CODEWITHSUNDEEP
pythontensorflow
Vikash Singh
Vikash Singh

Reputation: 14001

how to set rmse cost function in tensorflow

I have cost function in tensorflow.

activation = tf.add(tf.mul(X, W), b)
cost = (tf.pow(Y-y_model, 2)) # use sqr error for cost function

I am trying out this example. How can I change it to rmse cost function?

Upvotes: 16

Views: 32153

Answers (5)

Moumen Lahmidi
Moumen Lahmidi

Reputation: 482

for who want to implement RMSE as a metric

rmse = tf.keras.metrics.RootMeanSquaredError()

exapmle of how to use it

model.compile(optimizer=optimizer, loss='mean_squared_error',
              metrics=[rmse,'mae'])

Upvotes: 6

lazyman
lazyman

Reputation: 123

Now we have tf.losses.mean_squared_error

Therefore, 

RMSE = tf.sqrt(tf.losses.mean_squared_error(label, prediction))

Upvotes: 8

Rajarshee Mitra
Rajarshee Mitra

Reputation: 1905

tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(targets, outputs))))

And slightly simplified (TensorFlow overloads the most important operators):

tf.sqrt(tf.reduce_mean((targets - outputs)**2))

Upvotes: 49

dga
dga

Reputation: 21917

(1) Are you sure you need this? Minimizing the l2 loss will give you the same result as minimizing the RMSE error. (Walk through the math: You don't need to take the square root, because minimizing x^2 still minimizes x for x>0, and you know that the sum of a bunch of squares is positive. Minimizing x*n minimizes x for constant n).

(2) If you need to know the numerical value of the RMSE error, then implement it directly from the definition of RMSE:

tf.sqrt(tf.reduce_sum(...)/n)

(You need to know or calculate n - the number of elements in the sum, and set the reduction axis appropriately in the call to reduce_sum).

Upvotes: 6

Salvador Dali
Salvador Dali

Reputation: 222521

The formula for root mean square error is:

enter image description here

The way to implement it in TF is tf.sqrt(tf.reduce_mean(tf.squared_difference(Y1, Y2))).

The important thing to remember is that there is no need to minimize RMSE loss with the optimizer. With the same result you can minimize just tf.reduce_mean(tf.squared_difference(Y1, Y2)) or even tf.reduce_sum(tf.squared_difference(Y1, Y2)) but because they have a smaller graph of operations, they will be optimized faster.

But you can use this function if you just want to tract the value of RMSE.

Upvotes: 15

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