python-kerasHow do I calculate the loss for a model using Python and Keras?

The loss of a model in Python and Keras can be calculated using the model.evaluate() function. The model.evaluate() function takes in two arguments: the test data and the test labels. It then returns the loss value for the model.

Example code

loss = model.evaluate(x_test, y_test)
print(loss)

Output example

0.065

The model.evaluate() function works by calculating the mean squared error (MSE) between the predicted values and the true values. The MSE is then used to calculate the loss.

Parts of the code:

• model.evaluate() – This is the function used to calculate the loss of the model.
• x_test – This is the test data.
• y_test – This is the test labels.
• loss – This is the variable that stores the loss value.
• print(loss) – This is used to print the loss value.

Helpful links

• Keras Documentation - model.evaluate()
• Mean Squared Error (MSE)

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