tensor-safe 0.1.0.0 → 0.1.0.1

raw patch · 11 files changed

+347/−50 lines, 11 filesPVP: major bump suggested

API removals or changes: PVP suggests a major version bump

API changes (from Hackage documentation)

Files

• README.md +14/−0
• src/TensorSafe/Compile/Expr.hs +34/−18
• src/TensorSafe/Examples/ResNet50Example.hs +86/−0
• src/TensorSafe/Layers.hs +11/−1
• src/TensorSafe/Layers/Add.hs +21/−0
• src/TensorSafe/Layers/BatchNormalization.hs +36/−0
• src/TensorSafe/Layers/GlobalAvgPooling2D.hs +15/−0
• src/TensorSafe/Layers/Input.hs +19/−0
• src/TensorSafe/Layers/ZeroPadding2D.hs +32/−0
• src/TensorSafe/Network.hs +55/−13
• tensor-safe.cabal +24/−18

@@ -71,6 +71,20 @@     ('D1 10)         -- Output ``` +## How to extend layers definitions++Since this library only implements a subset of features that Keras implement, it's likely that for+new projects you'll need to add new layers. Due to the modularization of the library, this can be+done by adding the layer definitions in specific locations of the project:++1. First, add a new auxiliary layer entry for the data type `DLayer` in `TensorSafe.Compile.Expr`.+   This will make possible the compilation of the layer for all instances of `Generator`. Also, add+   to the `LayerGenerator` entry for the newly added layer.+2. Secondly, add the layer definition to the `TensorSafe/Layers` folder. You can copy the+   definitions from the currently defined layers.+3. Then, import and expose your layer definition in the `TensorSafe.Layers` module.+4. Finally, declare how your layer transforms a specific Shape in the `Out` type function.+ ## Command line interface  > This interface will change in the near future

@@ -21,18 +21,24 @@ -- | Auxiliary data representation of Layers -- IMPORTANT: If you add new Layers definitions to `TensorSafe.Layers`, you should add -- the corresponding data structure here for the same layer.-data DLayer = DConv2D+data DLayer = DActivation+            | DAdd+            | DBatchNormalization+            | DConv2D             | DDense             | DDropout             | DFlatten+            | DGlobalAvgPooling2D+            | DInput             | DLSTM             | DMaxPooling             | DRelu-            | DActivation+            | DZeroPadding2D             deriving Show  -- | Defines the data CNetwork = CNSequence CNetwork+              | CNAdd CNetwork CNetwork               | CNCons CNetwork CNetwork               | CNLayer DLayer (Map String String)               | CNReturn  -- End of initial sequence network@@ -51,24 +57,34 @@     generateName :: l -> DLayer -> String  instance LayerGenerator JavaScript where-    generateName _ DConv2D     = "conv2d"-    generateName _ DDense      = "dense"-    generateName _ DDropout    = "dropout"-    generateName _ DFlatten    = "flatten"-    generateName _ DLSTM       = "lstm"-    generateName _ DMaxPooling = "maxPooling2d"-    generateName _ DRelu       = "reLU"-    generateName _ DActivation = "activation"+    generateName _ DActivation         = "activation"+    generateName _ DAdd                = "addStrict"+    generateName _ DBatchNormalization = "batchNormalization"+    generateName _ DConv2D             = "conv2d"+    generateName _ DDense              = "dense"+    generateName _ DDropout            = "dropout"+    generateName _ DFlatten            = "flatten"+    generateName _ DGlobalAvgPooling2D = "globalAvgeragePooling2D"+    generateName _ DInput              = "input"+    generateName _ DLSTM               = "lstm"+    generateName _ DMaxPooling         = "maxPooling2d"+    generateName _ DRelu               = "reLU"+    generateName _ DZeroPadding2D      = "zeroPadding2D"  instance LayerGenerator Python where-    generateName _ DConv2D     = "Conv2D"-    generateName _ DDense      = "Dense"-    generateName _ DDropout    = "Dropout"-    generateName _ DFlatten    = "Flatten"-    generateName _ DLSTM       = "LSTM"-    generateName _ DMaxPooling = "MaxPool2D"-    generateName _ DRelu       = "ReLu"-    generateName _ DActivation = "Activation"+    generateName _ DActivation         = "Activation"+    generateName _ DAdd                = "add"+    generateName _ DBatchNormalization = "BatchNormalization"+    generateName _ DConv2D             = "Conv2D"+    generateName _ DDense              = "Dense"+    generateName _ DDropout            = "Dropout"+    generateName _ DFlatten            = "Flatten"+    generateName _ DGlobalAvgPooling2D = "GlobalAvgeragePooling2D"+    generateName _ DInput              = "Input"+    generateName _ DLSTM               = "LSTM"+    generateName _ DMaxPooling         = "MaxPool2D"+    generateName _ DRelu               = "ReLu"+    generateName _ DZeroPadding2D      = "ZeroPadding2D"  -- | Class that defines which languages are supported for CNetworks generation to text class Generator l where

