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I have been using LSTM multi-output Neural Nets to perform two tasks, regression coupled with a classification. The data is in a time-series format where my dependent variable is trade quantity between nations as well as an indicator as to whether they trade at all. The problem I'm running into, is that my network predicts only a single value for the regression (~0.36), which is not the mean for the training data, and only 1's (100% probability for each sample) for the classification.

The normalised data distributions for training, validation and testing look like this Blue = Training, Red = Validation, Green = Testing

For the classification problem there is about a 5:1 ratio of 1's to 0's but nothing excessively imbalanced. My network setup looks like this, but I would usually pass it through a genetic algorithm for hyper parameter tuning.

callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3, min_delta=1e-9)

inputs = keras.layers.Input(shape=(x_train.shape[1], x_train.shape[2]))
layer = LSTM(128, input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True)(inputs)
layer = keras.layers.Dropout(0.2)(layer)
layer = LSTM(64, return_sequences=False)(layer)
layer = keras.layers.Dropout(0.2)(layer)
layer = Dense(32, activation='relu')(layer)

partner_layer = Dense(1, activation='softmax', name='partner_pred')(layer)
trade_layer = Dense(1, name='trade_pred')(layer)  # No activation due to regression, possible sigmoid as values are between 0 and 1.

model = keras.models.Model(inputs, [partner_layer, trade_layer])

model.compile(loss={'trade_pred': 'mse', 'partner_pred': 'binary_crossentropy'}, optimizer=Adam(learning_rate=0.009),
              loss_weights={'trade_pred': 1, 'partner_pred': 10},
              metrics={'trade_pred': 'mse', 'partner_pred': 'accuracy'})

history = model.fit(x_train, {'trade_pred': y_train[:, 0], 'partner_pred': y_train[:, 1]}, epochs=int(300), batch_size=int(512), validation_data=(x_val, {'trade_pred': y_val[:, 0], 'partner_pred': y_val[:, 1]}), verbose=1,
                    shuffle=False, callbacks=[callback])

Am I forgetting something in the network structure? I already tried fiddling with the weighting of the loss functions, but to no avail. Still the same prediction.

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  • $\begingroup$ Do you mean a predicted probability of $1$? Neural networks, like logistic regressions, output probability values, not categories. $\endgroup$ – Dave Jun 15 at 9:55
  • $\begingroup$ Exactly, given the softmax output layer. Sorry for the imprecision. $\endgroup$ – SimonDude Jun 15 at 11:58
  • $\begingroup$ Does the loss decrease during training? If you train the same network, but instead only use one of the two outputs in the loss computation, does the same problem happen? What happens if you change the weights of the loss? How did you choose these weights? What happens if you set both weights to 1? $\endgroup$ – Sycorax Jun 15 at 12:43
  • $\begingroup$ It does start out decreasing, but only in the first epoch. I'm only interested in the regression part of the problem, yet without adding the classification, the network only predicts zeros. Meaning I would consider it predicting something $>0$ as some kind of success. This is why I don't think setting the classification loss to zero is of much use.The weights are chosen arbitrarily, but I thought that I need to give the classification some higher weight, as its loss is mostly between 0 and 1 unlike the loss for the regression. $\endgroup$ – SimonDude Jun 15 at 13:40
  • $\begingroup$ Have a look at stats.stackexchange.com/questions/261704/… $\endgroup$ – mhdadk Jun 15 at 13:51

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