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, requires_grad=True)
print(w)
print(b)
tensor([[ 0.1715, 0.6757, -0.3921],
[-0.1155, 1.7360, 1.1818]], requires_grad=True)
tensor([ 0.3256, -0.8298], requires_grad=True)