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tweak gradient calls to match api change #235

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Dec 4, 2019
Merged

tweak gradient calls to match api change #235

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2 changes: 1 addition & 1 deletion Autoencoder/main.swift
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Expand Up @@ -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)
}
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2 changes: 1 addition & 1 deletion Benchmarks/Models/ImageClassificationTraining.swift
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Expand Up @@ -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)
}
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2 changes: 1 addition & 1 deletion Examples/LeNet-MNIST/main.swift
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Expand Up @@ -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(
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4 changes: 2 additions & 2 deletions GAN/main.swift
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Expand Up @@ -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)
Expand All @@ -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)
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