Categoria:Praefecti urbi Romae recentes
Pro officio antiquo vide Categoria:Praefecti urbi Romae antiqui.
| Millennia | Saecula | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| millennio 2 | saec. 20 | |||||||||
| 2 | 2 | |||||||||
| Homines vivi | ||||||||||
| 5 | ||||||||||
LSTM sequence prediction: 3d input to 2d output
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
