CODEWITHSUNDEEP
pythontensorflowkerasdeep-learningneural-network
Hamad
Hamad

Reputation: 383

How can I know if my Neural Network is doing good or not using Mean_Square_Error (Keras)

I'm using Keras and I'm trying to build a Neural Network to predict the interest rate of given data. The data looks like this:

    loan_amnt   annual_inc  emp_length  int_rate
    10000    38000.0         5.600882          12.40
    13750    17808.0         5.600882          28.80
    26100    68000.0         10.000000         20.00
    13000    30000.0         1.000000          20.00
    7000     79950.0         7.000000          7.02

The features (X) are loan_amnt , annual_inc , and emp_length. The target (y) is int_rate.

Here's my process and what I've done after normalizing the data:

      #Building out model
     model = Sequential([
     Dense(9, activation='relu', input_shape=(3,)),
     Dense(3, activation='relu'),
     Dense(1, activation='linear'),
     ])

      #Compiling model
      model.compile(loss='mean_absolute_percentage_error',
              metrics=['mse'],
              optimizer='RMSprop')

       hist = model.fit(X_train, Y_train,
          batch_size=100, epochs=20, verbose=1)

Here's an output sample after running model.fit():

    Epoch 1/20
    693/693 [==============================] - 1s 905us/step - loss: 96.2391 - mean_squared_error: 
    179.8007
    Epoch 2/20
    693/693 [==============================] - 0s 21us/step - loss: 95.2362 - mean_squared_error: 
    176.9865
    Epoch 3/20
    693/693 [==============================] - 0s 20us/step - loss: 94.4133 - mean_squared_error: 
    174.6367

Finally, evaluating the model model.evaluate(X_train, Y_train) and got the following output:

      693/693 [==============================] - 0s 372us/step
      [77.88501817667468, 132.0109032635049]

The question is, how can I know if my model is doing well or not, and how can I read the numbers?

Upvotes: 1

Views: 61

Answers (2)

Nathan
Nathan

Reputation: 3648

You should not check the accuracy of your model using the training data because it makes your solution prone to overfitting. Instead you should set some data aside (20% is what I usually use) to validate your results.

If you plan on doing a lot of testing you should set aside a third dataset only for testing the final solution.

You can also use k_folds cross validation where you train the set on part of the data and use the rest to evaluate it, but doing so multiple times to get a better understanding of how accurate your model is.

Upvotes: 1

Orphee Faucoz
Orphee Faucoz

Reputation: 1290

You are using a variant of the MSE loss which is defined as :

MSE = mean((y_true - y_pred)^2)

So when you have 132. as a MSE metrics, then you really have a mean of sqrt(132.)~= 11,5 mean difference between the y_true and y_pred. Which is quite a bit on your data as it is shown on the MSPE loss, you're having ~78% error on your data.

In example if the y_true was 20, you could either predict 36 or 4. Something like that.

You could say that your error is good when MSPE is at 10%. Depends on your case

Upvotes: 1

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