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Introduction to Keras Metrics

We need certain functions known as Keras’ metrics to judge and measure the model performance we created in Keras. Loss functions and metric functions are quite similar in nature and behavior. The only difference between them is that the loss function involves the usage of the generated results in the model training process. In contrast, metric functions do not use the resultant for training the model.

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What are Keras metrics?

Metrics are the functions used in keras to measure the model’s performance. The loss and metric functions are similar, having only the difference in usage of results for the training process. In loss functions, the resultant generated is used in the training process, while metric functions don’t follow this approach. We can even use the loss function as the metric for performance analysis. There are many available functions that you can use as metrics. However, you are also free to create your customized metric functions.

How to create keras metrics?

We can create a customized metric by following either of two approaches. One is by using simple callable, which are stateless, that means does not store information about the state. The other way is by treating it as the subclass of the Metric class, which is a stateful process as the information of the instance is maintained in the state.

We will be seeing both of these methods in the below section of the customized metric creation section.

Keras metrics classification

Metrics are classified into various domains that are created as per the usage. This section will list all of the available metrics and their classifications –

1. Probabilistic Metrics

KL Divergence class

Binary Cross entropy class

Sparse categorical cross-entropy class

Poisson class

Categorical cross-entropy class

2. Accuracy Metrics

Binary accuracy class

Sparse categorical accuracy class

Accuracy class

Sparse top k categorical accuracy class

Top k categorical accuracy class

Categorical accuracy class

3. Classification metrics based on negative and positive Boolean values and true and false.

Sensitivity at specificity class

Recall class

Precision class

AUC class

True negative class

True positives class

False negatives class

False positives class

Specificity in sensitivity class

Precision at recall class

4. Regression metrics

Cosine similarity class

Mean absolute percentage error class

Mean squared error class

Mean absolute error class

Root mean squared error class

Log cosh error class

Mean squared logarithm error class

5. Hinge metrics for maximum margin classification

Squared hinge class

Hinge class

Categorical hinge class

6. Image segmentation metrics

Mean IO U class

Keras metrics Customize

The stateless method as simple callables –

Let us consider an example –

Axis value will be kept as -1 in this example –

print (“Compiled successfully by using the specified metrics.”)

After execution of the above code snippet, you get the following output –

For the above example, to track the records while training and evaluating the scalar metrics, we are using the value calculated of an average of metric values per batch for all the given batches for the call given to the model. evaluate() function or all the given epochs.

Stateful method of treating it as a subclass of the Metric class –

It is impossible to represent all the metrics as the callables in stateless form. This is because the metrics are being evaluated for each batch of evaluation and training. But there are some scenarios where we are not interested in the average values per batch.

For this kind of metric, we will be subclassing the class named Metric to ensure that the state is being maintained for all the batches. For this, we will follow the below-mentioned steps –

• We can then clear all the states by using the method function reset_states()

Let us consider one example for this implementation –

print(‘The last acquired result:’, float(sampleObj .result()))

The execution of the above code snippet results into –

Conclusion

We can create the Keras metrics according to our necessities by customizing them or using them from the classes available to evaluate our Keras model’s performance.