My model doesn't seem to work, as accuracy and loss are 0

Question

I tried to design an LSTM network using keras but the accuracy is 0.00 while the loss value is 0.05 the code which I wrote is below.

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation = tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation = tf.nn.relu))



def percentage_difference(y_true, y_pred):
    return K.mean(abs(y_pred/y_true - 1) * 100)



model.compile(optimizer='sgd', 
             loss='mse',
             metrics = ['accuracy', percentage_difference])

model.fit(x_train, y_train.values, epochs = 10)

my input train and test data set have been imported using the pandas' library. The number of features is 5 and the number of target is 1. All endeavors will be appreciated.

Answer 1

From what I see is that you're using a neural network applied for a regression problem.

Regression is the task of predicting continuous values by learning from various independent features.

So, in the regression problem we don't have metrics like accuracy because this is for classification branch of the supervised learning.

The equivalent of accuracy for regression could be coefficient of determination or R^2 Score .

from keras import backend as K

def coeff_determination(y_true, y_pred):
    SS_res =  K.sum(K.square( y_true-y_pred ))
    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )
    return ( 1 - SS_res/(SS_tot + K.epsilon()) )

model.compile(optimizer='sgd', 
         loss='mse',
         metrics = [coeff_determination])