mirror of
https://github.com/tensorflow/haskell.git
synced 2024-11-26 21:09:44 +01:00
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)
This commit is contained in:
fkm3
Judah Jacobson
parent
91f508eb5c
commit
f170df9d13
11 changed files with 154 additions and 114 deletions
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@ -22,14 +22,9 @@ import Data.List (genericLength)
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import qualified Data.Text.IO as T
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import qualified Data.Vector as V
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import qualified TensorFlow.Build as TF
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import qualified TensorFlow.ControlFlow as TF
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.Gradient as TF
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import qualified TensorFlow.Nodes as TF
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import qualified TensorFlow.Ops as TF
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import qualified TensorFlow.Session as TF
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import qualified TensorFlow.Tensor as TF
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import qualified TensorFlow.Types as TF
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import TensorFlow.Examples.MNIST.InputData
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import TensorFlow.Examples.MNIST.Parse
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@ -48,10 +48,9 @@ import TensorFlow.Tensor
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, tensorFromName
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)
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import TensorFlow.Ops
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import TensorFlow.Nodes (unScalar)
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import TensorFlow.Session
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(runSession, run, run_, runWithFeeds, build, buildAnd)
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import TensorFlow.Types (TensorType(..), Shape(..))
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import TensorFlow.Types (TensorDataType(..), Shape(..), unScalar)
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import Test.Framework (Test)
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import Test.Framework.Providers.HUnit (testCase)
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import Test.HUnit ((@=?), Assertion)
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@ -42,7 +42,7 @@ import TensorFlow.Build
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, withNameScope
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)
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import TensorFlow.ControlFlow (named)
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import TensorFlow.Nodes (unScalar)
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import TensorFlow.Types (unScalar)
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import TensorFlow.Ops
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( add
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, assign
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@ -12,6 +12,7 @@
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE ScopedTypeVariables #-}
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import Data.Int (Int32, Int64)
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@ -32,7 +33,7 @@ import qualified TensorFlow.Types as TF
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-- DynamicSplit is undone with DynamicStitch to get the original input
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-- back.
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testDynamicPartitionStitchInverse :: forall a.
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(TF.TensorType a, Show a, Eq a) => StitchExample a -> Property
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(TF.TensorDataType V.Vector a, Show a, Eq a) => StitchExample a -> Property
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testDynamicPartitionStitchInverse (StitchExample numParts values partitions) =
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let splitParts :: [TF.Tensor TF.Value a] =
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CoreOps.dynamicPartition numParts (TF.vector values) partTensor
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@ -12,6 +12,7 @@
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE RankNTypes #-}
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{-# LANGUAGE ScopedTypeVariables #-}
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@ -129,8 +130,9 @@ testEmbeddingLookupGradients = testCase "testEmbeddingLookupGradients" $ do
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-- Verifies that direct gather is the same as dynamic split into
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-- partitions, followed by embedding lookup.
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testEmbeddingLookupUndoesSplit :: forall a. (TF.TensorType a, Show a, Eq a)
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=> LookupExample a -> Property
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testEmbeddingLookupUndoesSplit ::
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forall a. (TF.TensorDataType V.Vector a, Show a, Eq a)
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=> LookupExample a -> Property
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testEmbeddingLookupUndoesSplit
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(LookupExample numParts
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shape@(TF.Shape (firstDim : restDims))
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@ -5,7 +5,7 @@ import Control.Exception (evaluate)
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import Control.Monad.IO.Class (liftIO)
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import Criterion.Main (defaultMain, bgroup, bench)
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import Criterion.Types (Benchmarkable(..))
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import qualified Data.Vector as V
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import qualified Data.Vector.Storable as S
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.Ops as TF
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@ -23,12 +23,12 @@ nfSession init x = Benchmarkable $ \m -> TF.runSession $ do
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go m
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-- | Benchmark feeding and fetching a vector.
