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tensorflow-haskell/tensorflow-mnist/app/Main.hs
fkm3 f170df9d13 Support fetching storable vectors + use them in benchmark (#50)
In addition, you can now fetch TensorData directly. This might be useful in
scenarios where you feed the result of a computation back in, like RNN.

Before:

benchmarking feedFetch/4 byte
time                 83.31 μs   (81.88 μs .. 84.75 μs)
                     0.997 R²   (0.994 R² .. 0.998 R²)
mean                 87.32 μs   (86.06 μs .. 88.83 μs)
std dev              4.580 μs   (3.698 μs .. 5.567 μs)
variance introduced by outliers: 55% (severely inflated)

benchmarking feedFetch/4 KiB
time                 114.9 μs   (111.5 μs .. 118.2 μs)
                     0.996 R²   (0.994 R² .. 0.998 R²)
mean                 117.3 μs   (116.2 μs .. 118.6 μs)
std dev              3.877 μs   (3.058 μs .. 5.565 μs)
variance introduced by outliers: 31% (moderately inflated)

benchmarking feedFetch/4 MiB
time                 109.0 ms   (107.9 ms .. 110.7 ms)
                     1.000 R²   (0.999 R² .. 1.000 R²)
mean                 108.6 ms   (108.2 ms .. 109.2 ms)
std dev              740.2 μs   (353.2 μs .. 1.186 ms)

After:

benchmarking feedFetch/4 byte
time                 82.92 μs   (80.55 μs .. 85.24 μs)
                     0.996 R²   (0.993 R² .. 0.998 R²)
mean                 83.58 μs   (82.34 μs .. 84.89 μs)
std dev              4.327 μs   (3.664 μs .. 5.375 μs)
variance introduced by outliers: 54% (severely inflated)

benchmarking feedFetch/4 KiB
time                 85.69 μs   (83.81 μs .. 87.30 μs)
                     0.997 R²   (0.996 R² .. 0.999 R²)
mean                 86.99 μs   (86.11 μs .. 88.15 μs)
std dev              3.608 μs   (2.854 μs .. 5.273 μs)
variance introduced by outliers: 43% (moderately inflated)

benchmarking feedFetch/4 MiB
time                 1.582 ms   (1.509 ms .. 1.677 ms)
                     0.970 R²   (0.936 R² .. 0.993 R²)
mean                 1.645 ms   (1.554 ms .. 1.981 ms)
std dev              490.6 μs   (138.9 μs .. 1.067 ms)
variance introduced by outliers: 97% (severely inflated)
2016-12-14 18:53:06 -08:00

150 lines
5.6 KiB
Haskell

-- 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 (zipWithM, when, 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.Core as TF
import qualified TensorFlow.Gradient as TF
import qualified TensorFlow.Ops 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.Value Float)
randomParam width (TF.Shape shape) =
(* stddev) <$> TF.truncatedNormal (TF.vector shape)
where
stddev = TF.scalar (1 / sqrt (fromIntegral width))
reduceMean :: TF.Tensor TF.Value Float -> TF.Tensor TF.Value Float
reduceMean xs = TF.mean xs (TF.scalar (0 :: Int32))
-- 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` hiddenWeights) `TF.add` hiddenBiases
let hidden = TF.relu hiddenZ
-- Logits.
logitWeights <-
TF.initializedVariable =<< randomParam numUnits [numUnits, numLabels]
logitBiases <- TF.zeroInitializedVariable [numLabels]
let logits = (hidden `TF.matMul` logitWeights) `TF.add` 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 =
reduceMean $ fst $ TF.softmaxCrossEntropyWithLogits logits labelVecs
params = [hiddenWeights, hiddenBiases, logitWeights, logitBiases]
grads <- TF.gradients loss params
let lr = TF.scalar 0.00001
applyGrad param 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_ [
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)