tf.keras.losses.MeanSquaredError

Computes the mean of squares of errors between labels and predictions.

Inherits From: Loss

tf.keras.losses.MeanSquaredError(
    reduction='sum_over_batch_size',
    name='mean_squared_error'
)

Used in the tutorials
Time series forecasting Intro to Autoencoders Load CSV data Hello, many worlds Recommending movies: ranking

loss = mean(square(y_true - y_pred))

Args
`reduction`	Type of reduction to apply to the loss. In almost all cases this should be `"sum_over_batch_size"`. Supported options are `"sum"`, `"sum_over_batch_size"` or `None`.
`name`	Optional name for the loss instance.

Methods

call(
    y_true, y_pred
)

@classmethod
from_config(
    config
)

get_config()

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.

Last updated 2024-06-07 UTC.