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feedFetchBenchmark :: TF.Session (V.Vector Float -> TF.Session (V.Vector Float))
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feedFetchBenchmark :: TF.Session (S.Vector Float -> TF.Session (S.Vector Float))
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feedFetchBenchmark = do
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input <- TF.build (TF.placeholder (TF.Shape [-1]))
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output <- TF.build (TF.render (TF.identity input))
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return $ \v -> do
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let shape = TF.Shape [fromIntegral (V.length v)]
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let shape = TF.Shape [fromIntegral (S.length v)]
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inputData = TF.encodeTensorData shape v
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feeds = [TF.feed input inputData]
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TF.runWithFeeds feeds output
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@ -36,8 +36,8 @@ feedFetchBenchmark = do
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main :: IO ()
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main = defaultMain
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[ bgroup "feedFetch"
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[ bench "4 byte" $ nfSession feedFetchBenchmark (V.replicate 1 0)
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, bench "4 KiB" $ nfSession feedFetchBenchmark (V.replicate 1024 0)
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, bench "4 MiB" $ nfSession feedFetchBenchmark (V.replicate (1024^2) 0)
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[ bench "4 byte" $ nfSession feedFetchBenchmark (S.replicate 1 0)
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, bench "4 KiB" $ nfSession feedFetchBenchmark (S.replicate 1024 0)
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, bench "4 MiB" $ nfSession feedFetchBenchmark (S.replicate (1024^2) 0)
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]
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]
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@ -85,7 +85,7 @@ instance Arbitrary a => Arbitrary (TensorDataInputs a) where
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return $ TensorDataInputs sizes (V.fromList elems)
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-- Test that a vector is unchanged after being encoded and decoded.
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encodeDecodeProp :: (TF.TensorType a, Eq a) => TensorDataInputs a -> Bool
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encodeDecodeProp :: (TF.TensorDataType V.Vector a, Eq a) => TensorDataInputs a -> Bool
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encodeDecodeProp (TensorDataInputs shape vec) =
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TF.decodeTensorData (TF.encodeTensorData (TF.Shape shape) vec) == vec
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@ -20,7 +20,7 @@ module Main where
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import Control.Monad.IO.Class (liftIO)
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import Data.Int (Int64)
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import Google.Test (googleTest)
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import TensorFlow.Nodes (Scalar(..))
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import TensorFlow.Types (Scalar(..))
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import TensorFlow.Ops (scalar)
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import TensorFlow.Queue
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import TensorFlow.Session
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@ -36,7 +36,6 @@ module TensorFlow.Core
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, buildWithSummary
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-- ** Running graphs
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, Fetchable
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, Scalar(..)
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, Nodes
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, run
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, run_
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@ -64,8 +63,10 @@ module TensorFlow.Core
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, value
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, tensorFromName
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-- ** Element types
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, TensorType
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, TensorData
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, TensorType(decodeTensorData, encodeTensorData)
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, TensorDataType(decodeTensorData, encodeTensorData)
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, Scalar(..)
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, Shape(..)
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, OneOf
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, type (/=)
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@ -12,8 +12,8 @@
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE FlexibleInstances #-}
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{-# LANGUAGE GeneralizedNewtypeDeriving #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-# LANGUAGE RankNTypes #-}
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{-# LANGUAGE ScopedTypeVariables #-}
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@ -24,12 +24,10 @@ import Control.Applicative (liftA2, liftA3)
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import Data.Map.Strict (Map)
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import Data.Monoid ((<>))
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import Data.Set (Set)
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import Data.String (IsString)
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import Data.Text (Text)
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import Lens.Family2 ((^.))
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import qualified Data.Map.Strict as Map
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import qualified Data.Set as Set
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import qualified Data.Vector as V
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import TensorFlow.Build
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import TensorFlow.Output
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@ -101,18 +99,12 @@ instance a ~ () => Fetchable ControlNode a where
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instance Nodes (Tensor v a) where
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getNodes t = Set.singleton <$> getOrAddOp (t ^. tensorOutput . outputOp)
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fetchTensorList :: TensorType a => Tensor v a -> Build (Fetch (Shape, [a]))
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fetchTensorList t = fmap (fmap V.toList) <$> fetchTensorVector t
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fetchTensorVector :: forall a v . TensorType a
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=> Tensor v a -> Build (Fetch (Shape, V.Vector a))
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=> Tensor v a -> Build (Fetch (TensorData a))
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fetchTensorVector (Tensor _ o) = do
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outputName <- renderOutput o
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return $ Fetch (Set.singleton outputName) $ \tensors ->
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let tensorData = tensors Map.! outputName
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shape = Shape $ FFI.tensorDataDimensions tensorData
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vec = decodeTensorData $ TensorData tensorData
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expectedType = tensorType (undefined :: a)
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actualType = FFI.tensorDataType tensorData
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badTypeError = error $ "Bad tensor type: expected "
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@ -121,21 +113,12 @@ fetchTensorVector (Tensor _ o) = do
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++ show actualType
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in if expectedType /= actualType
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then badTypeError
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else (shape, vec)
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else TensorData tensorData
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-- The constraint "a ~ a'" means that the input/output of fetch can constrain
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-- the TensorType of each other.
