Epoch 0 - Loss: 53.2891 Epoch 100 - Loss: 3.8639 Epoch 200 - Loss: 1.2792 Epoch 300 - Loss: 1.0363 Epoch 400 - Loss: 0.8841 Epoch 500 - Loss: 0.7888 Epoch 600 - Loss: 0.7177 Epoch 700 - Loss: 0.6763 Epoch 800 - Loss: 0.6633 Epoch 900 - Loss: 0.6511 Epoch 1000 - Loss: 0.6441 Epoch 1100 - Loss: 0.6383 Epoch 1200 - Loss: 0.6335 Epoch 1300 - Loss: 0.6292 Epoch 1400 - Loss: 0.6241 Epoch 1500 - Loss: 0.6200 Epoch 1600 - Loss: 0.6169 Epoch 1700 - Loss: 0.6141 Epoch 1800 - Loss: 0.6112 Epoch 1900 - Loss: 0.6089 Test Loss: 0.9853

这段代码是一个训练了2000个epochs的神经网络模型，每100个epochs打印一次训练集上的损失值。根据打印出来的信息，可以看出随着训练的进行，损失值逐渐减小，而且训练了更多的epochs，损失值下降的更为明显。最后在测试集上的均方误差损失值为0.9853，说明该模型在测试集上的预测效果比上一个模型更好。