40 lines
1.4 KiB
Haskell
40 lines
1.4 KiB
Haskell
-- | Simple linear regression example for the README.
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import Control.Monad (replicateM, replicateM_)
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import System.Random (randomIO)
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import Test.HUnit (assertBool)
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.GenOps.Core as TF
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import qualified TensorFlow.Minimize as TF
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import qualified TensorFlow.Ops as TF hiding (initializedVariable)
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import qualified TensorFlow.Variable as TF
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main :: IO ()
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main = do
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-- Generate data where `y = x*3 + 8`.
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xData <- replicateM 100 randomIO
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let yData = [x*3 + 8 | x <- xData]
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-- Fit linear regression model.
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(w, b) <- fit xData yData
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assertBool "w == 3" (abs (3 - w) < 0.001)
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assertBool "b == 8" (abs (8 - b) < 0.001)
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fit :: [Float] -> [Float] -> IO (Float, Float)
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fit xData yData = TF.runSession $ do
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-- Create tensorflow constants for x and y.
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let x = TF.vector xData
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y = TF.vector yData
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-- Create scalar variables for slope and intercept.
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w <- TF.initializedVariable 0
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b <- TF.initializedVariable 0
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-- Define the loss function.
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let yHat = (x `TF.mul` TF.readValue w) `TF.add` TF.readValue b
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loss = TF.square (yHat `TF.sub` y)
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-- Optimize with gradient descent.
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trainStep <- TF.minimizeWith (TF.gradientDescent 0.001) loss [w, b]
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replicateM_ 1000 (TF.run trainStep)
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-- Return the learned parameters.
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(TF.Scalar w', TF.Scalar b') <- TF.run (TF.readValue w, TF.readValue b)
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return (w', b')
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