tf.keras.metrics.MeanSquaredLogarithmicError

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Computes the mean squared logarithmic error between y_true and y_pred.

tf.keras.metrics.MeanSquaredLogarithmicError(
    name='mean_squared_logarithmic_error', dtype=None
)

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()])

Args:

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()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

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:

Returns:

Update op.