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f170df9d13
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)
83 lines
2.7 KiB
Haskell
83 lines
2.7 KiB
Haskell
-- Copyright 2016 TensorFlow authors.
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--
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-- Licensed under the Apache License, Version 2.0 (the "License");
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-- you may not use this file except in compliance with the License.
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-- You may obtain a copy of the License at
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--
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-- http://www.apache.org/licenses/LICENSE-2.0
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--
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-- Unless required by applicable law or agreed to in writing, software
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-- distributed under the License is distributed on an "AS IS" BASIS,
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-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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 OverloadedStrings #-}
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{-# LANGUAGE ScopedTypeVariables #-}
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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.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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( asyncProdNodes
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, build
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, buildAnd
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, run
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, runSession
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, run_
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)
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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 ((@=?))
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import qualified Data.ByteString as BS
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-- | Test basic queue behaviors.
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testBasic :: Test
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testBasic = testCase "testBasic" $ runSession $ do
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(q :: Queue2 Int64 BS.ByteString) <- build $ makeQueue2 1 ""
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buildAnd run_ (enqueue q 42 (scalar "Hi"))
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x <- buildAnd run (dequeue q)
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liftIO $ (Scalar 42, Scalar "Hi") @=? x
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buildAnd run_ (enqueue q 56 (scalar "Bar"))
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y <- buildAnd run (dequeue q)
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liftIO $ (Scalar 56, Scalar "Bar") @=? y
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-- | Test queue pumping.
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testPump :: Test
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testPump = testCase "testPump" $ runSession $ do
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(deq, pump) <- build $ do
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q :: Queue2 Int64 BS.ByteString <- makeQueue2 2 "ThePumpQueue"
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(,) <$> dequeue q
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<*> enqueue q 31 (scalar "Baz")
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-- This is a realistic use. The pump inputs are pre-bound to some
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-- nodes that produce values when pumped (e.g. read from a
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-- file).
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run_ (pump, pump)
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(x, y) <- run (deq, deq)
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liftIO $ (Scalar 31, Scalar "Baz") @=? x
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liftIO $ (Scalar 31, Scalar "Baz") @=? y
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testAsync :: Test
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testAsync = testCase "testAsync" $ runSession $ do
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(deq, pump) <- build $ do
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q :: Queue2 Int64 BS.ByteString <- makeQueue2 2 ""
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(,) <$> dequeue q
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<*> enqueue q 10 (scalar "Async")
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-- Pumps the queue until canceled by runSession exiting.
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asyncProdNodes pump
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-- Picks up a couple values and verifies they are as expected.
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run deq >>= liftIO . ((Scalar 10, Scalar "Async") @=?)
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run deq >>= liftIO . ((Scalar 10, Scalar "Async") @=?)
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main :: IO ()
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main = googleTest [ testBasic
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, testPump
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, testAsync
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]
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