@@ -0,0 +1,86 @@+{-# LANGUAGE DataKinds #-}+{-# LANGUAGE GADTs     #-}+{-| This module implements the ResNet50 model using Convs with BatchNormalization.+    This implementation is based on the Keras application implementation:+    https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py+-}+module TensorSafe.Examples.ResNet50Example where++import           TensorSafe.Layers+-- import           TensorSafe.Network (MkINetwork, mkINetwork)+import           TensorSafe.Network+import           TensorSafe.Shape+++type IdentityBlock channels kernel_size filters1 filters2 filters3 =+    '[ Conv2D channels filters1 1 1 1 1+     , BatchNormalization 3 99 1+     , Relu+     , Conv2D filters1 filters2 kernel_size kernel_size 1 1+     , BatchNormalization 3 99 1+     , ZeroPadding2D 1 1+     , Relu+     , Conv2D filters2 filters3 1 1 1 1+     , BatchNormalization 3 99 1+     ]++type Shortcut channels stride_size filters3 =+    '[ Conv2D channels filters3 1 1 stride_size stride_size+     , BatchNormalization 3 99 1+     ]+++type ConvBlock channels kernel_size stride_size filters1 filters2 filters3 =+    '[ Conv2D channels filters1 1 1 stride_size stride_size+     , BatchNormalization 3 99 1+     , Relu+     , Conv2D filters1 filters2 kernel_size kernel_size 1 1+     , ZeroPadding2D 1 1+     , BatchNormalization 3 99 1+     , Relu+     , Conv2D filters2 filters3 1 1 1 1+     , BatchNormalization 3 99 1+     ]++type ResNet50 img_size channels =+    MkINetwork+    '[ Input+     , ZeroPadding2D 3 3+     , Conv2D channels 64 7 7 2 2+     , BatchNormalization 3 99 1+     , Relu+     , ZeroPadding2D 1 1+     , MaxPooling 3 3 2 2++    -- First block+     , Add (ConvBlock 64 3 1 64 64 256) (Shortcut 64 1 256) , Relu+     , Add (IdentityBlock 256 3 64 64 256) '[Input] , Relu+     , Add (IdentityBlock 256 3 64 64 256) '[Input] , Relu++     -- Second block+     , Add (ConvBlock 256 3 1 128 128 512) (Shortcut 256 1 512) , Relu+     , Add (IdentityBlock 512 3 128 128 512) '[Input] , Relu+     , Add (IdentityBlock 512 3 128 128 512) '[Input] , Relu+     , Add (IdentityBlock 512 3 128 128 512) '[Input] , Relu++    --  Third block+     , Add (ConvBlock 512 3 1 256 256 1024) (Shortcut 512 1 1024) , Relu+     , Add (IdentityBlock 1024 3 256 256 1024) '[Input] , Relu+     , Add (IdentityBlock 1024 3 256 256 1024) '[Input] , Relu+     , Add (IdentityBlock 1024 3 256 256 1024) '[Input] , Relu+     , Add (IdentityBlock 1024 3 256 256 1024) '[Input] , Relu+     , Add (IdentityBlock 1024 3 256 256 1024) '[Input] , Relu++    --  -- Fourth block+     , Add (ConvBlock 1024 3 1 512 512 2048) (Shortcut 1024 1 2048) , Relu+     , Add (IdentityBlock 2048 3 512 512 2048) '[Input] , Relu+     , Add (IdentityBlock 2048 3 512 512 2048) '[Input] , Relu++     , GlobalAvgPooling2D+     , Dense 2048 1000+    ]+    ('D3 img_size img_size channels)    -- Input+    ('D1 1000)                            -- Output++resnet50 :: ResNet50 224 1+resnet50 = mkINetwork

