Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

tfr.keras.losses.MeanSquaredLoss

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.