Basma BOUSSAHA

@devm2024 @husky-parul The error is in line for seq_index in [14077,20122,40035,40064, 40056, 40068, 40090, 40095, 40100, 40119, 40131, 40136, 40150, 40153]: The indices are out of range, try with indices...

@ShellingFord221 @zw76859420 The error is in line for seq_index in [14077,20122,40035,40064, 40056, 40068, 40090, 40095, 40100, 40119, 40131, 40136, 40150, 40153]: The indices are out of range, try with indices...

Here is a small code `` seq2seq = Sequential() # my turn shape=(None, MAX_SEQUENCE_LENGTH) seq2seq.add(Embedding(output_dim=args.emb_dim, input_dim=MAX_NB_WORDS, input_length=MAX_SEQUENCE_LENGTH, weights=[embedding_matrix], mask_zero=True, trainable=True)) seq2seq.add(LSTM(units=args.hidden_size, return_sequences=True)) seq2seq.add(AttentionDecoder(args.hidden_size, args.emb_dim)) # the decoded shape=(None, MAX_SEQUENCE_LENGTH, args.emb_dim)...

No sure no, this is what is actually been done inside. It's just to explain but you can ignore it ..

No, but can you give me more hints please ?

Well I think the problem was not clear, but actually now I found a way to transform my decoder output to classes instead of probabilities using argmax. So now my...

> You willl need to convert your predicted labels into one hot vectors representing the class And how can I do this ?

@phenotypic I have the same issue because BSSID is NONE