Average loss over epoch 1: 0.1931264997 Average loss over epoch 2: 0.1047099456 Average loss over epoch 3: 0.0877031833 Average loss over epoch 4: 0.0859238505 Average loss over epoch 5: 0.0797647089 Average loss over epoch 6: 0.0749474317 Average loss over epoch 7: 0.0707892254 Average loss over epoch 8: 0.0699648410 Average loss over epoch 9: 0.0642149597 Average loss over epoch 10: 0.0567317083 Average loss over epoch 11: 0.0510864668 Average loss over epoch 12: 0.0442583747 Average loss over epoch 13: 0.0466077179 Average loss over epoch 14: 0.0384134278 Average loss over epoch 15: 0.0315162912 Average loss over epoch 16: 0.0257765315 Average loss over epoch 17: 0.0226529166 Average loss over epoch 18: 0.0192399584 Average loss over epoch 19: 0.0170757137 Average loss over epoch 20: 0.0146231744 tensor([ 1.4250e-01, 8.5307e-02, -1.7114e-02, 9.7782e-01, 9.6013e-01, 8.8191e-01, 1.6054e-02, 9.8698e-01, 9.8728e-01, 6.1431e-02, 3.0031e-02, 9.8349e-01, 9.8461e-01, 2.0448e-02, 9.7779e-01, 9.8663e-01, 1.4332e-01, 9.6214e-01, 3.1209e-02, 1.9827e-02, 9.4560e-01, 7.1624e-02, 9.5283e-01, 1.0946e-01, 3.6064e-02, 8.3764e-02, 3.8745e-02, 2.4623e-02, 9.5010e-01, 9.6710e-01, 2.1637e-01, 4.7736e-02, 1.6094e-01, 1.6014e-02, 9.5201e-01, 7.1560e-01, 9.6889e-01, 9.6905e-01, 9.8388e-01, 1.7154e-02, 2.2611e-02, 9.5295e-01, -5.5325e-04, 4.4402e-04, 9.3434e-01, 9.5940e-01, -1.5981e-02, 3.7433e-02, 9.6443e-01, 4.7762e-02, 8.7402e-01, 9.8732e-01, 3.5539e-01, 9.5397e-01, 8.9528e-01, 1.4730e-02, 9.6775e-01, 2.0069e-01, 9.8729e-01, 1.6074e-01, 9.8670e-01, 1.8014e-01, -1.7564e-02, 1.3383e-01, -1.2588e-02, 9.0992e-01, 2.8477e-02, 9.7062e-01, 7.1744e-02, 9.6907e-01, 1.6271e-01, 9.5729e-01, 9.8249e-01, 1.9738e-02, 9.0089e-01, 8.6523e-01, -8.3638e-03, 9.6839e-01, 6.2789e-01, 8.4011e-01, 4.5536e-01, 1.8631e-02, 7.4860e-02, 9.7570e-01, 9.6808e-01, 4.3066e-02, 9.8159e-01, -9.6172e-03, 9.6944e-01, 7.1470e-02, 1.2516e-02, 9.6689e-01, 6.5276e-01, -1.6437e-02, 1.5261e-01, 9.6202e-01, 9.7928e-01, 3.2031e-01, -1.1624e-02, 9.1926e-01, 3.8975e-01, -1.7338e-02, 9.6579e-01, 9.6086e-01, 8.0801e-01, 9.6280e-01, 1.8201e-01, 6.0152e-02, 8.5455e-01, 9.5858e-01, 9.8529e-01, 5.5160e-01, 9.4838e-01, 9.6608e-01, 1.5243e-02, 9.4039e-01, 4.2736e-02, 4.9263e-02, 9.8325e-01, -1.4157e-02, 1.2023e-02, 7.9077e-01, 9.7189e-01, 9.7417e-01, -4.3507e-03, 6.0538e-03, 9.8112e-01, 9.5592e-01, 2.7987e-02, 5.2768e-03, 9.8633e-01, 8.9608e-02, 1.2541e-01, 9.6925e-01, 3.8299e-02, 9.6882e-01, 4.2315e-01, 7.3514e-01, 1.9419e-01, 9.6792e-01, 9.8511e-01, 9.8623e-01, 9.7958e-01, 1.2326e-01, 2.5122e-02, -1.4857e-02, 2.8561e-01, 8.2447e-01, 8.1088e-02, 3.4794e-02, 9.8694e-01, 9.8437e-01, 6.4135e-02, 2.5101e-01, 1.1637e-01, 9.7067e-02, 9.8717e-01, 9.6605e-01, 5.9117e-02, 1.4578e-02, 9.8715e-01, 9.6851e-01, 9.8586e-01, 9.8243e-01, 5.0061e-02, 1.7968e-01, 7.7282e-01, 1.4225e-01, 9.5643e-01, 1.1141e-01, 1.0573e-01, 5.8293e-02, 1.9870e-02, 1.2682e-01, 9.8052e-01, 9.6637e-01, 9.6254e-01, -5.5157e-03, 9.7613e-01, 9.6434e-01, 5.0389e-01, 1.8467e-01, 9.4926e-01, 9.6459e-01, 9.5752e-01, 6.9572e-01, 2.2238e-01, 8.8897e-01, 5.2837e-02, 1.5207e-02, 9.2735e-01, 9.4305e-01, 2.8161e-02, 9.7326e-01, 1.4751e-02, 7.5425e-01, 1.0021e-01, 9.4649e-02, 9.8573e-01, 6.7561e-01], grad_fn=) Accuracy: 99.0% Runtime: 2.57e+01 s or 4.28e-01 min.