Attend the Women in ML Symposium on December 7 Register now

tfr.keras.losses.MeanSquaredLoss

Stay organized with collections Save and categorize content based on your preferences.

Computes mean squared loss between y_true and y_pred.

loss = (y_true - y_pred)**2

Standalone usage:

y_true = [[1., 0.]]
y_pred = [[0.6, 0.8]]
loss = tfr.keras.losses.MeanSquaredLoss()
loss(y_true, y_pred).numpy()
0.4
# Using ragged tensors
y_true = tf.ragged.constant([[1., 0.], [0., 1., 0.]])
y_pred = tf.ragged.constant([[0.6, 0.8], [0.5, 0.8, 0.4]])
loss = tfr.keras.losses.MeanSquaredLoss(ragged=True)
loss(y_true, y_pred).numpy()
0.20833336

Usage with the compile() API:

model.compile(optimizer='sgd', loss=tfr.keras.losses.MeanSquaredLoss())

Definition:

\[ \mathcal{L}(\{y\}, \{s\}) = \sum_i (y_i - s_i)^{2} \]

reduction (Optional) The tf.keras.losses.Reduction to use (see tf.keras.losses.Loss).
name (Optional) The name for the op.
ragged (Optional) If True, this loss will accept ragged tensors. If False, this loss will accept dense tensors.

Methods

from_config

Instantiates a Loss from its config (output of get_config()).

Args
config Output of get_config().

Returns
A Loss instance.

get_config

View source

Returns the config dictionary for a Loss instance.

__call__

View source

See tf.keras.losses.Loss.