target distribution in each batch is now approximately equal, but varying learning rate doesn’t help either.
Still, with each epoch I get accuracy 49-50%. Predictions look like random numbers at each epoch
data tensor([[ 0.1041, -0.1237, -0.2148, …, -0.4751, -0.4867, -0.4756],
[ 0.1770, 0.2372, 0.1080, …, -0.6973, -0.7080, -0.6980],
[-1.0250, -0.9747, -1.1029, …, 1.8648, 1.8507, 1.8675],
…,
[-0.8286, -0.8282, -1.0329, …, 0.7890, 0.8069, 0.7968],
[-0.5453, -0.4008, -0.4164, …, 0.2117, 0.2365, 0.2151],
[ 1.2337, 1.2667, 1.1302, …, -0.3912, -0.3921, -0.3910]])
target tensor([1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,
1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,
1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0])
pred tr tensor([0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 1, 0, 0])