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In this example, tf.losses.mean_squared_error is used for the loss parameter of EstimatorSpec, while tf.metrics.root_mean_squared_error is used for eval_metric_ops parameter.
Does anyone have ideas what is the main difference between tf.loss and tf.metrics?
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A metric is a function that is used to judge the performance of your model. Metric functions are similar to loss functions, except that the results from evaluating a metric are not used when training the model. Note that you may use any loss function as a metric.
The accuracy function tf. metrics. accuracy calculates how often predictions matches labels based on two local variables it creates: total and count , that are used to compute the frequency with which logits matches labels .
A tf.loss ('s derivative) is used to update the model during backpropagation. tf.metrics are for evaluating the model.
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