before training
loss=1.465227
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.7946, dtype=torch.float64)
torch.min(eigvals): tensor(0.5000, dtype=torch.float64)
all([e > 0 for e in eigvals]): True
covar:
 tensor([[0.5043, 0.0043, 0.0043,  ..., 0.0041, 0.0041, 0.0042],
        [0.0043, 0.5043, 0.0042,  ..., 0.0041, 0.0041, 0.0041],
        [0.0043, 0.0042, 0.5042,  ..., 0.0041, 0.0041, 0.0041],
        ...,
        [0.0041, 0.0041, 0.0041,  ..., 0.5042, 0.0042, 0.0042],
        [0.0041, 0.0041, 0.0041,  ..., 0.0042, 0.5042, 0.0042],
        [0.0042, 0.0041, 0.0041,  ..., 0.0042, 0.0042, 0.5042]],
       dtype=torch.float64, grad_fn=<AddBackward0>)
Parameters:
likelihood.noise_covar.noise
tensor([0.5000], dtype=torch.float64)

mean_module.constant
Parameter containing:
tensor([0.], dtype=torch.float64, requires_grad=True)

base_covar_module.outputscale
tensor(0.6931, dtype=torch.float64)

base_covar_module.base_kernel.lengthscale
tensor([[0.6931, 0.6931]], dtype=torch.float64)

covar_module.inducing_points
Parameter containing:
tensor([[ 0.0521, -0.6976],
        [ 0.0632, -0.4839],
        [ 0.2233,  0.3943],
        [ 0.1918, -0.7864],
        [ 0.7498,  0.8417],
        [-0.8966,  0.8190],
        [-0.8619,  0.4701],
        [-0.0591, -0.0799],
        [-0.4869,  0.7875],
        [ 0.1616, -0.0695],
        [-0.0354,  0.7248],
        [ 0.9909, -0.5697],
        [-0.8870, -0.6765],
        [-0.5430, -0.8170],
        [-0.3609, -0.3869],
        [-0.0071,  0.2852]], dtype=torch.float64, requires_grad=True)


epoch=0/100 batch=0 loss=1.465227
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.6255, dtype=torch.float64)
torch.min(eigvals): tensor(0.4168, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=1/100 batch=0 loss=0.509937
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.0650, dtype=torch.float64)
torch.min(eigvals): tensor(0.0024, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=2/100 batch=0 loss=-1.449859
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.0025, dtype=torch.float64)
torch.min(eigvals): tensor(0.0006, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=3/100 batch=0 loss=-2.212336
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.0006, dtype=torch.float64)
torch.min(eigvals): tensor(0.0003, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=4/100 batch=0 loss=-2.671866
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.0003, dtype=torch.float64)
torch.min(eigvals): tensor(0.0002, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=5/100 batch=0 loss=-3.014810
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(0.0003, dtype=torch.float64)
torch.min(eigvals): tensor(0.0001, dtype=torch.float64)
all([e > 0 for e in eigvals]): True
covar:
 tensor([[1.2720e-04, 1.7792e-05, 1.3826e-05,  ..., 1.6445e-06, 1.6768e-06,
         1.6631e-06],
        [1.7791e-05, 1.1988e-04, 1.1284e-05,  ..., 1.3325e-06, 1.3585e-06,
         1.3472e-06],
        [1.3826e-05, 1.1284e-05, 1.1415e-04,  ..., 1.0328e-06, 1.0529e-06,
         1.0441e-06],
        ...,
        [1.6338e-06, 1.3218e-06, 1.0222e-06,  ..., 1.0594e-04, 5.9573e-07,
         6.0441e-07],
        [1.6661e-06, 1.3479e-06, 1.0424e-06,  ..., 5.9571e-07, 1.0598e-04,
         6.2799e-07],
        [1.6526e-06, 1.3367e-06, 1.0336e-06,  ..., 6.0437e-07, 6.2797e-07,
         1.0601e-04]], dtype=torch.float64, grad_fn=<AddBackward0>)
Parameters:
likelihood.noise_covar.noise
tensor([8.8240e-05], dtype=torch.float64)

mean_module.constant
Parameter containing:
tensor([0.0057], dtype=torch.float64, requires_grad=True)

base_covar_module.outputscale
tensor(0.6027, dtype=torch.float64)

base_covar_module.base_kernel.lengthscale
tensor([[1.2276, 1.1023]], dtype=torch.float64)

covar_module.inducing_points
Parameter containing:
tensor([[ 0.0765, -0.8154],
        [ 0.1267, -0.3993],
        [ 0.0757,  0.3193],
        [ 0.3583, -0.5108],
        [ 0.7529,  0.7354],
        [-0.8866,  0.8077],
        [-0.8409,  0.4318],
        [ 0.5946, -0.1004],
        [-0.4840,  0.7056],
        [ 0.9833, -0.4459],
        [-0.0277,  0.7254],
        [ 0.6723, -0.4268],
        [-0.7652, -0.6188],
        [-0.4834, -0.6123],
        [-0.4852, -0.6137],
        [-0.1640,  0.1421]], dtype=torch.float64, requires_grad=True)


