tf.compat.v1.losses.mean_squared_error

Adds a Sum-of-Squares loss to the training procedure.

tf.compat.v1.losses.mean_squared_error(
    labels,
    predictions,
    weights=1.0,
    scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

Migrate to TF2

tf.compat.v1.losses.mean_squared_error is mostly compatible with eager execution and tf.function. But, the loss_collection argument is ignored when executing eagerly and no loss will be written to the loss collections. You will need to either hold on to the return value manually or rely on tf.keras.Model loss tracking.

To switch to native TF2 style, instantiate the tf.keras.losses.MeanSquaredError class and call the object instead.

Structural Mapping to Native TF2

Before:

loss = tf.compat.v1.losses.mean_squared_error(
  labels=labels,
  predictions=predictions,
  weights=weights,
  reduction=reduction)

After:

loss_fn = tf.keras.losses.MeanSquaredError(
  reduction=reduction)
loss = loss_fn(
  y_true=labels,
  y_pred=predictions,
  sample_weight=weights)

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
labels y_true In __call__() method
predictions y_pred In __call__() method
weights sample_weight In __call__() method. The shape requirements for sample_weight is different from weights. Please check the argument definition for details.
scope Not supported -
loss_collection Not supported Losses should be tracked explicitly or with Keras APIs, for example, add_loss, instead of via collections
reduction reduction In constructor. Value of tf.compat.v1.losses.Reduction.SUM_OVER_BATCH_SIZE, tf.compat.v1.losses.Reduction.SUM, tf.compat.v1.losses.Reduction.NONE in tf.compat.v1.losses.softmax_cross_entropy correspond to tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE, tf.keras.losses.Reduction.SUM, tf.keras.losses.Reduction.NONE, respectively. If you used other value for reduction, including the default value tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS, there is no directly corresponding value. Please modify the loss implementation manually.

Before & After Usage Example

Before:

y_true = [1, 2, 3]
y_pred = [1, 3, 5]
weights = [0, 1, 0.25]
# samples with zero-weight are excluded from calculation when `reduction`
# argument is set to default value `Reduction.SUM_BY_NONZERO_WEIGHTS`
tf.compat.v1.losses.mean_squared_error(
   labels=y_true,
   predictions=y_pred,
   weights=weights).numpy()
1.0

tf.compat.v1.losses.mean_squared_error(
   labels=y_true,
   predictions=y_pred,
   weights=weights,
   reduction=tf.compat.v1.losses.Reduction.SUM_OVER_BATCH_SIZE).numpy()
0.66667

After:

y_true = [[1.0], [2.0], [3.0]]
y_pred = [[1.0], [3.0], [5.0]]
weights = [1, 1, 0.25]
mse = tf.keras.losses.MeanSquaredError(
   reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)
mse(y_true=y_true, y_pred=y_pred, sample_weight=weights).numpy()
0.66667

Description

weights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weights 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 weights vector. If the shape of weights matches the shape of predictions, then the loss of each measurable element of predictions is scaled by the corresponding value of weights.

labels The ground truth output tensor, same dimensions as 'predictions'.
predictions The predicted outputs.
weights Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
scope The scope for the operations performed in computing the loss.
loss_collection collection to which the loss will be added.
reduction Type of reduction to apply to loss.

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

ValueError If the shape of predictions doesn't match that of labels or if the shape of weights is invalid. Also if labels or predictions is None.