tensorflow-mnist (empty) → 0.1.0.0
raw patch · 10 files changed
+734/−0 lines, 10 filesdep +HUnitdep +basedep +binarysetup-changedbinary-added
Dependencies added: HUnit, base, binary, bytestring, containers, filepath, lens-family, proto-lens, split, tensorflow, tensorflow-core-ops, tensorflow-mnist, tensorflow-mnist-input-data, tensorflow-ops, tensorflow-proto, test-framework, test-framework-hunit, text, transformers, vector, zlib
Files
- LICENSE +203/−0
- Setup.hs +3/−0
- app/Main.hs +147/−0
- data/MNIST.pb binary
- data/MNISTBias.ckpt binary
- data/MNISTWts.ckpt binary
- src-data/TensorFlow/Examples/MNIST/TrainedGraph.hs +30/−0
- src/TensorFlow/Examples/MNIST/Parse.hs +96/−0
- tensorflow-mnist.cabal +80/−0
- tests/ParseTest.hs +175/−0
+ LICENSE view
@@ -0,0 +1,203 @@+Copyright 2016 The TensorFlow Authors. All rights reserved.++ Apache License+ Version 2.0, January 2004+ http://www.apache.org/licenses/++ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION++ 1. Definitions.++ "License" shall mean the terms and conditions for use, reproduction,+ and distribution as defined by Sections 1 through 9 of this document.++ "Licensor" shall mean the copyright owner or entity authorized by+ the copyright owner that is granting the License.++ "Legal Entity" shall mean the union of the acting entity and all+ other entities that control, are controlled by, or are under common+ control with that entity. 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We also recommend that a+ file or class name and description of purpose be included on the+ same "printed page" as the copyright notice for easier+ identification within third-party archives.++ Copyright 2016, The 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.
+ Setup.hs view
@@ -0,0 +1,3 @@+import Distribution.Simple++main = defaultMain
+ app/Main.hs view
@@ -0,0 +1,147 @@+-- 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 #-}+{-# LANGUAGE TypeApplications #-}++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.Build @LabelType $+ 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)
+ data/MNIST.pb view
binary file changed (absent → 10086 bytes)
+ data/MNISTBias.ckpt view
binary file changed (absent → 188 bytes)
+ data/MNISTWts.ckpt view
binary file changed (absent → 31538 bytes)
+ src-data/TensorFlow/Examples/MNIST/TrainedGraph.hs view
@@ -0,0 +1,30 @@+-- 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 OverloadedStrings #-}+-- | Paths to test helper files.+module TensorFlow.Examples.MNIST.TrainedGraph where++import Paths_tensorflow_mnist (getDataFileName)+import Data.ByteString (ByteString)+import Data.ByteString.Char8 (pack)++-- | File containing a Tensorflow serialized proto of MNIST.+mnistPb :: IO FilePath+mnistPb = getDataFileName "data/MNIST.pb"++-- | Files containing pre-trained weights for MNIST.+wtsCkpt, biasCkpt :: IO ByteString+wtsCkpt = pack <$> getDataFileName "data/MNISTWts.ckpt"+biasCkpt = pack <$> getDataFileName "data/MNISTBias.ckpt"
+ src/TensorFlow/Examples/MNIST/Parse.hs view
@@ -0,0 +1,96 @@+-- 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 OverloadedStrings #-}+{-# LANGUAGE OverloadedLists #-}+{-# LANGUAGE TypeSynonymInstances #-}+{-# LANGUAGE FlexibleInstances #-}+{-# LANGUAGE ViewPatterns #-}++module TensorFlow.Examples.MNIST.Parse where++import Control.Monad (when, liftM)+import Data.Binary.Get (Get, runGet, getWord32be, getLazyByteString)+import Data.ByteString.Lazy (toStrict, readFile)+import Data.List.Split (chunksOf)+import Data.Monoid ((<>))+import Data.ProtoLens (Message, decodeMessageOrDie)+import Data.Text (Text)+import Data.Word (Word8, Word32)+import Prelude hiding (readFile)+import qualified Codec.Compression.GZip as GZip+import qualified Data.ByteString.Lazy as L+import qualified Data.Text as Text+import qualified Data.Vector as V++-- | Utilities specific to MNIST.+type MNIST = V.Vector Word8++-- | Produces a unicode rendering of the MNIST digit sample.+drawMNIST :: MNIST -> Text+drawMNIST = chunk . block+ where+ block :: V.Vector Word8 -> Text+ block (V.splitAt 1 -> ([0], xs)) = " " <> block xs+ block (V.splitAt 1 -> ([n], xs)) = c `Text.cons` block xs+ where c = "\9617\9618\9619\9608" !! fromIntegral (n `div` 64)+ block (V.splitAt 1 -> _) = ""+ chunk :: Text -> Text+ chunk "" = "\n"+ chunk xs = Text.take 28 xs <> "\n" <> chunk (Text.drop 28 xs)++-- | Check's the file's endianess, throwing an error if it's not as expected.+checkEndian :: Get ()+checkEndian = do+ magic <- getWord32be+ when (magic `notElem` ([2049, 2051] :: [Word32])) $+ fail "Expected big endian, but image file is little endian."++-- | Reads an MNIST file and returns a list of samples.+readMNISTSamples :: FilePath -> IO [MNIST]+readMNISTSamples path = do+ raw <- GZip.decompress <$> readFile path+ return $ runGet getMNIST raw+ where+ getMNIST :: Get [MNIST]+ getMNIST = do+ checkEndian+ -- Parse header data.+ cnt <- liftM fromIntegral getWord32be+ rows <- liftM fromIntegral getWord32be+ cols <- liftM fromIntegral getWord32be+ -- Read all of the data, then split into samples.+ pixels <- getLazyByteString $ fromIntegral $ cnt * rows * cols+ return $ V.fromList <$> chunksOf (rows * cols) (L.unpack pixels)++-- | Reads a list of MNIST labels from a file and returns them.+readMNISTLabels :: FilePath -> IO [Word8]+readMNISTLabels path = do+ raw <- GZip.decompress <$> readFile path+ return $ runGet getLabels raw+ where getLabels :: Get [Word8]+ getLabels = do+ checkEndian+ -- Parse header data.+ cnt <- liftM fromIntegral getWord32be+ -- Read all of the labels.+ L.unpack <$> getLazyByteString cnt++readMessageFromFileOrDie :: Message m => FilePath -> IO m+readMessageFromFileOrDie path = do+ pb <- readFile path+ return $ decodeMessageOrDie $ toStrict pb++-- TODO: Write a writeMessageFromFileOrDie and read/write non-lethal+-- versions.
