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626 lines
27 KiB
Text
626 lines
27 KiB
Text
-- Hoogle documentation, generated by Haddock
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-- See Hoogle, http://www.haskell.org/hoogle/
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-- | TensorFlow bindings.
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--
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-- Please see README.md
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@package tensorflow
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@version 0.1.0.0
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-- | Originally taken from internal proto-lens code.
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module TensorFlow.Internal.VarInt
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-- | Decode an unsigned varint.
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getVarInt :: Parser Word64
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-- | Encode a Word64.
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putVarInt :: Word64 -> Builder
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module TensorFlow.Internal.FFI
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data TensorFlowException
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TensorFlowException :: Code -> Text -> TensorFlowException
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data Session
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-- | Runs the given action after creating a session with options populated
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-- by the given optionSetter.
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withSession :: (SessionOptions -> IO ()) -> ((IO () -> IO ()) -> Session -> IO a) -> IO a
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extendGraph :: Session -> GraphDef -> IO ()
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run :: Session -> [(ByteString, TensorData)] -> [ByteString] -> [ByteString] -> IO [TensorData]
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-- | All of the data needed to represent a tensor.
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data TensorData
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TensorData :: [Int64] -> !DataType -> !(Vector Word8) -> TensorData
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[tensorDataDimensions] :: TensorData -> [Int64]
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[tensorDataType] :: TensorData -> !DataType
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[tensorDataBytes] :: TensorData -> !(Vector Word8)
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setSessionConfig :: ConfigProto -> SessionOptions -> IO ()
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setSessionTarget :: ByteString -> SessionOptions -> IO ()
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-- | Returns the serialized OpList of all OpDefs defined in this address
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-- space.
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getAllOpList :: IO ByteString
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-- | Serializes the given msg and provides it as (ptr,len) argument to the
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-- given action.
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useProtoAsVoidPtrLen :: (Message msg, Num c) => msg -> (Ptr b -> c -> IO a) -> IO a
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instance GHC.Classes.Eq TensorFlow.Internal.FFI.TensorData
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instance GHC.Show.Show TensorFlow.Internal.FFI.TensorData
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instance GHC.Classes.Eq TensorFlow.Internal.FFI.TensorFlowException
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instance GHC.Show.Show TensorFlow.Internal.FFI.TensorFlowException
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instance GHC.Exception.Exception TensorFlow.Internal.FFI.TensorFlowException
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module TensorFlow.Types
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-- | The class of scalar types supported by tensorflow.
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class TensorType a
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tensorType :: TensorType a => a -> DataType
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tensorRefType :: TensorType a => a -> DataType
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tensorVal :: TensorType a => Lens' TensorProto [a]
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-- | Decode the bytes of a TensorData into a Vector.
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decodeTensorData :: TensorType a => TensorData a -> 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) element 1: index (0, ..., 1) ...
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encodeTensorData :: TensorType a => Shape -> Vector a -> TensorData a
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-- | Data about a tensor that is encoded for the TensorFlow APIs.
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newtype TensorData a
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TensorData :: TensorData -> TensorData a
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[unTensorData] :: TensorData a -> TensorData
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-- | Shape (dimensions) of a tensor.
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newtype Shape
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Shape :: [Int64] -> Shape
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protoShape :: Lens' TensorShapeProto Shape
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class Attribute a
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attrLens :: Attribute a => Lens' AttrValue a
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-- | A <a>Constraint</a> specifying the possible choices of a
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-- <a>TensorType</a>.
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--
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-- We implement a <a>Constraint</a> like <tt>OneOf '[Double, Float]
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-- a</tt> by turning the natural representation as a conjunction, i.e.,
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--
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-- <pre>
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-- a == Double || a == Float
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-- </pre>
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--
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-- into a disjunction like
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--
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-- <pre>
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-- a /= Int32 && a /= Int64 && a /= ByteString && ...
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-- </pre>
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--
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-- using an enumeration of all the possible <a>TensorType</a>s.
