tf.compat.v1.metrics.mean_squared_error

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Computes the mean squared error between the labels and predictions.

tf.compat.v1.metrics.mean_squared_error(
    labels,
    predictions,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Used in the notebooks

Used in the guide
Migrate metrics and optimizers

The mean_squared_error function creates two local variables,total and count that are used to compute the mean squared error. This average is weighted by weights, and it is ultimately returned asmean_squared_error: an idempotent operation that simply divides total bycount.

For estimation of the metric over a stream of data, the function creates anupdate_op operation that updates these variables and returns themean_squared_error. Internally, a squared_error operation computes the element-wise square of the difference between predictions and labels. Thenupdate_op increments total with the reduced sum of the product ofweights and squared_error, and it increments count with the reduced sum of weights.

If weights is None, weights default to 1. Use weights of 0 to mask values.

Args
labels	A Tensor of the same shape as predictions.
predictions	A Tensor of arbitrary shape.
weights	Optional Tensor whose rank is either 0, or the same rank aslabels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding labels dimension).
metrics_collections	An optional list of collections thatmean_squared_error should be added to.
updates_collections	An optional list of collections that update_op should be added to.
name	An optional variable_scope name.

Returns
mean_squared_error	A Tensor representing the current mean, the value oftotal divided by count.
update_op	An operation that increments the total and count variables appropriately and whose value matches mean_squared_error.

Raises
ValueError	If predictions and labels have mismatched shapes, or ifweights is not None and its shape doesn't match predictions, or if either metrics_collections or updates_collections are not a list or tuple.
RuntimeError	If eager execution is enabled.

tf.compat.v1.metrics.mean_squared_error | TensorFlow v2.16.1 (original) (raw)

Used in the notebooks