+ tensorflow-mnist.cabal view
@@ -0,0 +1,80 @@+name: tensorflow-mnist+version: 0.1.0.0+synopsis: TensorFlow demo application for learning MNIST model.+description: Please see README.md+homepage: https://github.com/tensorflow/haskell#readme+license: Apache+license-file: LICENSE+author: TensorFlow authors+maintainer: tensorflow-haskell@googlegroups.com+copyright: Google Inc.+category: Machine Learning+build-type: Simple+cabal-version: >=1.22+data-files: data/*.ckpt+ , data/*.pb++library+ hs-source-dirs: src+ , src-data+ exposed-modules: TensorFlow.Examples.MNIST.Parse+ , TensorFlow.Examples.MNIST.TrainedGraph+ other-modules: Paths_tensorflow_mnist+ build-depends: proto-lens == 0.2.*+ , base >= 4.7 && < 5+ , binary+ , bytestring+ , filepath+ , lens-family+ , containers+ , split+ , tensorflow-proto == 0.2.*+ , tensorflow-core-ops == 0.2.*+ , tensorflow == 0.2.*+ , text+ , vector+ , zlib+ default-language: Haskell2010++executable Main+ default-language: Haskell2010+ main-is: Main.hs+ hs-source-dirs: app+ build-depends: base+ , bytestring+ , filepath+ , lens-family+ , proto-lens+ , tensorflow+ , tensorflow-mnist+ , tensorflow-mnist-input-data+ , tensorflow-ops+ , tensorflow-proto+ , text+ , transformers+ , vector++Test-Suite ParseTest+ default-language: Haskell2010+ type: exitcode-stdio-1.0+ main-is: ParseTest.hs+ hs-source-dirs: tests+ build-depends: HUnit+ , base+ , bytestring+ , proto-lens+ , lens-family+ , tensorflow+ , tensorflow-mnist+ , tensorflow-mnist-input-data+ , tensorflow-ops+ , tensorflow-proto+ , test-framework+ , test-framework-hunit+ , text+ , transformers+ , vector++source-repository head+ type: git+ location: https://github.com/tensorflow/haskell
+ tests/ParseTest.hs view
@@ -0,0 +1,175 @@+-- 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 OverloadedLists #-}+{-# LANGUAGE OverloadedStrings #-}++module Main where++import Control.Monad.IO.Class (liftIO)+import Data.Int (Int64)+import Data.Text (Text)+import qualified Data.Text.IO as Text+import Lens.Family2 ((&), (.~), (^.))+import Prelude hiding (abs)+import Proto.Tensorflow.Core.Framework.Graph+ ( GraphDef(..)+ , version+ , node )+import Proto.Tensorflow.Core.Framework.NodeDef+ ( NodeDef(..)+ , op )+import System.IO as IO+import TensorFlow.Examples.MNIST.InputData+import TensorFlow.Examples.MNIST.Parse+import TensorFlow.Examples.MNIST.TrainedGraph+import TensorFlow.Build+ ( asGraphDef+ , addGraphDef+ , Build+ )+import TensorFlow.Tensor+ ( Tensor(..)+ , Ref+ , feed+ , render+ , tensorFromName+ , tensorValueFromName+ )+import TensorFlow.Ops+import TensorFlow.Session+ (runSession, run, run_, runWithFeeds, build)+import TensorFlow.Types (TensorDataType(..), Shape(..), unScalar)+import Test.Framework (defaultMain, Test)+import Test.Framework.Providers.HUnit (testCase)+import Test.HUnit ((@=?), Assertion)+import qualified Data.Vector as V++-- | Test that a file can be read and the GraphDef proto correctly parsed.+testReadMessageFromFileOrDie :: Test+testReadMessageFromFileOrDie = testCase "testReadMessageFromFileOrDie" $ do+ -- Check the function on a known well-formatted file.+ mnist <- readMessageFromFileOrDie =<< mnistPb :: IO GraphDef+ -- Simple field read.+ 1 @=? mnist^.version+ -- Count the number of nodes.+ let nodes :: [NodeDef]+ nodes = mnist^.node+ 100 @=? length nodes+ -- Check that the expected op is found at an arbitrary index.+ "Variable" @=? nodes!!6^.op++-- | Parse the test set for label and image data. Will only fail if the file is+-- missing or incredibly corrupt.