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it looks like to me that they use this structure for decoding the lstm:
Dense(100) -> tanh -> Dense(n_classes) -> softmax
while you implemented in the function _decode_lstm:
Dense(n_classes) -> tanh -> Dense(n_classes) -> softmax
I think that the first, hidden fully connected layer has too few neurons, and that is it different from the original implementation.
The text was updated successfully, but these errors were encountered:
With reference to the original implementation
https://github.com/kracwarlock/action-recognition-visual-attention/blob/6738a0e2240df45ba79e87d24a174f53adb4f29b/src/actrec.py#L111
it looks like to me that they use this structure for decoding the lstm:
Dense(100) -> tanh -> Dense(n_classes) -> softmax
while you implemented in the function _decode_lstm:
Dense(n_classes) -> tanh -> Dense(n_classes) -> softmax
I think that the first, hidden fully connected layer has too few neurons, and that is it different from the original implementation.
The text was updated successfully, but these errors were encountered: