In this example, <code>tf.losses.mean_squared_error</code> is used for the <code>loss</code> parameter of <code>EstimatorSpec</code>, while <code>tf.metrics.root_mean_squared_error</code> is used for <code>eval_metric_ops</code> parameter. Does anyone have ideas what is the main difference between <code>tf.loss</code> and <code>tf.metrics</code>?

A <code>tf.loss</code> ('s derivative) is used to update the model during backpropagation. <code>tf.metric</code>s are for evaluating the model.

What's the difference between tf.losses and tf.metrics?

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?

What is metrics in Tensorflow?

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.

What is accuracy metric in Tensorflow?

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.