parameters: (predictions, labels=None, weights=1.0, scope=None)
docstring: Adds a Sum-of-Squares loss to the training procedure.
  
    `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`.
  
    Args:
      predictions: The predicted outputs.
      labels: The ground truth output tensor, same dimensions as 'predictions'.
      weights: Coefficients for the loss a scalar, a tensor of shape
        [batch_size] or a tensor whose shape matches `predictions`.
      scope: The scope for the operations performed in computing the loss.
  
    Returns:
      A scalar `Tensor` representing the loss value.
  
    Raises:
      ValueError: If the shape of `predictions` doesn't match that of `labels` or
        if the shape of `weights` is invalid.
docstring_summary: Adds a Sum-of-Squares loss to the training procedure.
docstring_tokens: Adds a Sum - of - Squares loss to the training procedure .
function: def mean_squared_error(predictions, labels=None, weights=1.0, scope=None):
    """Adds a Sum-of-Squares loss to the training procedure.
  
    `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`.
  
    Args:
      predictions: The predicted outputs.
      labels: The ground truth output tensor, same dimensions as 'predictions'.
      weights: Coefficients for the loss a scalar, a tensor of shape
        [batch_size] or a tensor whose shape matches `predictions`.
      scope: The scope for the operations performed in computing the loss.
  
    Returns:
      A scalar `Tensor` representing the loss value.
  
    Raises:
      ValueError: If the shape of `predictions` doesn't match that of `labels` or
        if the shape of `weights` is invalid.
    """
    with ops.name_scope(scope, "mean_squared_error",
                        [predictions, labels, weights]) as scope:
      predictions.get_shape().assert_is_compatible_with(labels.get_shape())
      predictions = math_ops.to_float(predictions)
      labels = math_ops.to_float(labels)
      losses = math_ops.square(math_ops.subtract(predictions, labels))
      return compute_weighted_loss(losses, weights, scope=scope)
function_tokens: def mean_squared_error ( predictions , labels = None , weights = 1.0 , scope = None ) : with ops . name_scope ( scope , "mean_squared_error" , [ predictions , labels , weights ] ) as scope : predictions . get_shape ( ) . assert_is_compatible_with ( labels . get_shape ( ) ) predictions = math_ops . to_float ( predictions ) labels = math_ops . to_float ( labels ) losses = math_ops . square ( math_ops . subtract ( predictions , labels ) ) return compute_weighted_loss ( losses , weights , scope = scope )