Misc MNIST example cleanup (#9)

* Use native oneHot op in the example code. It didn't exist when this was originally written.
* Misc cleanup in MNIST example

- Use unspecified dimension for batch size in model. This simplifies the
  code for the test set.
- Move error rate calculation into model.

tensorflow-mnist/app/Main.hs

@@ -12,21 +12,24 @@
 -- See the License for the specific language governing permissions and
 -- limitations under the License.
 
+{-# LANGUAGE FlexibleContexts #-}
 {-# LANGUAGE OverloadedLists #-}
 
 import Control.Monad (zipWithM, when, forM, forM_)
 import Control.Monad.IO.Class (liftIO)
 import Data.Int (Int32, Int64)
+import Data.List (genericLength)
 import qualified Data.Text.IO as T
 import qualified Data.Vector as V
 
-import qualified TensorFlow.ControlFlow as TF
 import qualified TensorFlow.Build as TF
+import qualified TensorFlow.ControlFlow as TF
+import qualified TensorFlow.Gradient as TF
+import qualified TensorFlow.Nodes as TF
 import qualified TensorFlow.Ops as TF
 import qualified TensorFlow.Session as TF
 import qualified TensorFlow.Tensor as TF
 import qualified TensorFlow.Types as TF
-import qualified TensorFlow.Gradient as TF
 
 import TensorFlow.Examples.MNIST.InputData
 import TensorFlow.Examples.MNIST.Parse
@@ -41,30 +44,10 @@ randomParam width (TF.Shape shape) =
   where
     stddev = TF.scalar (1 / sqrt (fromIntegral width))
 
--- Types must match due to model structure (sparseToDense requires
+reduceMean xs = TF.mean xs (TF.scalar (0 :: Int32))
--- index types to match)
+
+-- Types must match due to model structure.
 type LabelType = Int32
-type BatchSize = Int32
-
--- | Convert scalar labels to one-hot vectors.
-labelClasses :: TF.Tensor TF.Value LabelType
-             -> LabelType
-             -> BatchSize
-             -> TF.Tensor TF.Value Float
-labelClasses labels numClasses batchSize =
-    let indices = TF.range 0 (TF.scalar batchSize) 1
-        concated = TF.concat 1 [TF.expandDims indices 1, TF.expandDims labels 1]
-    in TF.sparseToDense concated
-       (TF.constant [2] [batchSize, numClasses])
-       1 {- ON value -}
-       0 {- default (OFF) value -}
-
--- | Fraction of elements that differ between two vectors.
-errorRate :: Eq a => V.Vector a -> V.Vector a -> Double
-errorRate xs ys = fromIntegral (len - numCorrect) / fromIntegral len
-  where
-    numCorrect = V.length $ V.filter id $ V.zipWith (==) xs ys
-    len = V.length xs
 
 data Model = Model {
       train :: TF.TensorData Float  -- ^ images
@@ -72,10 +55,15 @@ data Model = Model {
             -> TF.Session ()
     , infer :: TF.TensorData Float  -- ^ images
             -> TF.Session (V.Vector LabelType)  -- ^ predictions
+    , errorRate :: TF.TensorData Float  -- ^ images
+                -> TF.TensorData LabelType
+                -> TF.Session Float
     }
 
-createModel :: Int64 -> TF.Build Model
+createModel :: TF.Build Model
-createModel batchSize = do
+createModel = do
+    -- Use -1 batch size to support variable sized batches.
+    let batchSize = -1
     -- Inputs.
     images <- TF.placeholder [batchSize, numPixels]
     -- Hidden layer.
@@ -95,22 +83,29 @@ createModel batchSize = do
 
     -- Create training action.
     labels <- TF.placeholder [batchSize]
-    let labelVecs = labelClasses labels 10 (fromIntegral batchSize)
+    let labelVecs = TF.oneHot labels (fromIntegral numLabels) 1 0
-        loss = fst $ TF.softmaxCrossEntropyWithLogits logits labelVecs
+        loss =
+            reduceMean $ fst $ TF.softmaxCrossEntropyWithLogits logits labelVecs
         params = [hiddenWeights, hiddenBiases, logitWeights, logitBiases]
     grads <- TF.gradients loss params
 
-    let lr = TF.scalar $ 0.001 / fromIntegral batchSize
+    let lr = TF.scalar 0.00001
-        applyGrad param grad
+        applyGrad param grad = TF.assign param $ param `TF.sub` (lr * grad)
-            = TF.assign param $ param `TF.sub` (lr * grad)
     trainStep <- TF.group =<< zipWithM applyGrad params grads
 