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type OneOf ts a = (TensorType a, TensorTypes ts, NoneOf (AllTensorTypes \\ ts) a)
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-- | A constraint checking that two types are different.
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-- | Helper types to produce a reasonable type error message when the
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-- Constraint "a /= a" fails. TODO(judahjacobson): Use ghc-8's
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-- CustomTypeErrors for this.
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data TypeError a
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data ExcludedCase
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-- | A <a>Constraint</a> checking that the input is a list of
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-- <a>TensorType</a>s. Helps improve error messages when using
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-- <a>OneOf</a>.
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-- | A constraint that the type <tt>a</tt> doesn't appear in the type list
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-- <tt>ts</tt>. Assumes that <tt>a</tt> and each of the elements of
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-- <tt>ts</tt> are <a>TensorType</a>s.
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-- | Takes the difference of two lists of types.
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-- | Removes a type from the given list of types.
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-- | An enumeration of all valid <a>TensorType</a>s.
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type AllTensorTypes = '[Float, Double, Int8, Int16, Int32, Int64, Word8, Word16, ByteString, Bool]
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instance GHC.Show.Show TensorFlow.Types.Shape
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instance TensorFlow.Types.TensorType GHC.Types.Float
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instance TensorFlow.Types.TensorType GHC.Types.Double
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instance TensorFlow.Types.TensorType GHC.Int.Int32
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instance TensorFlow.Types.TensorType GHC.Int.Int64
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instance TensorFlow.Types.TensorType GHC.Word.Word8
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instance TensorFlow.Types.TensorType GHC.Word.Word16
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instance TensorFlow.Types.TensorType GHC.Int.Int16
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instance TensorFlow.Types.TensorType GHC.Int.Int8
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instance TensorFlow.Types.TensorType Data.ByteString.Internal.ByteString
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instance TensorFlow.Types.TensorType GHC.Types.Bool
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instance TensorFlow.Types.TensorType (Data.Complex.Complex GHC.Types.Float)
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instance TensorFlow.Types.TensorType (Data.Complex.Complex GHC.Types.Double)
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instance GHC.Exts.IsList TensorFlow.Types.Shape
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instance TensorFlow.Types.Attribute GHC.Types.Float
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instance TensorFlow.Types.Attribute Data.ByteString.Internal.ByteString
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instance TensorFlow.Types.Attribute GHC.Int.Int64
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instance TensorFlow.Types.Attribute Proto.Tensorflow.Core.Framework.Types.DataType
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instance TensorFlow.Types.Attribute Proto.Tensorflow.Core.Framework.Tensor.TensorProto
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instance TensorFlow.Types.Attribute GHC.Types.Bool
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instance TensorFlow.Types.Attribute TensorFlow.Types.Shape
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instance TensorFlow.Types.Attribute Proto.Tensorflow.Core.Framework.AttrValue.AttrValue'ListValue
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instance TensorFlow.Types.Attribute [Proto.Tensorflow.Core.Framework.Types.DataType]
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instance TensorFlow.Types.Attribute [GHC.Int.Int64]
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module TensorFlow.Output
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-- | A type of graph node which has no outputs. These nodes are valuable
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-- for causing side effects when they are run.
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newtype ControlNode
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ControlNode :: Op -> ControlNode
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[unControlNode] :: ControlNode -> Op
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-- | A device that a node can be assigned to. There's a naming convention
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-- where the device names are constructed from job and replica names.
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newtype Device
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Device :: Text -> Device
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[deviceName] :: Device -> Text
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-- | The name of a node in the graph. This corresponds to the proto field
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-- NodeDef.name. Includes the scope prefix (if any) and a unique
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-- identifier (if the node was implicitly named).
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newtype NodeName
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NodeName :: Text -> NodeName
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[unNodeName] :: NodeName -> Text
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-- | The representation of a node in a TensorFlow graph.
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data Op
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-- | Properties are fixed, including the device, name, and scope.