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instance (TensorType a, a ~ a') => Fetchable (Tensor v a) (V.Vector a') where
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getFetch t = fmap snd <$> fetchTensorVector t
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instance (TensorType a, a ~ a') => Fetchable (Tensor v a) (TensorData a') where
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getFetch = fetchTensorVector
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newtype Scalar a = Scalar {unScalar :: a}
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deriving (Show, Eq, Ord, Num, Fractional, Floating, Real, RealFloat,
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RealFrac, IsString)
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instance (TensorType a, a ~ a') => Fetchable (Tensor v a) (Scalar a') where
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getFetch t = fmap (Scalar . headFromSingleton . snd) <$> fetchTensorList t
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where
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headFromSingleton [x] = x
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headFromSingleton xs
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= error $ "Unable to extract singleton from tensor of length "
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++ show (length xs)
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instance (TensorType a, TensorDataType s a, a ~ a') => Fetchable (Tensor v a) (s a') where
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getFetch t = fmap decodeTensorData <$> fetchTensorVector t
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@ -16,6 +16,7 @@
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{-# LANGUAGE DataKinds #-}
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE FlexibleInstances #-}
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{-# LANGUAGE GeneralizedNewtypeDeriving #-}
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{-# LANGUAGE MultiParamTypeClasses #-}
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{-# LANGUAGE OverloadedStrings #-}
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{-# LANGUAGE RankNTypes #-}
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@ -30,6 +31,8 @@
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module TensorFlow.Types
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( TensorType(..)
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, TensorData(..)
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, TensorDataType(..)
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, Scalar(..)
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, Shape(..)
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, protoShape
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, Attribute(..)
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@ -50,11 +53,13 @@ import Data.Complex (Complex)
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import Data.Default (def)
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import Data.Int (Int8, Int16, Int32, Int64)
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import Data.Monoid ((<>))
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import Data.String (IsString)
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import Data.Word (Word8, Word16, Word64)
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import Foreign.Storable (Storable)
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import GHC.Exts (Constraint, IsList(..))
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import Lens.Family2 (Lens', view, (&), (.~))
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import Lens.Family2.Unchecked (iso)
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import Text.Printf (printf)
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import qualified Data.Attoparsec.ByteString as Atto
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import Data.ByteString (ByteString)
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import qualified Data.ByteString as B
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@ -95,66 +100,31 @@ import Proto.Tensorflow.Core.Framework.Types (DataType(..))
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import TensorFlow.Internal.VarInt (getVarInt, putVarInt)
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import qualified TensorFlow.Internal.FFI as FFI
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-- | Data about a tensor that is encoded for the TensorFlow APIs.
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newtype TensorData a = TensorData { unTensorData :: FFI.TensorData }
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-- | The class of scalar types supported by tensorflow.
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class TensorType a where
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tensorType :: a -> DataType
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tensorRefType :: a -> DataType
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tensorVal :: Lens' TensorProto [a]
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-- | Decode the bytes of a TensorData into a Vector.
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decodeTensorData :: TensorData a -> V.Vector a
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-- | Encode a Vector into a TensorData.
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--
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-- The values should be in row major order, e.g.,
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--
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-- element 0: index (0, ..., 0)
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-- element 1: index (0, ..., 1)
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-- ...
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encodeTensorData :: Shape -> V.Vector a -> TensorData a
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-- All types, besides ByteString, are encoded as simple arrays and we can use
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-- Vector.Storable to encode/decode by type casting pointers.
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-- TODO(fmayle): Assert that the data type matches the return type.
