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Hi, I appreciate your job for HAR. I firstly read your paper and then google your DeepConvLSTM. I fork your repo, and want to reproduction your result. But there is some question I did not get you. In DeepConvLSTM.ipynb, why are you just slide window for test data, but not for traning data?
It is locate in # Sensor data is segmented using a sliding window mechanism X_test, y_test = opp_sliding_window(X_test, y_test, SLIDING_WINDOW_LENGTH, SLIDING_WINDOW_STEP) print(" ..after sliding window (testing): inputs {0}, targets {1}".format(X_test.shape, y_test.shape))
I look forward to your reply. And I will give an acknowledgement in my paper for your help.
The text was updated successfully, but these errors were encountered:
The notebook comprises only classification results, therefore there is not reference to the training data. Instead the model presented uses the parameters included in weights/DeepConvLSTM_oppChallenge_gestures.pkl (network weights). To take a look at the training code you can go to #1 . The training data is segmented exactly as the test data.
Hi, I appreciate your job for HAR. I firstly read your paper and then google your DeepConvLSTM. I fork your repo, and want to reproduction your result. But there is some question I did not get you. In DeepConvLSTM.ipynb, why are you just slide window for test data, but not for traning data?
It is locate in
# Sensor data is segmented using a sliding window mechanism X_test, y_test = opp_sliding_window(X_test, y_test, SLIDING_WINDOW_LENGTH, SLIDING_WINDOW_STEP) print(" ..after sliding window (testing): inputs {0}, targets {1}".format(X_test.shape, y_test.shape))
I look forward to your reply. And I will give an acknowledgement in my paper for your help.
The text was updated successfully, but these errors were encountered: