模型驗證
Python 深度學習入門
Dan Becker
Data Scientist and contributor to Keras and TensorFlow libraries
深度學習中的驗證
常用驗證切分，而非交叉驗證
深度學習多用於大型資料集
單一驗證分數基於大量資料，可信度高
交叉驗證需重複訓練，耗時
模型驗證
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(predictors, target, validation_split=0.3)
Epoch 1/10
89648/89648 [=====] - 3s - loss: 0.7552 - acc: 0.5775 - val_loss: 0.6969 - val_acc: 0.5561
Epoch 2/10
89648/89648 [=====] - 4s - loss: 0.6670 - acc: 0.6004 - val_loss: 0.6580 - val_acc: 0.6102
...
Epoch 8/10
89648/89648 [=====] - 5s - loss: 0.6578 - acc: 0.6125 - val_loss: 0.6594 - val_acc: 0.6037
Epoch 9/10
89648/89648 [=====] - 5s - loss: 0.6564 - acc: 0.6147 - val_loss: 0.6568 - val_acc: 0.6110
Epoch 10/10
89648/89648 [=====] - 5s - loss: 0.6555 - acc: 0.6158 - val_loss: 0.6557 - val_acc: 0.6126
提前停止（Early Stopping）
from tensorflow.keras.callbacks import EarlyStopping
early_stopping_monitor = EarlyStopping(patience=2)
model.fit(predictors, target, validation_split=0.3, epochs=20,
callbacks = [early_stopping_monitor])
提前停止的輸出
Train on 89648 samples, validate on 38421 samples
Epoch 1/20
89648/89648 [====] - 5s - loss: 0.6550 - acc: 0.6151 - val_loss: 0.6548 - val_acc: 0.6151
Epoch 2/20
89648/89648 [====] - 6s - loss: 0.6541 - acc: 0.6165 - val_loss: 0.6537 - val_acc: 0.6154
...
Epoch 8/20
89648/89648 [====] - 6s - loss: 0.6527 - acc: 0.6181 - val_loss: 0.6531 - val_acc: 0.6160
Epoch 9/20
89648/89648 [====] - 7s - loss: 0.6524 - acc: 0.6176 - val_loss: 0.6513 - val_acc: 0.6172
Epoch 10/20
89648/89648 [====] - 6s - loss: 0.6527 - acc: 0.6176 - val_loss: 0.6549 - val_acc: 0.6134
Epoch 11/20
89648/89648 [====] - 6s - loss: 0.6522 - acc: 0.6178 - val_loss: 0.6517 - val_acc: 0.6169
實驗與調參
- 嘗試不同網路架構
- 增加層數
- 減少層數
- 單層更多節點
- 單層更少節點
- 要打造好模型需不斷試驗
一起來練習吧！
Python 深度學習入門
