How to look at the parameters of a pytorch model?
I have a simple pytorch neural net that I copied from openai, and I modified it to some extent (mostly the input).
When I run my code, the output of the network remains the same on every episode, as if no training occurs.
I want to see if any training happens, or if some other reason causes the results to be the same.
How can I make sure any movement happens to the weights?
Thanks
machine-learning neural-network pytorch openai-gym
asked Jan 18 at 18:57
GulzarGulzar
8371820
add a comment |
I have a simple pytorch neural net that I copied from openai, and I modified it to some extent (mostly the input).
When I run my code, the output of the network remains the same on every episode, as if no training occurs.
I want to see if any training happens, or if some other reason causes the results to be the same.
How can I make sure any movement happens to the weights?
Thanks
machine-learning neural-network pytorch openai-gym
asked Jan 18 at 18:57
GulzarGulzar
8371820
add a comment |
I have a simple pytorch neural net that I copied from openai, and I modified it to some extent (mostly the input).
When I run my code, the output of the network remains the same on every episode, as if no training occurs.
I want to see if any training happens, or if some other reason causes the results to be the same.
How can I make sure any movement happens to the weights?
Thanks
machine-learning neural-network pytorch openai-gym
asked Jan 18 at 18:57
GulzarGulzar
8371820
I have a simple pytorch neural net that I copied from openai, and I modified it to some extent (mostly the input).
When I run my code, the output of the network remains the same on every episode, as if no training occurs.
I want to see if any training happens, or if some other reason causes the results to be the same.
How can I make sure any movement happens to the weights?
Thanks
machine-learning neural-network pytorch openai-gym
machine-learning neural-network pytorch openai-gym
asked Jan 18 at 18:57
GulzarGulzar
8371820
asked Jan 18 at 18:57
GulzarGulzar
8371820
edited Jan 18 at 19:17
Gulzar
asked Jan 18 at 18:57
GulzarGulzar
8371820
asked Jan 18 at 18:57
GulzarGulzar
8371820
asked Jan 18 at 18:57
GulzarGulzar
8371820
8371820
add a comment |
add a comment |
1 Answer
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Depends on what you are doing, but the easiest would be to check weights of your model.
You can check them really easily (and compare with the ones from previous iteration) using this code:
for parameter in model.parameters():
print(parameter.data)
If the weights are changing, the neural network is being optimized (which doesn't necessarily mean it learns anything useful in particular).
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Depends on what you are doing, but the easiest would be to check weights of your model.
You can check them really easily (and compare with the ones from previous iteration) using this code:
for parameter in model.parameters():
print(parameter.data)
If the weights are changing, the neural network is being optimized (which doesn't necessarily mean it learns anything useful in particular).
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
add a comment |
Depends on what you are doing, but the easiest would be to check weights of your model.
You can check them really easily (and compare with the ones from previous iteration) using this code:
for parameter in model.parameters():
print(parameter.data)
If the weights are changing, the neural network is being optimized (which doesn't necessarily mean it learns anything useful in particular).
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
add a comment |
Depends on what you are doing, but the easiest would be to check weights of your model.
You can check them really easily (and compare with the ones from previous iteration) using this code:
for parameter in model.parameters():
print(parameter.data)
If the weights are changing, the neural network is being optimized (which doesn't necessarily mean it learns anything useful in particular).
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
Depends on what you are doing, but the easiest would be to check weights of your model.
You can check them really easily (and compare with the ones from previous iteration) using this code:
for parameter in model.parameters():
print(parameter.data)
If the weights are changing, the neural network is being optimized (which doesn't necessarily mean it learns anything useful in particular).
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
answered Jan 18 at 20:29
Szymon MaszkeSzymon Maszke
4648
4648
add a comment |
add a comment |
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