tf.keras.losses.MeanSquaredError

Class MeanSquaredError

Defined in tensorflow/python/keras/losses.py.

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

For example, if y_true is [0., 0., 1., 1.] and y_pred is [1., 1., 1., 0.] then the mean squared error value is 3/4 (0.75).

Usage:

mse = tf.keras.losses.MeanSquaredError()
loss = mse([0., 0., 1., 1.], [1., 1., 1., 0.])
print('Loss: ', loss.numpy())  # Loss: 0.75

Usage with tf.keras API:

model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.MeanSquaredError())

__init__

__init__(
    reduction=losses_impl.ReductionV2.SUM_OVER_BATCH_SIZE,
    name=None
)

Initialize self. See help(type(self)) for accurate signature.

Methods

tf.keras.losses.MeanSquaredError.__call__

__call__(
    y_true,
    y_pred,
    sample_weight=None
)

Invokes the Loss instance.

Args:

• y_true: Ground truth values.
• y_pred: The predicted values.
• sample_weight: Optional Tensor whose rank is either 0, or the same rank as y_true, or is broadcastable to y_true. sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight matches the shape of y_pred, then the loss of each measurable element of y_pred is scaled by the corresponding value of sample_weight.

Returns:

Weighted loss float Tensor. If reduction is NONE, this has the same shape as y_true; otherwise, it is scalar.

Raises:

• ValueError: If the shape of sample_weight is invalid.

tf.keras.losses.MeanSquaredError.call

call(
    y_true,
    y_pred
)

Invokes the MeanSquaredError instance.

Args:

• y_true: Ground truth values.
• y_pred: The predicted values.

Returns:

Mean squared error losses.

tf.keras.losses.MeanSquaredError.from_config

from_config(
    cls,
    config
)

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

Args:

• config: Output of get_config().

Returns:

A Loss instance.

tf.keras.losses.MeanSquaredError.get_config

get_config()