LSTM get next output with Keras So I'm learning RNN, and tried to do a prediction LSTM, but I do not understand how the output works. I have this LSTM RNN:

lstm_model = Sequential()
lstm_model.add(LSTM(100, batch_input_shape=(BATCH_SIZE, TIME_STEPS, x_t.shape[2]), dropout=0.0, recurrent_dropout=0.0, stateful=False,   kernel_initializer='random_uniform'))
lstm_model.add(Dropout(0.5))
lstm_model.add(Dense(25,activation='relu'))
lstm_model.add(Dense(1,activation='sigmoid'))
optimizer = optimizers.RMSprop(lr=0.003)
lstm_model.compile(loss='mean_squared_error', optimizer=optimizer)


and fit with:

history = lstm_model.fit(x_t, y_t, epochs=300, verbose=2, batch_size=BATCH_SIZE,
                    shuffle=False, validation_data=(trim_dataset(x_val, BATCH_SIZE),
                    trim_dataset(y_val, BATCH_SIZE)), callbacks=[csv_logger])


When I then try to predict using 

gotten = lstm_model.predict(x_test_t[-500:],batch_size=BATCH_SIZE)


Will I get only one new output? I'm very confused with the way you run and get predictions

Thanks for the help
With your current model, you will indeed get a single scalar output. This is because the last layer is Dense with just 1 unit. 

In other words, with this model you can map a sequence to a single value that may represent, e.g., the label of the series or its next value depending on what target you train the network on.