How to initialize tf.metrics members in tensorflow?

@darrion.kuhn 

To initialize tf.metrics members in TensorFlow, you can simply create a new instance of the metric you want to use. Here is an example of how you can initialize a metric like "accuracy":

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import tensorflow as tf

# Create a new instance of the accuracy metric
accuracy = tf.keras.metrics.Accuracy()

You can then use this initialized metric accuracy to update its values and calculate the metric during training or evaluation. For example, you can use the update_state() method to update the metric with new values, and the result() method to get the calculated metric value.

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# Update the accuracy metric with new values
accuracy.update_state(y_true, y_pred)

# Get the calculated accuracy value
accuracy_result = accuracy.result().numpy()

You can similarly initialize other metrics like "precision", "recall", "f1_score", etc. by creating new instances of the respective metric classes provided by TensorFlow.