tf.keras.metrics.MeanSquaredLogarithmicError

Computes the mean squared logarithmic error between y_true and y_pred.

View aliases

Main aliases

tf.metrics.MeanSquaredLogarithmicError

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.MeanSquaredLogarithmicError

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

Usage:

m = tf.keras.metrics.MeanSquaredLogarithmicError()
_ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result().numpy()
0.12011322

m.reset_states()
_ = m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
                   sample_weight=[1, 0])
m.result().numpy()
0.24022643

Usage with tf.keras API:

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

Args
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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reset_states()

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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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
y_true	Ground truth values. shape = [batch_size, d0, .. dN].
y_pred	The predicted values. shape = [batch_size, d0, .. dN].
sample_weight	Optional sample_weight acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the metric for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each metric element of y_pred is scaled by the corresponding value of sample_weight. (Note on dN-1: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.