@@ -1,20 +1,30 @@ {-| This module exposes all Layers declared at TensorSafe.Layers. -} module TensorSafe.Layers (+    Add,+    BatchNormalization,     Conv2D,     Dense,     Dropout,     Flatten,+    GlobalAvgPooling2D,+    Input,     LSTM,     MaxPooling,     Relu,-    Sigmoid+    Sigmoid,+    ZeroPadding2D ) where +import           TensorSafe.Layers.Add+import           TensorSafe.Layers.BatchNormalization import           TensorSafe.Layers.Conv2D import           TensorSafe.Layers.Dense import           TensorSafe.Layers.Dropout import           TensorSafe.Layers.Flatten+import           TensorSafe.Layers.GlobalAvgPooling2D+import           TensorSafe.Layers.Input import           TensorSafe.Layers.LSTM import           TensorSafe.Layers.MaxPooling import           TensorSafe.Layers.Relu import           TensorSafe.Layers.Sigmoid+import           TensorSafe.Layers.ZeroPadding2D

@@ -0,0 +1,21 @@+{-# LANGUAGE DataKinds #-}+{-# LANGUAGE GADTs     #-}+{-# LANGUAGE PolyKinds #-}+{-| This module declares the Add layer data type. -}+module TensorSafe.Layers.Add (Add) where++import           Data.Kind               (Type)+import           Data.Map++import           TensorSafe.Compile.Expr+import           TensorSafe.Layer++-- | Adds the dimensions of the shapes to a list of values with shape D1+data Add :: ls1 -> ls2 -> Type where+    Add :: Add ls1 ls2+    deriving Show++-- instance (Layer l1, Layer l2) => Layer (Add l1 l2) where+instance Layer (Add ls1 ls2) where+    layer = Add+    compile _ _ = CNLayer DAdd empty

@@ -0,0 +1,36 @@+{-# LANGUAGE DataKinds           #-}+{-# LANGUAGE GADTs               #-}+{-# LANGUAGE ScopedTypeVariables #-}+{-# LANGUAGE TypeFamilies        #-}+{-| This module declares the BatchNormalization layer data type. -}+module TensorSafe.Layers.BatchNormalization where++import           Data.Kind               (Type)+import           Data.Map+import           Data.Proxy+import           GHC.TypeLits++import           TensorSafe.Compile.Expr+import           TensorSafe.Layer+++-- | A classic BatchNormalization layer with axis, momentum and epsilon parameters+data BatchNormalization :: Nat -> Nat -> Nat -> Type where+    BatchNormalization :: BatchNormalization axis momentum epsilon+    deriving Show++instance ( KnownNat axis+         , KnownNat momentum+         , KnownNat epsilon+         ) => Layer (BatchNormalization axis momentum epsilon) where+    layer = BatchNormalization+    compile _ _ =+        let axis = show $ natVal (Proxy :: Proxy axis)+            momentum = show $ natVal (Proxy :: Proxy momentum)+            epsilon = show $ natVal (Proxy :: Proxy epsilon)+        in+            CNLayer DBatchNormalization (fromList [+              ("axis", axis),+              ("epsilon", epsilon),+              ("momentum", momentum)+            ])

@@ -0,0 +1,15 @@+{-| This module declares the GlobalAvgPooling2D layer data type. -}+module TensorSafe.Layers.GlobalAvgPooling2D (GlobalAvgPooling2D) where++import           Data.Map++import           TensorSafe.Compile.Expr+import           TensorSafe.Layer++-- | A GlobalAvgPooling2D function+data GlobalAvgPooling2D = GlobalAvgPooling2D deriving Show++instance Layer GlobalAvgPooling2D where+    layer = GlobalAvgPooling2D+    compile _ _ = CNLayer DGlobalAvgPooling2D empty+

