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No train metrics show up in scalar plots #2911
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I tried this a few more times. It looks like tensorboard 2.0 has trouble updating the train metrics by itself. If I kill tensorboard and restart it, it will then show both train and validation metrics. If the training is still ongoing, the validation metrics will be updated where as the train metrics are stuck. |
@zzb3886, Can you please provide more details about your issue.
Tensorflow Graphs get updated from the Event Files stored during Training. So, it is recommended to see and analyze the graphs after the Training is completed, rather than during Training. Please let me know your opinion about the same. |
I'm encountering the same problem. The train scaler isn't updated until TensorBoard is restarted. |
In the script, if the tensorboard is started before training is started, then the problem occurs. |
If I restart tensorboard during the training, the metrics get updated once,
but the problem persists.
…On Thu, Nov 14, 2019, 1:04 PM zzb3886 ***@***.***> wrote:
In the script, if the tensorboard is started before training is started,
then the problem occurs.
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Probably a duplicate of #2084; can you please try the workaround listed |
Thanks for the report. I can confirm this was working with So it seems Keras callbacks have been affected by this bug since March, sadly. I'm going to triage this to someone who knows more context. Googlers, see cl/237090182 |
Adding profile_batch=0 to the keras callback resolves it. |
Duplicate of #2084 |
I have the following code and I can't manage to get tensorboard to show my anything else but epoch_accuracy and epoch_loss. Can anyone help me? i have followed the steps above and is still not working. This is the command I run in terminal tensorboard --logdir='logs/' import tensorflow as tf X = pickle.load(open("X.pickle","rb")) X=np.array(X/255.0) dense_layers = [0, 1, 2] for dense_layer in dense_layers: model = Sequential() for l in range(conv_layer-1): model.add(Flatten()) model.add(Dense(64)) model.add(Dense(1)) model.compile(loss="binary_crossentropy", model.fit(X, y, batch_size=7, epochs=2, validation_split=0.5, callbacks=[tensorboard]) |
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Issue description
I just upgraded tensorflow to 2.0. In training, I noticed tensorboard now has two runs for each experiment, including train and validation. However, only validation has scalar value curves. Train metric plots are always empty.
I can reproduce this issue by using the script in tensorboard get started guide: https://www.tensorflow.org/tensorboard/get_started. The script prints out reasonable train and val metrics as it should, but I'm just not getting the right plots.
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