Steering model does not generalize

[johnyboyoh]

Hi,
I was able to train the model using the given code and data. The loss on the training data converges to ~350 however it appears that on Dev data the loss remains high (around 3500). This implies model overfitting and failure to generalize.
Am I doing something wrong or are these the expected results using your code+data?

[johnyboyoh]

Did not try to train it. This thread is about the steering model not the generative model

…

On Mar 1, 2017 3:59 PM, "zhaozhao" ***@***.***> wrote: hello,i meet some difficulties，i can't train train_generative_model.py autoencoder successful，I want to know how you train the model ? Do you need to change this code?thanks very much — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#42 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AOYw20d9MXifFDBsvR7pu1c08oefBG6Kks5rhXnagaJpZM4MJxKi> .

[ErlendFax]

Hi,
I know I'm a bit late but I have the same problem. Did you get any further on this? Really appreciate any suggestions.

[ghost]

Hello there,
Any word on this? I'm having the same problem and I tried tweaking the model and nothing would work, the validation loss starts a lot higher than the training loss for some reason and does not go down.

[ErlendFax]

Sorry for late reply,

The network is probably working as intended (checked it with my professor), but my guess is that the data is corrupt. Maybe you could make a script that previews the images before they are fed into the network?

[ghost]

Thanks for replying,

So you are still facing the same problem? I also have a feeling that there's something wrong with the validation data since its loss is way higher.

[ErlendFax]

I actually gave up. Can’t remember the different losses but you might be right. Maybe make your own dataset from KITTI?

[ghost]

I will look into that, thanks a lot.

[ErlendFax]

By the way @MahmoudKhaledAli , since this is a regression problem with MSE as a loss function, it makes more sense to use a linear activation function in the last layer.

Just to check, try removing the last model.add(ELU()) and replace model.add(Dense(512)) with model.add(Dense(512, activation='linear')).

[ghost]

I already tried setting the activation to None, but I will definitely give that a go, thanks for your help.

[ahmedyahia3393]

in the view steering model.py file
I found his error (ValueError: bad marshal data (unknown type code)) result when trying to execute the view steering model.py
here is the result from the cmd prompt

Traceback (most recent call last):
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 229, in func_load
raw_code = codecs.decode(code.encode('ascii'), 'base64')
UnicodeEncodeError: 'ascii' codec can't encode character '\xe0' in position 46:
ordinal not in range(128)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "view_steering_model.py", line 94, in
model = model_from_json(json.load(jfile))
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\models.py", line 349,
in model_from_json
return layer_module.deserialize(config, custom_objects=custom_objects)
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\layers_init_.py", l
ine 55, in deserialize
printable_module_name='layer')
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\models.py", line 1349,
in from_config
layer = layer_module.deserialize(conf, custom_objects=custom_objects)
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\layers_init_.py", l
ine 55, in deserialize
printable_module_name='layer')
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\layers\core.py", line
711, in from_config
function = func_load(config['function'], globs=globs)
File "C:\Users\lenovo\Anaconda3\lib\site-packages\keras\utils\generic_utils.py
", line 234, in func_load
code = marshal.loads(raw_code)
ValueError: bad marshal data (unknown type code)

[ghost]

Yeah I had the same problem, so I went ahead and re-wrote the graph myself and then loaded the weights from the .json file, however the mse is still very high.