How do you implement a custom metric in Keras?

To implement a custom metric in Keras, you need to create a function that takes in two arguments: y_true and y_pred. The y_true argument is an array of true labels, and the y_pred argument is an array of predicted labels. The function should return a single scalar value that represents the custom metric.

Once the custom metric function is created, it can be passed to the compile() method of the Keras model. The compile() method takes in a metrics argument, which is a list of metrics to be evaluated by the model during training and testing. The custom metric function can be added to this list.

For example, if you wanted to implement a custom metric called ‘my_metric’, you could do the following:

def my_metric(y_true, y_pred): # code to calculate custom metric return metric_value

model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[my_metric])

The custom metric will then be evaluated during training and testing, and the results will be available in the history object returned by the fit() and evaluate() methods.