Answers.Body: <p>Yes, due the input, output being constrained between zero and one that would be the only viable activation function. </p>
  
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Answers.CreationDate: 2020-03-17T08:57:06.647Z
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Answers.UserDisplayName: user q
Answers.Body: <p>You have a problem in your code, you want to use "sigmoid" in the last layer. Fot the code you are showin you are using linear activation in the last layer. </p>
  
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Answers.CreationDate: 2020-03-17T09:00:34.040Z
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Answers.UserDisplayName: hola
Answers.Body: <p>You should not limit yourself to sigmoid as activation function on the last layer. Usually you're normalizing your dataset, but when you're testing/evaluating the model you're applying the inverse of the scaling transformation to the predictions, so you could easily use tanh which is defined on [-1, 1] </p>
  
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Answers.UserDisplayName: razvanc92
Body: <p>I'm trying to develop a multistep forecasting model using LSTM  Network. The model takes three times steps as input and predicting two time_steps. both input and output columns are normalised using minmax_scalar within the range of 0 and 1.</p>
  
  <p>Please see the below model architecture</p>
  
  <p><em>Model Architecture</em> </p>
  
  <pre><code>model = Sequential()
  model.add(LSTM(80,input_shape=(3,1),activation='sigmoid',return_sequences=True))
  model.add(LSTM(20,activation='sigmoid',return_sequences=False))
  model.add(Dense(2))
  </code></pre>
  
  <p>In this case, using sigmoid as an activation function is it correct? </p>
  
Tags: <long-short-term-memory><activation-functions><regression><forecasting>
Title: Using sigmoid in LSTM network for multi-step forecasting
UserDisplayName: Majo_Jose
ThreadText: # Using sigmoid in LSTM network for multi-step forecasting
  
  (Asked by: username_0 on 2020-03-17)
  
  I'm trying to develop a multistep forecasting model using LSTM  Network. The model takes three times steps as input and predicting two time_steps. both input and output columns are normalised using minmax_scalar within the range of 0 and 1.
  Please see the below model architecture
  Model Architecture 
  model = Sequential()
  model.add(LSTM(80,input_shape=(3,1),activation='sigmoid',return_sequences=True))
  model.add(LSTM(20,activation='sigmoid',return_sequences=False))
  model.add(Dense(2))
  
  In this case, using sigmoid as an activation function is it correct?
  
  **Tags:** long-short-term-memory, activation-functions, regression, forecasting
  
  ---
  
  ## 3 answers
  
  ### Answer by username_1 on 2020-03-17. Score: 1
  
  Yes, due the input, output being constrained between zero and one that would be the only viable activation function.
  
  
  ---
  
  ### Answer by username_2 on 2020-03-17. Score: 1
  
  You have a problem in your code, you want to use "sigmoid" in the last layer. Fot the code you are showin you are using linear activation in the last layer.
  
  
  ---
  
  ### Answer by username_3 on 2020-03-17. Score: 1
  
  You should not limit yourself to sigmoid as activation function on the last layer. Usually you're normalizing your dataset, but when you're testing/evaluating the model you're applying the inverse of the scaling transformation to the predictions, so you could easily use tanh which is defined on [-1, 1]
  
  
  ---
  
  