tensorflow-haskell/tensorflow/src/TensorFlow/Core.hs

96 lines
2.1 KiB
Haskell
Raw Normal View History

-- 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 ExplicitNamespaces #-}
-- | The core functionality of TensorFlow.
--
-- Unless you are defining ops, you do not need to import other modules from
-- this package.
--
-- Basic ops are provided in the tensorflow-ops and tensorflow-core-ops
-- packages.
module TensorFlow.Core
( -- * Session
Session
, Options
, sessionConfig
, sessionTarget
, sessionTracer
, runSession
, runSessionWithOptions
, SavedModelTag(..)
, runSavedModel
, runSavedModelWithOptions
-- ** Building graphs
, MonadBuild(..)
-- ** Running graphs
, Fetchable
, Nodes
, run
, run_
, Feed
, feed
, runWithFeeds
, runWithFeeds_
-- ** Async
, asyncProdNodes
-- * Build
, Build
, BuildT
, render
, asGraphDef
, addGraphDef
, opName
, opAttr
, addInitializer
-- * Tensor
, ControlNode
, Tensor
, Value
, Ref
, value
, tensorFromName
, expr
-- ** Element types
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)
2016-12-15 03:53:06 +01:00
, TensorType
, TensorData
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)
2016-12-15 03:53:06 +01:00
, TensorDataType(decodeTensorData, encodeTensorData)
, ResourceHandle
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)
2016-12-15 03:53:06 +01:00
, Scalar(..)
, Shape(..)
, OneOf
, type (/=)
-- * Op combinators
, colocateWith
, Device(..)
, withDevice
, withNameScope
-- ** Dependencies
, withControlDependencies
, group
-- ** Misc
, noOp
) where
import TensorFlow.Build
import TensorFlow.ControlFlow
import TensorFlow.Nodes
import TensorFlow.Output
import TensorFlow.Session
import TensorFlow.Tensor
import TensorFlow.Types