epoch=6/100 batch=0 loss=-3.216315
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(0.0001, dtype=torch.float64)
torch.min(eigvals): tensor(8.4475e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=7/100 batch=0 loss=-3.349052
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(9.9394e-05, dtype=torch.float64)
torch.min(eigvals): tensor(7.5150e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=8/100 batch=0 loss=-3.450857
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(7.4041e-05, dtype=torch.float64)
torch.min(eigvals): tensor(6.7327e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=9/100 batch=0 loss=-3.533311
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(6.7669e-05, dtype=torch.float64)
torch.min(eigvals): tensor(6.0395e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=10/100 batch=0 loss=-3.603859
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(6.0371e-05, dtype=torch.float64)
torch.min(eigvals): tensor(5.4178e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=11/100 batch=0 loss=-3.656347
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(5.6204e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.5188e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=12/100 batch=0 loss=-3.706605
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(5.1626e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.6964e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=13/100 batch=0 loss=-3.747696
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(5.2272e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.1187e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=14/100 batch=0 loss=-3.779977
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(4.8281e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.0903e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=15/100 batch=0 loss=-3.819055
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(4.7312e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.2271e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=16/100 batch=0 loss=-3.848625
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(4.5553e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.3372e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=17/100 batch=0 loss=-3.870684
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(5.6592e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.1854e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=18/100 batch=0 loss=-3.891119
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(5.8252e-05, dtype=torch.float64)
torch.min(eigvals): tensor(4.1263e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=19/100 batch=0 loss=-3.909578
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(6.4548e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.9948e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=20/100 batch=0 loss=-3.934292
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(6.3655e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.8333e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=21/100 batch=0 loss=-3.961100
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.8688e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.6147e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=22/100 batch=0 loss=-3.979790
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.7945e-05, dtype=torch.float64)
torch.min(eigvals): tensor(2.4795e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=23/100 batch=0 loss=-3.999898
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.7241e-05, dtype=torch.float64)
torch.min(eigvals): tensor(1.5308e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=24/100 batch=0 loss=-4.025196
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.6644e-05, dtype=torch.float64)
torch.min(eigvals): tensor(2.0070e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=25/100 batch=0 loss=-4.050356
torch.allclose(covar, covar.T): True
torch.max(eigvals): tensor(7.6968e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.4462e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=26/100 batch=0 loss=-4.075860
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.4989e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.0445e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=27/100 batch=0 loss=-4.097970
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(5.3375e-05, dtype=torch.float64)
torch.min(eigvals): tensor(3.2858e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=28/100 batch=0 loss=-4.119830
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.3981e-05, dtype=torch.float64)
torch.min(eigvals): tensor(2.9871e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): True

epoch=29/100 batch=0 loss=-4.138579
torch.allclose(covar, covar.T): False
torch.max(eigvals): tensor(3.2372e-05, dtype=torch.float64)
torch.min(eigvals): tensor(-2.1481e-05, dtype=torch.float64)
all([e > 0 for e in eigvals]): False
covar:
 tensor([[ 3.0744e-05, -5.9116e-07, -6.0014e-07,  ..., -5.9368e-07,
         -5.9529e-07, -5.9687e-07],
        [-5.9130e-07,  3.0728e-05, -6.0373e-07,  ..., -5.9206e-07,
         -5.9108e-07, -5.9004e-07],
        [-6.0042e-07, -6.0388e-07,  3.0718e-05,  ..., -5.9150e-07,
         -5.8834e-07, -5.8509e-07],
        ...,
        [-6.4287e-07, -6.4132e-07, -6.4081e-07,  ...,  3.0723e-05,
         -5.7952e-07, -5.5667e-07],
        [-6.4501e-07, -6.4087e-07, -6.3817e-07,  ..., -5.8023e-07,
          3.0776e-05, -5.1833e-07],
        [-6.4710e-07, -6.4034e-07, -6.3544e-07,  ..., -5.5809e-07,
         -5.1904e-07,  3.0846e-05]], dtype=torch.float64,
       grad_fn=<AddBackward0>)
Parameters:
likelihood.noise_covar.noise
tensor([1.4199e-05], dtype=torch.float64)

mean_module.constant
Parameter containing:
tensor([0.0069], dtype=torch.float64, requires_grad=True)

base_covar_module.outputscale
tensor(0.6304, dtype=torch.float64)

base_covar_module.base_kernel.lengthscale
tensor([[1.5524, 1.4203]], dtype=torch.float64)

covar_module.inducing_points
Parameter containing:
tensor([[ 0.1540, -0.6542],
        [-0.1453, -0.4734],
        [-0.0165,  0.2547],
        [ 0.3614, -0.3993],
        [ 0.7173,  0.5495],
        [-0.8548,  0.7846],
        [-0.8305,  0.3709],
        [ 0.8371, -0.3482],
        [-0.4750,  0.5513],
        [ 1.2503, -0.7863],
        [-0.0201,  0.7434],
        [ 0.8170, -0.5308],
        [-0.8348, -0.6516],
        [-0.6655, -0.5397],
        [-0.7762, -0.7725],
        [-0.1686, -0.0699]], dtype=torch.float64, requires_grad=True)