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Rendered :: !NodeDef -> Op
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-- | Properties are not fixed, and may change depending on which context
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-- this op is rendered in.
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Unrendered :: !OpDef -> Op
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-- | Traverse on the <a>Unrendered</a> of an <a>Op</a>.
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--
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-- Same implementation as _Left.
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opUnrendered :: Traversal' Op OpDef
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-- | Op definition. This corresponds somewhat to the <a>NodeDef</a> proto.
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data OpDef
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OpDef :: !PendingNodeName -> !OpType -> !(Map Text AttrValue) -> [Output] -> [NodeName] -> OpDef
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[_opName] :: OpDef -> !PendingNodeName
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[_opType] :: OpDef -> !OpType
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[_opAttrs] :: OpDef -> !(Map Text AttrValue)
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[_opInputs] :: OpDef -> [Output]
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[_opControlInputs] :: OpDef -> [NodeName]
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opName :: Lens' OpDef PendingNodeName
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opType :: Lens' OpDef OpType
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opAttr :: Attribute a => Text -> Lens' OpDef a
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opInputs :: Lens' OpDef [Output]
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opControlInputs :: Lens' OpDef [NodeName]
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-- | The type of op of a node in the graph. This corresponds to the proto
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-- field NodeDef.op.
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newtype OpType
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OpType :: Text -> OpType
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[unOpType] :: OpType -> Text
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newtype OutputIx
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OutputIx :: Int -> OutputIx
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[unOutputIx] :: OutputIx -> Int
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-- | An output of a TensorFlow node.
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data Output
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Output :: !OutputIx -> !Op -> Output
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output :: OutputIx -> Op -> Output
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outputIndex :: Lens' Output OutputIx
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outputOp :: Lens' Output Op
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-- | The name specified for an unrendered Op. If an Op has an ImplicitName,
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-- it will be assigned based on the opType plus a unique identifier. Does
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-- not contain the "scope" prefix.
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data PendingNodeName
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ExplicitName :: !Text -> PendingNodeName
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ImplicitName :: PendingNodeName
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instance GHC.Classes.Ord TensorFlow.Output.Op
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instance GHC.Classes.Eq TensorFlow.Output.Op
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instance GHC.Show.Show TensorFlow.Output.Output
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instance GHC.Classes.Ord TensorFlow.Output.Output
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instance GHC.Classes.Eq TensorFlow.Output.Output
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instance GHC.Classes.Ord TensorFlow.Output.OpDef
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instance GHC.Classes.Eq TensorFlow.Output.OpDef
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instance GHC.Show.Show TensorFlow.Output.NodeName
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instance GHC.Classes.Ord TensorFlow.Output.NodeName
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instance GHC.Classes.Eq TensorFlow.Output.NodeName
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instance GHC.Show.Show TensorFlow.Output.PendingNodeName
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instance GHC.Classes.Ord TensorFlow.Output.PendingNodeName
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instance GHC.Classes.Eq TensorFlow.Output.PendingNodeName
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instance Data.String.IsString TensorFlow.Output.Device
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instance GHC.Classes.Ord TensorFlow.Output.Device
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instance GHC.Classes.Eq TensorFlow.Output.Device
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instance GHC.Show.Show TensorFlow.Output.OutputIx
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instance GHC.Enum.Enum TensorFlow.Output.OutputIx
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instance GHC.Num.Num TensorFlow.Output.OutputIx
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instance GHC.Classes.Ord TensorFlow.Output.OutputIx
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instance GHC.Classes.Eq TensorFlow.Output.OutputIx
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instance GHC.Show.Show TensorFlow.Output.OpType
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instance GHC.Classes.Ord TensorFlow.Output.OpType
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instance GHC.Classes.Eq TensorFlow.Output.OpType
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instance Data.String.IsString TensorFlow.Output.OpType
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instance GHC.Show.Show TensorFlow.Output.Device
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instance GHC.Show.Show TensorFlow.Output.Op
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instance Data.String.IsString TensorFlow.Output.Output
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module TensorFlow.Tensor
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-- | A named output of a TensorFlow operation.
