Skip to content
This repository was archived by the owner on Jul 1, 2023. It is now read-only.

Update Loss docs to reflect non-mutating API #232

Merged
merged 1 commit into from
Jun 13, 2019
Merged

Update Loss docs to reflect non-mutating API #232

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions Sources/TensorFlow/Loss.swift
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.

/// Computes the mean squared error between predictions and labels.
/// Returns the mean squared error between predictions and labels.
///
/// - Parameters:
/// - predicted: Predicted outputs from a neural network.
Expand Down Expand Up @@ -41,7 +41,7 @@ public func meanSquaredLogarithmicError<Scalar: TensorFlowFloatingPoint>(
return (logPredicted - logExpected).squared().mean()
}

/// Computes the mean absolute error between predictions and expectations.
/// Returns the mean absolute error between predictions and expectations.
///
/// - Parameters:
/// - predicted: Predicted outputs from a neural network.
Expand Down Expand Up @@ -115,7 +115,7 @@ public func kullbackLeiblerDivergence<Scalar: TensorFlowFloatingPoint>(
return (expected * log(expected / predicted)).sum()
}

/// Computes the softmax cross entropy (categorical cross entropy) between logits and labels.
/// Returns the softmax cross entropy (categorical cross entropy) between logits and labels.
///
/// - Parameters:
/// - logits: One-hot encoded outputs from a neural network.
Expand All @@ -136,7 +136,7 @@ func _vjpSoftmaxCrossEntropy<Scalar: TensorFlowFloatingPoint>(
return (loss.mean(), { v in (v / batchSize) * grad })
}

/// Computes the softmax cross entropy (categorical cross entropy) between logits and labels.
/// Returns the softmax cross entropy (categorical cross entropy) between logits and labels.
///
/// - Parameters:
/// - logits: Unscaled log probabilities from a neural network.
Expand All @@ -158,7 +158,7 @@ func _vjpSoftmaxCrossEntropy<Scalar: TensorFlowFloatingPoint>(
return (loss.mean(), { v in v / batchSize * grad })
}

/// Computes the sigmoid cross entropy (binary cross entropy) between logits and labels.
/// Returns the sigmoid cross entropy (binary cross entropy) between logits and labels.
///
/// The reduction is reduced over all elements. If reduced over batch size is intended, please
/// consider to scale the loss.
Expand Down