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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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Hadoop, Data Science, Statistics & othersWhat 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
Categorical cross-entropy class
2. Accuracy Metrics
Binary accuracy class
Sparse categorical 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
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
Categorical hinge class
6. Image segmentation metrics
Mean IO U classKeras 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.Recommended Articles
This is a guide to Keras Metrics. Here we discuss the Introduction: What are Keras metrics, and how to create keras metrics?. You may also look at the following articles to learn more –
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