tweak gradient calls to match api change (#235)

Author: brettkoonce

Autoencoder/main.swift

@@ -71,7 +71,7 @@ for epoch in 1...epochCount {
     for batch in trainingShuffled.batched(batchSize) {
         let x = batch.data
 
-        let 𝛁model = autoencoder.gradient { autoencoder -> Tensor<Float> in
+        let 𝛁model = TensorFlow.gradient(at: autoencoder) { autoencoder -> Tensor<Float> in
             let image = autoencoder(x)
             return meanSquaredError(predicted: image, expected: x)
         }

Benchmarks/Models/ImageClassificationTraining.swift

@@ -41,7 +41,7 @@ where
                 sampleCount: dataset.trainingExampleCount, randomSeed: Int64(epoch))
             for batch in trainingShuffled.batched(batchSize) {
                 let (labels, images) = (batch.label, batch.data)
-                let 𝛁model = model.gradient { model -> Tensor<Float> in
+                let 𝛁model = TensorFlow.gradient(at: model) { model -> Tensor<Float> in
                     let logits = model(images)
                     return softmaxCrossEntropy(logits: logits, labels: labels)
                 }

Examples/LeNet-MNIST/main.swift

@@ -55,7 +55,7 @@ for epoch in 1...epochCount {
     for batch in trainingShuffled.batched(batchSize) {
         let (labels, images) = (batch.label, batch.data)
         // Compute the gradient with respect to the model.
-        let 𝛁model = classifier.gradient { classifier -> Tensor<Float> in
+        let 𝛁model = TensorFlow.gradient(at: classifier) { classifier -> Tensor<Float> in
             let ŷ = classifier(images)
             let correctPredictions = ŷ.argmax(squeezingAxis: 1) .== labels
             trainStats.correctGuessCount += Int(

GAN/main.swift

@@ -153,7 +153,7 @@ for epoch in 1...epochCount {
         // Update generator.
         let vec1 = sampleVector(size: batchSize)
 
-        let 𝛁generator = generator.gradient { generator -> Tensor<Float> in
+        let 𝛁generator = TensorFlow.gradient(at: generator) { generator -> Tensor<Float> in
             let fakeImages = generator(vec1)
             let fakeLogits = discriminator(fakeImages)
             let loss = generatorLoss(fakeLogits: fakeLogits)
@@ -166,7 +166,7 @@ for epoch in 1...epochCount {
         let vec2 = sampleVector(size: batchSize)
         let fakeImages = generator(vec2)
 
-        let 𝛁discriminator = discriminator.gradient { discriminator -> Tensor<Float> in
+        let 𝛁discriminator = TensorFlow.gradient(at: discriminator) { discriminator -> Tensor<Float> in
             let realLogits = discriminator(realImages)
             let fakeLogits = discriminator(fakeImages)
             let loss = discriminatorLoss(realLogits: realLogits, fakeLogits: fakeLogits)