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tf.keras.metrics.MeanSquaredLogarithmicError

Class MeanSquaredLogarithmicError

Computes the mean squared logarithmic error between y_true and y_pred.

Aliases:

  • Class tf.compat.v1.keras.metrics.MeanSquaredLogarithmicError
  • Class tf.compat.v2.keras.metrics.MeanSquaredLogarithmicError
  • Class tf.compat.v2.metrics.MeanSquaredLogarithmicError
  • Class tf.keras.metrics.MeanSquaredLogarithmicError
View source on GitHub

For example, if y_true is [0., 0., 1., 1.], and y_pred is [1., 1., 1., 0.] the mean squared logarithmic error is 0.36034.

Usage:

m = tf.keras.metrics.MeanSquaredLogarithmicError()
m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Final result: ', m.result().numpy())  # Final result: 0.36034

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.MeanSquaredLogarithmicError()])

__init__

View source

__init__(
    name='mean_squared_logarithmic_error',
    dtype=None
)

Methods

reset_states

View source

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

update_state

View source

update_state(
    y_true,
    y_pred,
    sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args:

  • y_true: The ground truth values.
  • y_pred: The predicted values.
  • sample_weight: Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns:

Update op.