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# tf.keras.metrics.MeanSquaredLogarithmicError

Computes the mean squared logarithmic error between y_true and y_pred.

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()])

fn The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
**kwargs The keyword arguments that are passed on to fn.

## Methods

### reset_states

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Resets all of the metric state variables.

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

### result

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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

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.

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