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simpleDecode :: Storable a => TensorData a -> V.Vector a
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simpleDecode = S.convert . S.unsafeCast . FFI.tensorDataBytes . unTensorData
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simpleEncode :: forall a . (TensorType a, Storable a)
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=> Shape -> V.Vector a -> TensorData a
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simpleEncode (Shape xs)
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= TensorData . FFI.TensorData xs dt . S.unsafeCast . S.convert
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where
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dt = tensorType (undefined :: a)
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instance TensorType Float where
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tensorType _ = DT_FLOAT
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tensorRefType _ = DT_FLOAT_REF
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tensorVal = floatVal
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType Double where
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tensorType _ = DT_DOUBLE
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tensorRefType _ = DT_DOUBLE_REF
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tensorVal = doubleVal
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType Int32 where
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tensorType _ = DT_INT32
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tensorRefType _ = DT_INT32_REF
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tensorVal = intVal
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType Int64 where
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tensorType _ = DT_INT64
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tensorRefType _ = DT_INT64_REF
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tensorVal = int64Val
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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integral :: Integral a => Lens' [Int32] [a]
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integral = iso (fmap fromIntegral) (fmap fromIntegral)
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@ -163,40 +133,140 @@ instance TensorType Word8 where
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tensorType _ = DT_UINT8
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tensorRefType _ = DT_UINT8_REF
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tensorVal = intVal . integral
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType Word16 where
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tensorType _ = DT_UINT16
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tensorRefType _ = DT_UINT16_REF
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tensorVal = intVal . integral
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType Int16 where
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tensorType _ = DT_INT16
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tensorRefType _ = DT_INT16_REF
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tensorVal = intVal . integral
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType Int8 where
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tensorType _ = DT_INT8
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tensorRefType _ = DT_INT8_REF
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tensorVal = intVal . integral
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decodeTensorData = simpleDecode
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encodeTensorData = simpleEncode
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instance TensorType ByteString where
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tensorType _ = DT_STRING
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tensorRefType _ = DT_STRING_REF
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tensorVal = stringVal
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instance TensorType Bool where
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tensorType _ = DT_BOOL
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tensorRefType _ = DT_BOOL_REF
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tensorVal = boolVal
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instance TensorType (Complex Float) where
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tensorType _ = DT_COMPLEX64
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tensorRefType _ = DT_COMPLEX64
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tensorVal = error "TODO (Complex Float)"
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instance TensorType (Complex Double) where
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tensorType _ = DT_COMPLEX128
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tensorRefType _ = DT_COMPLEX128
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tensorVal = error "TODO (Complex Double)"
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-- | Tensor data with the correct memory layout for tensorflow.
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newtype TensorData a = TensorData { unTensorData :: FFI.TensorData }
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-- | Types that can be converted to and from 'TensorData'.
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--
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-- 'S.Vector' is the most efficient to encode/decode for most element types.
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class TensorType a => TensorDataType s a where
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-- | Decode the bytes of a 'TensorData' into an 's'.
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decodeTensorData :: TensorData a -> s a
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-- | Encode an 's' into a 'TensorData'.
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--
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-- The values should be in row major order, e.g.,
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--
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-- element 0: index (0, ..., 0)
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-- element 1: index (0, ..., 1)
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-- ...
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encodeTensorData :: Shape -> s a -> TensorData a
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-- All types, besides ByteString and Bool, are encoded as simple arrays and we
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-- can use Vector.Storable to encode/decode by type casting pointers.
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-- TODO(fmayle): Assert that the data type matches the return type.
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simpleDecode :: Storable a => TensorData a -> S.Vector a
|
||||
simpleDecode = S.unsafeCast . FFI.tensorDataBytes . unTensorData
|
||||
|
||||
simpleEncode :: forall a . (TensorType a, Storable a)
|
||||
=> Shape -> S.Vector a -> TensorData a
|
||||
simpleEncode (Shape xs) v =
|
||||
if product xs /= fromIntegral (S.length v)
|
||||
then error $ printf
|
||||
"simpleEncode: bad vector length for shape %v: expected=%d got=%d"
|
||||
(show xs) (product xs) (S.length v)
|
||||
else TensorData (FFI.TensorData xs dt (S.unsafeCast v))
|
||||
where
|
||||
dt = tensorType (undefined :: a)
|
||||
|
||||
instance TensorDataType S.Vector Float where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Double where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Int8 where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Int16 where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Int32 where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Int64 where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Word8 where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
instance TensorDataType S.Vector Word16 where
|
||||
decodeTensorData = simpleDecode
|
||||
encodeTensorData = simpleEncode
|
||||
|
||||
-- TODO: Haskell and tensorflow use different byte sizes for bools, which makes
|
||||
-- encoding more expensive. It may make sense to define a custom boolean type.
