Comments (9)
Btw, since you already manually called watch_keras_model
, you don't have to pass it again to create_keras_callback
, so keep this line like that:
deepkit_callback = experiment.create_keras_callback()
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This is nice - the fix works! And the GUI is beautiful! Keras version 2.4.3. Btw, how is the Python package release schedule? I would like to run containerised, so the library will be downloaded on each build. The fix would be nice to get pushed to the registry.
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I suspect the network is the problem, but I think it should be supported:
n_input = 12
n_features = 1
generator = TimeseriesGenerator(train, train, length=n_input, batch_size=6)
model = Sequential()
model.add(LSTM(200, activation='relu', input_shape=(n_input, n_features)))
model.add(Dropout(0.15))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
experiment.watch_keras_model(model)
deepkit_callback = experiment.create_keras_callback(model)
model.fit(generator,epochs=90, callbacks=[deepkit_callback])
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Mh, yes seems so. It can not automatically infer the input for the debugger. You can try to work around that by providing manually the model input.
experiment.watch_keras_model(model, model_input=x, is_batch=True)
is_batch
needs to be True or False, depending on your network structure. model_input
is the input (batched or a simple entry) of your network input aka x
.
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Since you have a generator, you could try
experiment.watch_keras_model(model, model_input=next(iter(generator)), is_batch=True)
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This seems to be an improvement, but when I combine and do:
experiment.watch_keras_model(model, model_input=next(iter(generator)), is_batch=True)
deepkit_callback = experiment.create_keras_callback()
model.fit(generator,epochs=90,callbacks=[deepkit_callback])
the program throws the exception:
File "/usr/local/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 601, in on_train_batch_begin
self.on_batch_begin(batch, logs=logs)
File "/usr/local/lib/python3.8/site-packages/deepkit/deepkit_keras.py", line 117, in on_batch_begin
batch_size = logs['size']
KeyError: 'size'
Ended task main # 0 exitCode 1
If I omit the callback, the script runs with the generator, however it would be nice to get real time updates and accuracy reports etc. Any tips?
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Which Keras version do you use? It seems it does not report the size
log entry in its on_batch_begin
hook.
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I pushed a change to master which should fix this issue. Please try it (by installing master on your computer or simply copy&paste its file content). When you provide me a reproduction code I can integrate in a unit test.
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I just released 1.0.8
with the fix. Thanks!
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Related Issues (20)
- Naming of "Default" experiment list HOT 1
- Scalar plots are empty, log_scalar values only appear on experiment overview HOT 3
- Project name of "deepkit" changes nested directory behavior HOT 4
- Invalid yaml causes GUI to freeze HOT 1
- Experiment log output cannot be copied on Windows
- Docker run error HOT 5
- deepkit link error HOT 2
- Docker build fails HOT 1
- Job Failure HOT 2
- Automatic provisioning of VM instances HOT 7
- Docker Mounts HOT 4
- Passing additional options to docker
- Links in README are no longer working
- How about the project activity state? HOT 2
- [Feature request] Distributed training HOT 1
- please windows version ? HOT 6
- Is it possible to run this without docker installed on the server? HOT 2
- Is this alive? HOT 1
- Is it possible to download ? HOT 1
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