+    let correctPredictions = TF.equal predict labels
+    errorRateTensor <- TF.render $ 1 - reduceMean (TF.cast correctPredictions)
+
     return Model {
-        train = \imFeed lFeed -> TF.runWithFeeds_ [
+          train = \imFeed lFeed -> TF.runWithFeeds_ [
-              TF.feed images imFeed
+                TF.feed images imFeed
-            , TF.feed labels lFeed
+              , TF.feed labels lFeed
-            ] trainStep
+              ] trainStep
         , infer = \imFeed -> TF.runWithFeeds [TF.feed images imFeed] predict
+        , errorRate = \imFeed lFeed -> TF.unScalar <$> TF.runWithFeeds [
+                TF.feed images imFeed
+              , TF.feed labels lFeed
+              ] errorRateTensor
         }
 
 main = TF.runSession $ do
@@ -120,40 +115,36 @@ main = TF.runSession $ do
     testImages <- liftIO (readMNISTSamples =<< testImageData)
     testLabels <- liftIO (readMNISTLabels =<< testLabelData)
 
-    let batchSize = 100 :: Int64
-
     -- Create the model.
-    model <- TF.build $ createModel batchSize
+    model <- TF.build createModel
 
-    -- Helpers for generate batches.
+    -- Functions for generating batches.
-    let selectBatch i xs = take size $ drop (i * size) $ cycle xs
+    let encodeImageBatch xs =
-          where size = fromIntegral batchSize
+            TF.encodeTensorData [genericLength xs, numPixels]
-    let getImageBatch i xs = TF.encodeTensorData
+                                (fromIntegral <$> mconcat xs)
-            [batchSize, numPixels]
+    let encodeLabelBatch xs =
-            $ fromIntegral <$> mconcat (selectBatch i xs)
+            TF.encodeTensorData [genericLength xs]
-    let getExpectedLabelBatch i xs =
+                                (fromIntegral <$> V.fromList xs)
-            fromIntegral <$> V.fromList (selectBatch i xs)
+    let batchSize = 100
+    let selectBatch i xs = take batchSize $ drop (i * batchSize) (cycle xs)
 
     -- Train.
     forM_ ([0..1000] :: [Int]) $ \i -> do
-        let images = getImageBatch i trainingImages
+        let images = encodeImageBatch (selectBatch i trainingImages)
-            labels = getExpectedLabelBatch i trainingLabels
+            labels = encodeLabelBatch (selectBatch i trainingLabels)
-        train model images (TF.encodeTensorData [batchSize] labels)
+        train model images labels
         when (i `mod` 100 == 0) $ do
-            preds <- infer model images
+            err <- errorRate model images labels
-            liftIO $ putStrLn $
+            liftIO $ putStrLn $ "training error " ++ show (err * 100)
-                "training error " ++ show (errorRate preds labels * 100)
     liftIO $ putStrLn ""
 
     -- Test.
-    let numTestBatches = length testImages `div` fromIntegral batchSize
+    testErr <- errorRate model (encodeImageBatch testImages)
-    testPreds <- fmap mconcat $ forM [0..numTestBatches] $ \i -> do
+                               (encodeLabelBatch testLabels)
-        infer model (getImageBatch i testImages)
+    liftIO $ putStrLn $ "test error " ++ show (testErr * 100)
-    let testExpected = fromIntegral <$> V.fromList testLabels
-    liftIO $ putStrLn $
-        "test error " ++ show (errorRate testPreds testExpected * 100)
 
     -- Show some predictions.
+    testPreds <- infer model (encodeImageBatch testImages)
     liftIO $ forM_ ([0..3] :: [Int]) $ \i -> do
         putStrLn ""
         T.putStrLn $ drawMNIST $ testImages !! i

tensorflow-ops/src/TensorFlow/Ops.hs

@@ -65,12 +65,15 @@ module TensorFlow.Ops
     , CoreOps.cast
     , CoreOps.concat
     , constant
+    , CoreOps.equal
     , expandDims
     , initializedVariable
     , zeroInitializedVariable
     , CoreOps.fill
+    , CoreOps.oneHot
     , CoreOps.matMul
     , matTranspose
+    , CoreOps.mean
     , CoreOps.mul
     , CoreOps.neg
     , CoreOps.pack