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Add consistent parameter naming in Loss docs #245

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Jun 15, 2019
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Add consistent parameter naming in Loss docs #245

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12 changes: 6 additions & 6 deletions Sources/TensorFlow/Loss.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -12,35 +12,35 @@
// See the License for the specific language governing permissions and
// limitations under the License.

/// Returns the L1 loss between predictions and labels.
/// Returns the L1 loss between predictions and expectations.
///
/// - Parameters:
/// - predicted: Predicted outputs from a neural network.
/// - labels: Expected values, i.e. targets, that correspond to the correct output.
/// - expected: Expected values, i.e. targets, that correspond to the correct output.
@differentiable(wrt: predicted)
public func l1Loss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>, expected: Tensor<Scalar>
) -> Tensor<Scalar> {
return abs(expected - predicted).sum()
}

/// Returns the L2 loss between predictions and labels.
/// Returns the L2 loss between predictions and expectations.
///
/// - Parameters:
/// - predicted: Predicted outputs from a neural network.
/// - labels: Expected values, i.e. targets, that correspond to the correct output.
/// - expected: Expected values, i.e. targets, that correspond to the correct output.
@differentiable(wrt: predicted)
public func l2Loss<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>, expected: Tensor<Scalar>
) -> Tensor<Scalar> {
return (expected - predicted).squared().sum()
}

/// Returns the mean squared error between predictions and labels.
/// Returns the mean squared error between predictions and expectations.
///
/// - Parameters:
/// - predicted: Predicted outputs from a neural network.
/// - labels: Expected values, i.e. targets, that correspond to the correct output.
/// - expected: Expected values, i.e. targets, that correspond to the correct output.
@differentiable(wrt: predicted)
public func meanSquaredError<Scalar: TensorFlowFloatingPoint>(
predicted: Tensor<Scalar>, expected: Tensor<Scalar>
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