tf.keras.losses.MeanSquaredError

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

Used in the notebooks

Used in the guide Used in the tutorials

loss = square(y_true - y_pred)

Standalone usage:

y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [1., 0.]]
# Using 'auto'/'sum_over_batch_size' reduction type.
mse = tf.keras.losses.MeanSquaredError()
mse(y_true, y_pred).numpy()
0.5
# Calling with 'sample_weight'.
mse(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()
0.25
# Using 'sum' reduction type.
mse = tf.keras.losses.MeanSquaredError(
    reduction=tf.keras.losses.Reduction.SUM)