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',
    dtype=None
)

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"`, `"mean"`, `"mean_with_sample_weight"` or `None`. `"sum"` sums the loss, `"sum_over_batch_size"` and `"mean"` sum the loss and divide by the sample size, and `"mean_with_sample_weight"` sums the loss and divides by the sum of the sample weights. `"none"` and `None` perform no aggregation. Defaults to `"sum_over_batch_size"`.
`name`	Optional name for the loss instance.
`dtype`	The dtype of the loss's computations. Defaults to `None`, which means using `keras.backend.floatx()`. `keras.backend.floatx()` is a `"float32"` unless set to different value (via `keras.backend.set_floatx()`). If a `keras.DTypePolicy` is provided, then the `compute_dtype` will be utilized.

y_true = keras.ops.array([1.0, 0.0, 1.0])
y_pred = keras.ops.array([0.9, 0.1, 0.8])
loss = keras.losses.MeanSquaredError()
loss(y_true, y_pred)
0.02

Attributes
`dtype`

Attributes

dtype

call(
    y_true, y_pred
)

@classmethod
from_config(
    config
)

get_config()

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.