Loss is negative and accuracy=0.006 when tried to prune anamoly detection

[MounikaVaddeboina]

I have used Tensorflow optimization toolkit to prune the benchmark anamoly_detection.

The procedure I have followed is same as shown in the below link.
https://www.tensorflow.org/model_optimization/guide/combine/pqat_example

The output during training is like this:
Epoch 2/100
2412/2412 [==============================] - 20s 8ms/step - loss: 11.1539 - val_loss: 11.1307
Epoch 3/100
2412/2412 [==============================] - 20s 8ms/step - loss: 10.6982 - val_loss: 10.6691
Epoch 4/100
2412/2412 [==============================] - 20s 8ms/step - loss: 10.4117 - val_loss: 10.5804
Epoch 5/100
2412/2412 [==============================] - 20s 8ms/step - loss: 10.2858 - val_loss: 10.2876
Epoch 6/100
2412/2412 [==============================] - 20s 8ms/step - loss: 10.1822 - val_loss: 10.2884
Epoch 7/100
2412/2412 [==============================] - 20s 8ms/step - loss: 10.1250 - val_loss: 10.2690
Epoch 8/100
2412/2412 [==============================] - 20s 8ms/step - loss: 10.0805 - val_loss: 10.3325

Output during pruning is like this:
Epoch 1/100
2680/2680 [==============================] - 47s 16ms/step - loss: -291053.6562 - accuracy: 0.0359
Epoch 2/100
2680/2680 [==============================] - 42s 16ms/step - loss: -287242.3438 - accuracy: 0.0335
Epoch 3/100
2680/2680 [==============================] - 42s 16ms/step - loss: -294022.2500 - accuracy: 0.0341
Epoch 4/100
2680/2680 [==============================] - 41s 15ms/step - loss: -301931.7188 - accuracy: 0.0336
Epoch 5/100
2680/2680 [==============================] - 41s 15ms/step - loss: -311050.6875 - accuracy: 0.0294
Epoch 6/100
2680/2680 [==============================] - 42s 15ms/step - loss: -321004.3750 - accuracy: 0.0241
Epoch 7/100
2680/2680 [==============================] - 41s 15ms/step - loss: -331941.9375 - accuracy: 0.0177
Epoch 8/100
2680/2680 [==============================] - 42s 16ms/step - loss: -343348.4688 - accuracy: 0.0109
Epoch 9/100
2680/2680 [==============================] - 42s 16ms/step - loss: -355611.3438 - accuracy: 0.0080
Epoch 10/100
2680/2680 [==============================] - 42s 16ms/step - loss: -368586.7812 - accuracy: 0.0073
Epoch 11/100
2680/2680 [==============================] - 42s 16ms/step - loss: -382228.8125 - accuracy: 0.0068
Epoch 12/100
2680/2680 [==============================] - 42s 16ms/step - loss: -396485.6875 - accuracy: 0.0066
Epoch 13/100
2680/2680 [==============================] - 42s 16ms/step - loss: -411286.4062 - accuracy: 0.0065
Epoch 14/100
2680/2680 [==============================] - 41s 15ms/step - loss: -426653.8750 - accuracy: 0.0061
Epoch 15/100
2680/2680 [==============================] - 41s 15ms/step - loss: -442495.9062 - accuracy: 0.0056
Epoch 16/100
2680/2680 [==============================] - 41s 15ms/step - loss: -458785.0625 - accuracy: 0.0049

Can you help me with this issue?

[cskiraly]

@MounikaVaddeboina I'm sorry but we haven't tried pruning so far, so it is difficult to say what could go wrong. If you fork the repo and commit your modified code in a branch, someone who already tried pruning the model might be able to spot the problem.