I use CNN for semantic segmentation, create 4 output branches, and customize the loss function.
I'm using the following versions:
- Keras 2.2.4
- Python 3.6.8
- tensorflow 1.12.0
When I training,acc and val_acc are not displayed on the progress bar,But loss and val_loss are normally displayed.
436/436 [==============================] - 160s 367ms/step - loss: 0.4628 - output_0_loss: 0.0351 - output_1_loss: 0.0698 - output_2_loss: 0.1572 - output_3_loss: 0.2008 - output_0_acc: 0.6728 - output_1_acc: 0.6944 - output_2_acc: 0.8087 - output_3_acc: 0.7010 - val_loss: 0.3001 - val_output_0_loss: 0.0145 - val_output_1_loss: 0.0261 - val_output_2_loss: 0.1511 - val_output_3_loss: 0.1083 - val_output_0_acc: 0.9742 - val_output_1_acc: 0.8642 - val_output_2_acc: 0.7887 - val_output_3_acc: 0.8423
My loss function is binary_crossentropy,I use the metric is ['accuracy']:
model.compile(loss=my_binary_crossentropy,
optimizer=optimizer_name,
metrics=['accuracy'])
The acc and val_acc in history is None:
epoch-loss:[0.44138801848175424, 0.31117319703451685] epoch-acc:[None, None] epoch-val_loss:[0.2681478185099933, 0.21369548345525233] epoch-val_acc:[None, None]
How should I deal with this problem or where to debug it?
output_X_acc
andval_output_X_acc
, which do provide the required accuracies per output (see also answer below). – desertnaut