Building a Recurent Neuronal Network (Keras) # Building RNN from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout regressor = Sequential() # Layer 1 regressor.add(LSTM(units = 50, return_sequences = True, input_shape=(100, 1))) regressor.add(Dropout(0.2)) # Layer 2 regressor.add(LSTM(units = 50, return_sequences = True)) regressor.add(Dropout(0.2)) # Layer 3 regressor.add(LSTM(units = 50, return_sequences = True)) regressor.add(Dropout(0.2)) # Layer 4 regressor.add(LSTM(units = 50, return_sequences = False)) regressor.add(Dropout(0.2)) # Output layer regressor.add(Dense(units = 1)) # Compile regressor regressor.compile(optimizer = 'adam', loss = "mean_squared_error") # Fitting the model fit_result = regressor.fit(X_train, y_train, epochs = 200, batch_size = 32)