A few quick fixes to help unbreak swift-models.

MiniGo/Models/PythonCheckpointReader.swift

@@ -28,12 +28,9 @@ public class PythonCheckpointReader {
         let countSuffix = layerCounts[layerName] == nil ? "" : "_\(layerCounts[layerName]!)"
         let tensorName = layerName + countSuffix + "/" + weightName
         // TODO(jekbradbury): support variadic dtype attrs in RawOpsGenerated
-        return Tensor<Float>(handle: #tfop(
-            "RestoreV2",
-            StringTensor(path),
-            StringTensor([tensorName]),
-            StringTensor([""]),
-            dtypes$dtype: [Float.tensorFlowDataType]))
+        return Raw.restoreV2(prefix: StringTensor(path),
+                             tensorNames: StringTensor([tensorName]),
+                             shapeAndSlices: StringTensor([""]))
     }
 
     /// Increments a per-layer counter for variable names in the checkpoint file.

Transformer/Operators.swift

@@ -19,7 +19,8 @@ import TensorFlow
 /// Computes the Gaussian error linear unit (GELU) nonlinear activation function
 @differentiable
 func gelu<Scalar: TensorFlowFloatingPoint>(_ x: Tensor<Scalar>) -> Tensor<Scalar> {
-    let polynomial = 0.79788456 * (x + 0.044715 * x * x * x)
+    let xCubed = x * x * x
+    let polynomial = 0.79788456 * (x + 0.044715 * xCubed)
     return 0.5 * x * (1.0 + tanh(polynomial))
 }
 

Transformer/PythonCheckpointReader.swift

@@ -36,12 +36,9 @@ func readTensor<Scalar: TensorFlowScalar>(
     scalarType: Scalar.Type
 ) -> Tensor<Scalar> {
     // TODO(jekbradbury): support variadic dtype attrs in RawOpsGenerated
-    return Tensor(handle: #tfop(
-        "RestoreV2",
-        StringTensor(path),
-        StringTensor([name]),
-        StringTensor([""]),
-        dtypes$dtype: [Scalar.tensorFlowDataType]))
+    return Raw.restoreV2(prefix: StringTensor(path),
+                         tensorNames: StringTensor([name]),
+                         shapeAndSlices: StringTensor([""]))
 }
 
 protocol InitializableFromPythonCheckpoint {