tf.keras.metrics.MeanSquaredError

View source on GitHub

Computes the mean squared error between y_true and y_pred.

tf.keras.metrics.MeanSquaredError(
    name='mean_squared_error', dtype=None
)

For example, if y_true is [0., 0., 1., 1.], and y_pred is [1., 1., 1., 0.] the mean squared error is 3/4 (0.75).

Usage:

m = tf.keras.metrics.MeanSquaredError()
m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Final result: ', m.result().numpy())  # Final result: 0.75

Usage with tf.keras API:

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

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

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:

• 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.