improve LSTM accuracy

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I’m trying to build LSTM architecture to predict sickness rate. I’m actually stuck in 40% accuracy, I’m new in machine learning and I tried several tips like changing the optimzer, the layer node number and the dropout value without any improving. So could you guys help me with some advice.

the x array is composed of 10 columns

the y array is only one column the sickness rate

here is my model def lstm_model(): model = Sequential() model.add(LSTM(10, input_shape=(1,10), return_sequences= True)) model.add(Dropout(0.2)) model.add(LSTM(100, return_sequences= True)) model.add(LSTM(100, return_sequences= False)) model.add(Dropout(0.2)) model.add(Dense(50,kernel_constraint=NonNeg(),kernel_initializer='normal' ,activation="relu")) model.add(Dense(1,activation="linear")) model.compile(optimizer='adam',loss='mean_squared_error',metrics=['accuracy']) return model lstm = lstm_model() this is the output of . evaluate() 1275/1275 [==============================] - 1s 526us/sample - loss: 0.0015 - acc: 0.3930 0.0014869439909029204 0.3930161 and thank you in advance

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Created 4 years ago
Comments:7 (2 by maintainers)

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dabasajaycommented, Jul 5, 2019

Adding to @RooieRakkert 's answer, there are two methods you can use to check how if your model is performing well for regression task:

If you’re using root_mean_squared metric, make sure that training, validation, and testing error are low and close to each other in magnitude.
Use R^2 (coefficient of determination) metric from sklearn library. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get an R^2 score of 0.0. So you can check if your R^2 score is close to 1 then it’s a good model.

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briannemsickcommented, Jul 2, 2019

In that case linear and mean_square_error are both fine, accuracy is not a valid metric in this case (not a classification problem). Consider using mean_square_error (the loss function) or mean_absolute_error as a metric.