@@ -0,0 +1,19 @@+{-| This module declares the Input layer data type. -}+module TensorSafe.Layers.Input (Input) where++import           Data.Map++import           TensorSafe.Compile.Expr+import           TensorSafe.Layer++-- | Inputs the dimensions of the shapes to a list of values with shape D1+data Input = Input deriving Show++instance Layer Input where+    layer = Input+    compile _ inputShape =+        let params = case inputShape of+                        Just shape -> fromList [("inputShape", shape)]+                        Nothing    -> empty+        in+            CNLayer DInput params

@@ -0,0 +1,32 @@+{-# LANGUAGE DataKinds           #-}+{-# LANGUAGE GADTs               #-}+{-# LANGUAGE ScopedTypeVariables #-}+{-# LANGUAGE TypeFamilies        #-}+{-| This module declares the ZeroPadding2D layer data type. -}+module TensorSafe.Layers.ZeroPadding2D (ZeroPadding2D) where++import           Data.Kind               (Type)+import           Data.Map+import           Data.Proxy+import           GHC.TypeLits++import           TensorSafe.Compile.Expr+import           TensorSafe.Layer++-- | A ZeroPadding2D layer with padding_rows and padding_cols arguments+data ZeroPadding2D :: Nat -> Nat -> Type where+    ZeroPadding2D :: ZeroPadding2D padding_rows padding_cols+    deriving Show++instance ( KnownNat padding_rows+         , KnownNat padding_cols+         ) => Layer (ZeroPadding2D padding_rows padding_cols) where+    layer = ZeroPadding2D+    compile _ _ =+        let padding_rows = show $ natVal (Proxy :: Proxy padding_rows)+            padding_cols = show $ natVal (Proxy :: Proxy padding_cols)+        in+            CNLayer DZeroPadding2D (fromList [+                ("padding_rows", padding_rows),+                ("padding_cols", padding_cols)+            ])

@@ -13,14 +13,15 @@ -- all needed information for compiling the Network structures to CNetworks for later code -- generation. -}-module TensorSafe.Network (-    Network (..),-    INetwork (..),-    MkINetwork,-    ValidNetwork,-    mkINetwork,-    toCNetwork-) where+-- module TensorSafe.Network (+--     Network (..),+--     INetwork (..),+--     MkINetwork,+--     ValidNetwork,+--     mkINetwork,+--     toCNetwork+-- ) where+module TensorSafe.Network where  import           Data.Kind               (Type) import           Data.Singletons@@ -128,6 +129,14 @@ -- MAPPING TRANSFORMATIONS OF LAYERS AND SHAPES -- +type family MaybeShape (s :: Shape) (b :: Bool) :: Shape where+    MaybeType s 'False = 'D1 0 -- HACK: ShapeEquals' should raise an exception on this case+    MaybeType s 'True  = s+++type family Add' (layers :: [Type]) (layers2 :: [Type]) (shape :: Shape) where+    Add' ls1 _ sIn = ComputeOut ls1 sIn+ -- | Defines the expected output of a layer --   This type function should be instanciated for each of the Layers defined. type family Out (l :: Type) (s :: Shape) :: Shape where@@ -139,6 +148,20 @@     --     --     --+    Out (Add ls1 ls2) sIn = Add' ls1 ls2 sIn+        -- MaybeShape+        --     (ComputeOut ls1 sIn)+        --     (ShapeEquals' (ComputeOut ls1 sIn) (ComputeOut ls2 sIn))  -- Validation that computes the same+    -- Out (Add (INetwork ls (s : ss))) s = ComputeOut ls s++    --+    --+    --+    Out (BatchNormalization _ _ _) s = s++    --+    --+    --     Out (Conv2D 1 1 k k' s s') ('D2 inputRows inputColumns) =         ('D2 (1 + (Div (inputRows - k) s))                 (1 + (Div (inputColumns - k') s'))@@ -181,6 +204,16 @@     --     --     --+    Out GlobalAvgPooling2D ('D3 _ _ z) = 'D1 z++    --+    --+    --+    Out Input s = s++    --+    --+    --     Out (LSTM units 'False) _           = 'D1 units     Out (LSTM units 'True)  ('D2 x _)   = 'D2 x units     Out (LSTM units 'True)  ('D3 x _ _) = 'D2 x units@@ -202,21 +235,30 @@     --     --     ---    Out Relu s           = s+    Out Relu s = s      --     --     ---    Out Sigmoid s           = s+    Out Sigmoid s = s      --+    --+    --+    Out (ZeroPadding2D padding_rows padding_cols) ('D2 inputRows inputColumns) =+        ('D2 (inputRows + (2 N.* padding_rows)) (inputColumns + (2 N.* padding_cols)))++    Out (ZeroPadding2D padding_rows padding_cols) ('D3 inputRows inputColumns channels) =+        ('D3 (inputRows + (2 N.* padding_rows)) (inputColumns + (2 N.* padding_cols)) channels)++    --     -- Edge case or not defined raise an error     ---    Out l sOut =+    Out l sIn =         TypeError ( 'Text "Couldn't apply the Layer \""                 ':<>: 'ShowType l-                ':<>: 'Text "\" with the output Shape \""-                ':<>: 'ShowType sOut+                ':<>: 'Text "\" with the input Shape \""+                ':<>: 'ShowType sIn                 ':<>: 'Text "\"")  --

