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.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanSquaredError