Computing Time Interval Delta about gru-d HOT 4 OPEN

@zhiyongc Another thing, when computing x_last_obsv we need to make a copy of speed_squences array by using np.copy() because using assignment makes a reference.

(line 71 in main):
X_last_obsv = speed_sequences
should be:
X_last_obsv = np.copy(speed_sequences)

Many thanks to you for this great code, really helpful

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@zhiyongc When you generate Mask, Delta, and Last_observed_X in the code, you shuffle them twice, while you shuffle speed_sequences and speed_label once.
The first time when you create them and the second time when you expand their dimension as shown in the following

if masking:
        X_last_obsv = X_last_obsv[index] #---->   1st time
        Mask = Mask[index] #----> 1st time
        Delta = Delta[index] #-----> 1st time
        speed_sequences = np.expand_dims(speed_sequences, axis=1)
        X_last_obsv = np.expand_dims(X_last_obsv[index], axis=1) #---> 2nd time
        Mask = np.expand_dims(Mask[index], axis=1) #------> 2nd time
        Delta = np.expand_dims(Delta[index], axis=1) #-------> 2nd time
        dataset_agger = np.concatenate((speed_sequences, X_last_obsv, Mask, Delta), axis = 1)

So, I think we need to shuffle them once also

if masking:
        X_last_obsv = X_last_obsv[index]
        Mask = Mask[index]
        Delta = Delta[index]
        speed_sequences = np.expand_dims(speed_sequences, axis=1)
        X_last_obsv = np.expand_dims(X_last_obsv, axis=1)
        Mask = np.expand_dims(Mask, axis=1)
        Delta = np.expand_dims(Delta, axis=1)
        dataset_agger = np.concatenate((speed_sequences, X_last_obsv, Mask, Delta), axis = 1)

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Hi @DeepWolf90,
I agree with you in terms of the issues you posted. All the three issues are fixed in the new version of the code. As I tested, the prediction accuracy improved by using the updated code.
Thanks again for your kind help and the comprehensive descriptions of the issues!

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@zhiyongc Sorry if the following question sounds naive, but I'm new to pytorch

In forward function, line 144:
outputs = None

Based on my understanding you don't apply any prediction layer you just used the hidden state to compute the loss.

why don't you use a linear activation fully connected layer as a prediction layer?

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