Negative loss value

[st-tomic]

Hi,

I am trying to run the sample training on a librispeech clean 100h.

After few hours of training with batch size=10 the printed loss value becomes negative.
It happens in the first epoch.

The thing i changed is read_audio function to use soundfile for reading flac files insted of waves with wavfile.read. Although both give the same output when reading files so it shouldn't make a difference.

Are you familiar with the issue? Loss seems to decrease too fast.
Any guess what is going wrong?

[rolczynski]

Hey! Super weird. Could you provide more details?

[st-tomic]

I agree :)

The only changes are stated above. Used pipeline in basic.py and readme page.
I have also tried it with tfv2.0-GPU and the loss is also decreasing extremely fast within 1st epoch which doesn't seem real.

I only modified audio loading part to use soundfile and used future-fstrings to support f strings on Python 3.5.
Other than that, nothing is changed from your repo.

The loss is frozen at -0.6921 in the last try.

Data loaded from librispeech:

dataset = asr.dataset.Audio.from_csv('examples/libri-100.csv', batch_size=10)
dev_dataset = asr.dataset.Audio.from_csv('examples/dev-clean.csv', batch_size=10)
test_dataset = asr.dataset.Audio.from_csv('examples/test-clean.csv')

[HunbeomBak]

i have a same problem.

i tried training my dataset.

`
You do not need to update to CUDA 9.2.88; cherry-picking the ptxas binary is sufficient.
475/476 [============================>.] - ETA: 1s - loss: 2.6582

476/476 [==============================] - 802s 2s/step - loss: 2.6511 - val_loss: -0.6931
Epoch 2/5
119/476 [======>.......................] - ETA: 5:47 - loss: -0.6931
`

After first epoch, val_loss was negative.

and second epoch also had negative loss.
the loss does not change, and remains at -0.6931 in second epoch.

i use your environment-gpu.yml for creating a conda environments.

my english skill is bad, but i did my best.

[ValeriyMarkin]

Hi, I had the same problem. For me, it turned out that the pipeline.fit() method returns an empty string instead of a correct transcript, so a model learns to predict it. I used a following code and it works:

dataset =pipeline.wrap_preprocess(dataset, False, None)
y = tf.keras.layers.Input(name='y', shape=[None], dtype='int32')
loss = pipeline.get_loss()
pipeline._model.compile(pipeline._optimizer, loss, target_tensors=[y])
pipeline._model.fit(dataset,epochs=20)

[askinucuncu]

I guess there is no improvement in this regard. Because the system still produces negative values.

[wenjingyang]

I had similar issue even if I just tried example(basic.py) on tf v2.1. I think the negative loss value might be acceptable. keras-team/keras#9369

But when I used the predict to predict the test.csv (same as training file), the output is empty.['']. It doesn't look reasonable now.
code in basic.py : pipeline.predict(data)

Epoch 1/5
1/1 [==============================] - 3s 3s/step - loss: 303.5161
1/1 [==============================] - 19s 19s/step - loss: 610.6132 - val_loss: 303.5161
Epoch 2/5
Epoch 1/5
1/1 [==============================] - 1s 1s/step - loss: 61.1079
1/1 [==============================] - 7s 7s/step - loss: 76.0996 - val_loss: 61.1079
Epoch 3/5
Epoch 1/5
1/1 [==============================] - 1s 1s/step - loss: 9.9619
1/1 [==============================] - 7s 7s/step - loss: 4.4410 - val_loss: 9.9619
Epoch 4/5
Epoch 1/5
1/1 [==============================] - 1s 1s/step - loss: 2.4088
1/1 [==============================] - 7s 7s/step - loss: 0.6229 - val_loss: 2.4088
Epoch 5/5
Epoch 1/5
1/1 [==============================] - 1s 1s/step - loss: 0.5115
1/1 [==============================] - 7s 7s/step - loss: -0.3944 - val_loss: 0.5115