我有3D CNN U-net架构来解决分割问题。我正在将Adam优化与二进制交叉熵一起使用，度量标准是“准确性”。我试图了解为什么它没有改善。
Train on 2774 samples, validate on 694 samples
Epoch 1/20
2774/2774 [==============================] - 166s 60ms/step - loss: 0.5189 - acc: 0.7928 - val_loss: 0.5456 - val_acc: 0.7674
Epoch 00001: val_loss improved from inf to 0.54555, saving model to model-tgs-salt-1.h5
Epoch 2/20
2774/2774 [==============================] - 170s 61ms/step - loss: 0.5170 - acc: 0.7928 - val_loss: 0.5485 - val_acc: 0.7674
Epoch 00002: val_loss did not improve from 0.54555
Epoch 3/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5119 - acc: 0.7928 - val_loss: 0.5455 - val_acc: 0.7674
Epoch 00003: val_loss improved from 0.54555 to 0.54549, saving model to model-tgs-salt-1.h5
Epoch 4/20
2774/2774 [==============================] - 170s 61ms/step - loss: 0.5117 - acc: 0.7928 - val_loss: 0.5715 - val_acc: 0.7674
Epoch 00004: val_loss did not improve from 0.54549
Epoch 5/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5126 - acc: 0.7928 - val_loss: 0.5566 - val_acc: 0.7674
Epoch 00005: val_loss did not improve from 0.54549
Epoch 6/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5138 - acc: 0.7928 - val_loss: 0.5503 - val_acc: 0.7674
Epoch 00006: val_loss did not improve from 0.54549
Epoch 7/20
2774/2774 [==============================] - 170s 61ms/step - loss: 0.5103 - acc: 0.7928 - val_loss: 0.5444 - val_acc: 0.7674
Epoch 00007: val_loss improved from 0.54549 to 0.54436, saving model to model-tgs-salt-1.h5
Epoch 8/20
2774/2774 [==============================] - 169s 61ms/step - loss: 0.5137 - acc: 0.7928 - val_loss: 0.5454 - val_acc: 0.7674
答案 0 :(得分：0)
如果在网络中使用批次大小。让我们尝试增加它。我认为这对火车速度很有用。