How to Create a Metric in Keras that Returns a Multi-dimensional Value

Keras is a popular open-source neural network library written in Python. It provides a simple and easy-to-use interface for constructing deep learning models. When building a neural network in Keras, it is important to define appropriate metrics to evaluate the performance of the model. In some cases, we may want a metric that returns a multi-dimensional value. In this article, we will discuss how to create a custom multi-dimensional metric in Keras.

Defining a Custom Metric Function

In Keras, we can create a custom metric by defining a function that takes the true labels and the predicted values as inputs and returns the metric value. To create a multi-dimensional metric, we can simply return a list or a numpy array of values instead of a single scalar value.

    
import keras.backend as K
import numpy as np

def custom_multi_dimensional_metric(y_true, y_pred):
    # Calculate the metric values
    metric_value1 = K.mean(K.abs(y_true - y_pred))
    metric_value2 = K.mean(K.square(y_true - y_pred))
    
    # Return the multi-dimensional metric as a numpy array
    return np.array([metric_value1, metric_value2])
    
    

Using the Custom Metric in the Model

Once the custom metric function is defined, we can use it when compiling the Keras model. We simply pass the function as a list to the `metrics` argument of the `compile` method.

    
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[custom_multi_dimensional_metric])
    
    

Training the Model and Evaluating the Multi-dimensional Metric

After the model is compiled with the custom multi-dimensional metric, we can train the model and evaluate the metric on the validation set using the `evaluate` method.

    
loss, metrics = model.evaluate(x_val, y_val)
print('Validation loss:', loss)
print('Custom metric values:', metrics)
    
    

Conclusion

Creating a custom multi-dimensional metric in Keras is straightforward. By defining a custom metric function that returns a list or numpy array of values, we can evaluate the model’s performance using multiple metric values. This can be useful in various machine learning tasks where a single scalar metric may not fully capture the model’s performance.