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Updated 3 years ago
import numpy as np
import torch# Input (temp, rainfall, humidity)
inputs = np.array([[73, 67, 43],
[91, 88, 64],
[87, 134, 58],
[102, 43, 37],
[69, 96, 70]], dtype='float32')
inputsarray([[ 73., 67., 43.],
[ 91., 88., 64.],
[ 87., 134., 58.],
[102., 43., 37.],
[ 69., 96., 70.]], dtype=float32)# Targets (apples, oranges)
targets = np.array([[56, 70],
[81, 101],
[119, 133],
[22, 37],
[103, 119]], dtype='float32')
targetsarray([[ 56., 70.],
[ 81., 101.],
[119., 133.],
[ 22., 37.],
[103., 119.]], dtype=float32)# Convert inputs and targets to tensors
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.]])
# Weights and biases
w = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, requires_grad=True)
print(w)
print(b)tensor([[ 1.4509, 0.3496, -0.9204],
[ 1.1469, -0.3790, 0.9032]], requires_grad=True)
tensor([ 1.7208, -0.0603], requires_grad=True)