tensorflow-haskell/tensorflow-mnist/app/Main.hs

-- Copyright 2016 TensorFlow authors.
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.

{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE OverloadedLists #-}

import Control.Monad (forM_, when)
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.Core as TF
import qualified TensorFlow.Ops as TF hiding (initializedVariable, zeroInitializedVariable)
import qualified TensorFlow.Variable as TF
import qualified TensorFlow.Minimize as TF

import TensorFlow.Examples.MNIST.InputData
import TensorFlow.Examples.MNIST.Parse

numPixels, numLabels :: Int64
numPixels = 28*28 :: Int64
numLabels = 10 :: Int64

-- | Create tensor with random values where the stddev depends on the width.
randomParam :: Int64 -> TF.Shape -> TF.Build (TF.Tensor TF.Build Float)
randomParam width (TF.Shape shape) =
    (`TF.mul` stddev) <$> TF.truncatedNormal (TF.vector shape)
  where
    stddev = TF.scalar (1 / sqrt (fromIntegral width))

-- Types must match due to model structure.
type LabelType = Int32

data Model = Model {
      train :: TF.TensorData Float  -- ^ images
            -> TF.TensorData LabelType
            -> 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 :: TF.Build Model
createModel = do
    -- Use -1 batch size to support variable sized batches.
    let batchSize = -1
    -- Inputs.
    images <- TF.placeholder [batchSize, numPixels]
    -- Hidden layer.
    let numUnits = 500
    hiddenWeights <-
        TF.initializedVariable =<< randomParam numPixels [numPixels, numUnits]
    hiddenBiases <- TF.zeroInitializedVariable [numUnits]
    let hiddenZ = (images `TF.matMul` TF.readValue hiddenWeights)
                  `TF.add` TF.readValue hiddenBiases
    let hidden = TF.relu hiddenZ
    -- Logits.
    logitWeights <-
        TF.initializedVariable =<< randomParam numUnits [numUnits, numLabels]
    logitBiases <- TF.zeroInitializedVariable [numLabels]
    let logits = (hidden `TF.matMul` TF.readValue logitWeights)
                 `TF.add` TF.readValue logitBiases
    predict <- TF.render $ TF.cast $
               TF.argMax (TF.softmax logits) (TF.scalar (1 :: LabelType))

    -- Create training action.
    labels <- TF.placeholder [batchSize]
    let labelVecs = TF.oneHot labels (fromIntegral numLabels) 1 0
        loss =
            TF.reduceMean $ fst $ TF.softmaxCrossEntropyWithLogits logits labelVecs
        params = [hiddenWeights, hiddenBiases, logitWeights, logitBiases]
    trainStep <- TF.minimizeWith TF.adam loss params

    let correctPredictions = TF.equal predict labels
    errorRateTensor <- TF.render $ 1 - TF.reduceMean (TF.cast correctPredictions)

    return Model {
          train = \imFeed lFeed -> TF.runWithFeeds_ [
                TF.feed images imFeed
              , TF.feed labels lFeed
              ] 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 :: IO ()
main = TF.runSession $ do
    -- Read training and test data.
    trainingImages <- liftIO (readMNISTSamples =<< trainingImageData)
    trainingLabels <- liftIO (readMNISTLabels =<< trainingLabelData)
    testImages <- liftIO (readMNISTSamples =<< testImageData)
    testLabels <- liftIO (readMNISTLabels =<< testLabelData)

    -- Create the model.
    model <- TF.build createModel

    -- Functions for generating batches.
    let encodeImageBatch xs =
            TF.encodeTensorData [genericLength xs, numPixels]
                                (fromIntegral <$> mconcat xs)
    let encodeLabelBatch xs =
            TF.encodeTensorData [genericLength xs]
                                (fromIntegral <$> V.fromList xs)
    let batchSize = 100
    let selectBatch i xs = take batchSize $ drop (i * batchSize) (cycle xs)

    -- Train.
    forM_ ([0..1000] :: [Int]) $ \i -> do
        let images = encodeImageBatch (selectBatch i trainingImages)
            labels = encodeLabelBatch (selectBatch i trainingLabels)
        train model images labels
        when (i `mod` 100 == 0) $ do
            err <- errorRate model images labels
            liftIO $ putStrLn $ "training error " ++ show (err * 100)
    liftIO $ putStrLn ""

    -- Test.
    testErr <- errorRate model (encodeImageBatch testImages)
                               (encodeLabelBatch testLabels)
    liftIO $ putStrLn $ "test error " ++ show (testErr * 100)

    -- Show some predictions.
    testPreds <- infer model (encodeImageBatch testImages)
    liftIO $ forM_ ([0..3] :: [Int]) $ \i -> do
        putStrLn ""
        T.putStrLn $ drawMNIST $ testImages !! i
        putStrLn $ "expected " ++ show (testLabels !! i)
        putStrLn $ "     got " ++ show (testPreds V.! i)