|
||||
instance TensorDataType S.Vector Bool where
|
||||
decodeTensorData =
|
||||
S.convert . S.map (/= 0) . FFI.tensorDataBytes . unTensorData
|
||||
encodeTensorData (Shape xs) =
|
||||
TensorData . FFI.TensorData xs DT_BOOL . S.map fromBool . S.convert
|
||||
where
|
||||
fromBool x = if x then 1 else 0 :: Word8
|
||||
|
||||
instance {-# OVERLAPPABLE #-} (Storable a, TensorDataType S.Vector a)
|
||||
=> TensorDataType V.Vector a where
|
||||
decodeTensorData = (S.convert :: S.Vector a -> V.Vector a) . decodeTensorData
|
||||
encodeTensorData x = encodeTensorData x . (S.convert :: V.Vector a -> S.Vector a)
|
||||
|
||||
instance {-# OVERLAPPING #-} TensorDataType V.Vector (Complex Float) where
|
||||
decodeTensorData = error "TODO (Complex Float)"
|
||||
encodeTensorData = error "TODO (Complex Float)"
|
||||
|
||||
instance {-# OVERLAPPING #-} TensorDataType V.Vector (Complex Double) where
|
||||
decodeTensorData = error "TODO (Complex Double)"
|
||||
encodeTensorData = error "TODO (Complex Double)"
|
||||
|
||||
instance {-# OVERLAPPING #-} TensorDataType V.Vector ByteString where
|
||||
-- Encoded data layout (described in third_party/tensorflow/c/c_api.h):
|
||||
-- table offsets for each element :: [Word64]
|
||||
-- at each element offset:
|
||||
-- string length :: VarInt64
|
||||
-- string data :: [Word8]
|
||||
-- TODO(fmayle): Benchmark these functions.
|
||||
decodeTensorData tensorData =
|
||||
either (\err -> error $ "Malformed TF_STRING tensor; " ++ err) id $
|
||||
if expected /= count
|
||||
|
|
@ -241,32 +311,21 @@ instance TensorType ByteString where
|
|||
-- Convert to Vector Word8.
|
||||
byteVector = S.fromList $ L.unpack $ Builder.toLazyByteString bytes
|
||||
|
||||
-- TODO: Haskell and tensorflow use different byte sizes for bools, which makes
|
||||
-- encoding more expensive. It may make sense to define a custom boolean type.
|
||||
instance TensorType Bool where
|
||||
tensorType _ = DT_BOOL
|
||||
tensorRefType _ = DT_BOOL_REF
|
||||
tensorVal = boolVal
|
||||
decodeTensorData =
|
||||
S.convert . S.map (/= 0) . FFI.tensorDataBytes . unTensorData
|
||||
encodeTensorData (Shape xs) =
|
||||
TensorData . FFI.TensorData xs DT_BOOL . S.map fromBool . S.convert
|
||||
where
|
||||
fromBool x = if x then 1 else 0 :: Word8
|
||||
newtype Scalar a = Scalar {unScalar :: a}
|
||||
deriving (Show, Eq, Ord, Num, Fractional, Floating, Real, RealFloat,
|
||||
RealFrac, IsString)
|
||||
|
||||
instance TensorType (Complex Float) where
|
||||
tensorType _ = DT_COMPLEX64
|
||||
tensorRefType _ = DT_COMPLEX64
|
||||
tensorVal = error "TODO (Complex Float)"
|
||||
decodeTensorData = error "TODO (Complex Float)"
|
||||
encodeTensorData = error "TODO (Complex Float)"
|
||||
instance TensorDataType V.Vector a => TensorDataType Scalar a where
|
||||
decodeTensorData = Scalar . headFromSingleton . decodeTensorData
|
||||
encodeTensorData x (Scalar y) = encodeTensorData x (V.fromList [y])
|
||||
|
||||
headFromSingleton :: V.Vector a -> a
|
||||
headFromSingleton x
|
||||
| V.length x == 1 = V.head x
|
||||
| otherwise = error $
|
||||
"Unable to extract singleton from tensor of length "
|
||||
++ show (V.length x)
|
||||
|
||||
instance TensorType (Complex Double) where
|
||||
tensorType _ = DT_COMPLEX128
|
||||
tensorRefType _ = DT_COMPLEX128
|
||||
tensorVal = error "TODO (Complex Double)"
|
||||
decodeTensorData = error "TODO (Complex Double)"
|
||||
encodeTensorData = error "TODO (Complex Double)"
|
||||
|
||||
-- | Shape (dimensions) of a tensor.
|
||||
newtype Shape = Shape [Int64] deriving Show
|
||||
|
|
|
|||
Loading…
Reference in a new issue