tf.hsig

import TensorFlow.Core (Tensor, Value, feed, encodeTensorData, Scalar(..))
import TensorFlow.Ops (constant, add, placeholder, sub, reduceSum, mul)
import TensorFlow.GenOps.Core (square)
import TensorFlow.Variable (readValue, initializedVariable, Variable)
import TensorFlow.Session (runSession, run, runWithFeeds)
import TensorFlow.Minimize (gradientDescent, minimizeWith)
import Control.Monad (replicateM_)
import qualified Data.Vector as Vector
import Data.Vector (Vector)

runVariable :: Vector Float -> Vector Float -> IO (Float, Float)
runVariable xInput yInput = runSession $ do
  let xSize = fromIntegral $ Vector.length xInput
  let ySize = fromIntegral $ Vector.length yInput
  (w :: Variable Float) <- initializedVariable 3
  (b :: Variable Float) <- initializedVariable 1

runVariable :: Vector Float -> Vector Float -> IO (Float, Float)
  ...
  (x :: Tensor Value Float) <- placeholder [xSize]
  let linear_model = ((readValue w) `mul` x) `add` (readValue b)
  (y :: Tensor Value Float) <- placeholder [ySize]
  let square_deltas = square (linear_model `sub` y)
  let loss = reduceSum square_deltas
  trainStep <- minimizeWith (gradientDescent 0.01) loss [w,b] 
  let trainWithFeeds = \xF yF -> runWithFeeds
        [ feed x xF
        , feed y yF
        ]
        trainStep

runVariable :: Vector Float -> Vector Float -> IO (Float, Float)
...
  replicateM_ 1000 
    (trainWithFeeds (encodeTensorData [xSize] xInput) (encodeTensorData [ySize] yInput))
  (Scalar w_learned, Scalar b_learned) <- run (readValue w, readValue b)
  return (w_learned, b_learned)

main :: IO ()
main = do
  results <- runVariable [1.0, 2.0, 3.0, 4.0] [4.0, 9.0, 14.0, 19.0]
  print results