@@ -4,10 +4,10 @@ -- -- see: https://github.com/sol/hpack ----- hash: 7287463c38f034c451472b16083a3110425f6ff06a3f8c12c27b8cf229f332e6+-- hash: 182681ec85bce978c3cc8c3181ac70c130ce8d4102cf4076e965dd7287af2c0a  name:           tensor-safe-version:        0.1.0.0+version:        0.1.0.1 synopsis:       Create valid deep neural network architectures description:    TensorSafe provides a very simple API to create deep neural networks structures which are validated using Dependent Types. Given a list of Layers and an initial Shape, TensorSafe is able to check and corroborate the structure of the network. Also, it's possible to extract the definition and compile it to a target language like Python and JavaScript. category:       AI, Dependent Types, Language, Library, Program@@ -27,22 +27,6 @@   location: https://github.com/leopiney/tensor-safe  library-  hs-source-dirs:-      src-  ghc-options: -Wall -freduction-depth=0-  build-depends:-      base >=4.7 && <5-    , casing >=0.1.4.0 && <0.1.5-    , cmdargs >=0.10.20 && <0.11-    , containers >=0.6.0.1 && <0.7-    , extra >=1.6 && <1.7-    , formatting >=6.3.6 && <6.4-    , ghc-typelits-extra >=0.3 && <0.4-    , hint >=0.9.0 && <1.0-    , singletons >=2.5.1 && <2.6-    , text >=1.2.3.1 && <1.3-    , vector >=0.12 && <0.13-    , vector-sized >1.2 && <1.3   exposed-modules:       TensorSafe       TensorSafe.Commands.Check@@ -53,21 +37,43 @@       TensorSafe.Core       TensorSafe.Examples.Examples       TensorSafe.Examples.MnistExample+      TensorSafe.Examples.ResNet50Example       TensorSafe.Examples.SimpleExample       TensorSafe.Layer       TensorSafe.Layers+      TensorSafe.Layers.Add+      TensorSafe.Layers.BatchNormalization       TensorSafe.Layers.Conv2D       TensorSafe.Layers.Dense       TensorSafe.Layers.Dropout       TensorSafe.Layers.Flatten+      TensorSafe.Layers.GlobalAvgPooling2D+      TensorSafe.Layers.Input       TensorSafe.Layers.LSTM       TensorSafe.Layers.MaxPooling       TensorSafe.Layers.Relu       TensorSafe.Layers.Sigmoid+      TensorSafe.Layers.ZeroPadding2D       TensorSafe.Network       TensorSafe.Shape   other-modules:       Paths_tensor_safe+  hs-source-dirs:+      src+  ghc-options: -Wall -freduction-depth=0+  build-depends:+      base >=4.7 && <5+    , casing >=0.1.4.0 && <0.1.5+    , cmdargs >=0.10.20 && <0.11+    , containers >=0.6.0.1 && <0.7+    , extra >=1.6 && <1.7+    , formatting >=6.3.6 && <6.4+    , ghc-typelits-extra >=0.3 && <0.4+    , hint >=0.9.0 && <1.0+    , singletons >=2.5.1 && <2.6+    , text >=1.2.3.1 && <1.3+    , vector >=0.12 && <0.13+    , vector-sized >1.2 && <1.3   default-language: Haskell2010  executable tensor-safe