+testReadMNIST :: Test+testReadMNIST = testCase "testReadMNIST" $ do+ imageData <- readMNISTSamples =<< testImageData+ 10000 @=? length imageData+ labelData <- readMNISTLabels =<< testLabelData+ 10000 @=? length labelData++testNodeName :: Text -> Tensor Build a -> Assertion+testNodeName n g = n @=? opName+ where+ opName = head (gDef^.node)^.op+ gDef = asGraphDef $ render g++testGraphDefGen :: Test+testGraphDefGen = testCase "testGraphDefGen" $ do+ -- Test the inferred operation type.+ let f0 :: Tensor Build Float+ f0 = 0+ testNodeName "Const" f0+ testNodeName "Add" $ 1 + f0+ testNodeName "Mul" $ 1 * f0+ testNodeName "Sub" $ 1 - f0+ testNodeName "Abs" $ abs f0+ testNodeName "Sign" $ signum f0+ testNodeName "Neg" $ -f0+ -- Test the grouping.+ testNodeName "Add" $ 1 + f0 * 2+ testNodeName "Add" $ 1 + (f0 * 2)+ testNodeName "Mul" $ (1 + f0) * 2++-- | Convert a simple graph to GraphDef, load it, run it, and check the output.+testGraphDefExec :: Test+testGraphDefExec = testCase "testGraphDefExec" $ do+ let graphDef = asGraphDef $ render $ scalar (5 :: Float) * 10+ runSession $ do+ addGraphDef graphDef+ x <- run $ tensorValueFromName "Mul_2"+ liftIO $ (50 :: Float) @=? unScalar x++-- | Load MNIST from a GraphDef and the weights from a checkpoint and run on+-- sample data.+testMNISTExec :: Test+testMNISTExec = testCase "testMNISTExec" $ do+ -- Switch to unicode to enable pretty printing of MNIST digits.+ IO.hSetEncoding IO.stdout IO.utf8++ -- Parse the Graph definition, samples, & labels from files.+ mnist <- readMessageFromFileOrDie =<< mnistPb :: IO GraphDef+ mnistSamples <- readMNISTSamples =<< testImageData+ mnistLabels <- readMNISTLabels =<< testLabelData++ -- Select a sample to run on and convert it into a TensorData of Floats.+ let idx = 12+ sample :: MNIST+ sample = mnistSamples !! idx+ label = mnistLabels !! idx+ tensorSample = encodeTensorData (Shape [1,784]) floatSample+ where+ floatSample :: V.Vector Float+ floatSample = V.map fromIntegral sample+ Text.putStrLn $ drawMNIST sample++ -- Execute the graph on the sample data.+ runSession $ do+ -- The version of this session is 0, but the version of the graph is 1.+ -- Change the graph version to 0 so they're compatible.+ build $ addGraphDef $ mnist & version .~ 0+ -- Define nodes that restore saved weights and biases.+ let bias, wts :: Tensor Ref Float+ bias = tensorFromName "Variable"+ wts = tensorFromName "weights"+ wtsCkptPath <- liftIO wtsCkpt+ biasCkptPath <- liftIO biasCkpt+ -- Run those restoring nodes on the graph in the current session.+ run_ =<< (sequence :: Monad m => [m a] -> m [a])+ [ restore wtsCkptPath wts+ , restoreFromName biasCkptPath "bias" bias+ ]+ -- Encode the expected sample data as one-hot data.+ let ty = encodeTensorData [10] oneHotLabels+ where oneHotLabels = V.replicate 10 (0 :: Float) V.// updates+ updates = [(fromIntegral label, 1)]+ let feeds = [ feed (tensorValueFromName "x-input") tensorSample+ , feed (tensorValueFromName "y-input") ty+ ]+ -- Run the graph with the input feeds and read the ArgMax'd result from+ -- the test (not training) side of the evaluation.+ x <- runWithFeeds feeds $ tensorValueFromName "test/ArgMax"+ -- Print the trained model's predicted outcome.+ liftIO $ putStrLn $ "Expectation: " ++ show label ++ "\n"+ ++ "Prediction: " ++ show (unScalar x :: Int64)+ -- Check whether the prediction matches the expectation.+ liftIO $ (fromInteger . toInteger $ label :: Int64) @=? unScalar x++main :: IO ()+main = defaultMain+ [ testReadMessageFromFileOrDie+ , testReadMNIST+ , testGraphDefGen+ , testGraphDefExec+ , testMNISTExec ]