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--
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-- The type parameter <tt>a</tt> is the type of the elements in the
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-- <a>Tensor</a>. The parameter <tt>v</tt> is either <a>Value</a> or
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-- <a>Ref</a>, depending on whether the graph is treating this op output
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-- as an immutable <a>Value</a> or a stateful <a>Ref</a> (e.g., a
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-- variable). Note that a <tt>Tensor Ref</tt> can be casted into a
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-- <tt>Tensor Value</tt> via <a>value</a>.
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data Tensor v a
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Tensor :: (TensorKind v) -> Output -> Tensor v a
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data Value
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data Ref
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-- | This class provides a runtime switch on whether a <a>Tensor</a> should
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-- be treated as a <a>Value</a> or as a <a>Ref</a>.
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data TensorKind v
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ValueKind :: TensorKind Value
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RefKind :: TensorKind Ref
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tensorKind :: Lens' (Tensor v a) (TensorKind v)
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tensorOutput :: Lens' (Tensor v a) Output
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-- | Lens for the attributes of a tensor.
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--
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-- Only valid if the tensor has not yet been rendered. If the tensor has
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-- been rendered, the traversal will be over nothing (nothing can be read
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-- or written).
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tensorAttr :: Attribute attr => Text -> Traversal' (Tensor v a) attr
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-- | Cast a 'Tensor *' into a 'Tensor Value'. Common usage is to cast a Ref
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-- into Value. This behaves like a no-op.
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value :: Tensor v a -> Tensor Value a
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-- | A pair of a <a>Tensor</a> and some data that should be fed into that
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-- <a>Tensor</a> when running the graph.
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data Feed
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Feed :: Output -> TensorData -> Feed
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-- | Create a <a>Feed</a> for feeding the given data into a <a>Tensor</a>
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-- when running the graph.
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--
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-- Note that if a <a>Tensor</a> is rendered, its identity may change; so
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-- feeding the rendered <a>Tensor</a> may be different than feeding the
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-- original <a>Tensor</a>.
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feed :: Tensor v a -> TensorData a -> Feed
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-- | Create a <a>Tensor</a> for a given name. This can be used to reference
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-- nodes in a <tt>GraphDef</tt> that was loaded via <tt>addGraphDef</tt>.
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-- TODO(judahjacobson): add more safety checks here.
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tensorFromName :: TensorKind v -> Text -> Tensor v a
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module TensorFlow.Build
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-- | A type of graph node which has no outputs. These nodes are valuable
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-- for causing side effects when they are run.
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newtype ControlNode
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ControlNode :: Op -> ControlNode
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[unControlNode] :: ControlNode -> Op
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data Unique
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explicitName :: Text -> PendingNodeName
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implicitName :: PendingNodeName
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opDef :: OpType -> OpDef
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opDefWithName :: PendingNodeName -> OpType -> OpDef
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opName :: Lens' OpDef PendingNodeName
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opType :: Lens' OpDef OpType
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opAttr :: Attribute a => Text -> Lens' OpDef a
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opInputs :: Lens' OpDef [Output]
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opControlInputs :: Lens' OpDef [NodeName]
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data GraphState
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-- | Render a <a>Tensor</a>, fixing its name, scope, device and control
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-- inputs from the <a>Build</a> context. Also renders any dependencies of
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-- the <a>Tensor</a> that weren't already rendered.
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--
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-- This operation is idempotent; <tt>render >=> render ===
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-- render</tt>. However, rendering a (previously un-rendered)
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-- <a>Tensor</a> in two different contexts may result in two different
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-- <a>Tensor</a>s.
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render :: Tensor v a -> Build (Tensor v a)
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-- | Render a <a>Tensor</a> and get its node's name.
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renderNodeName :: Tensor v a -> Build NodeName
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renderedNodeDefs :: Lens' GraphState (Map NodeName NodeDef)
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-- | An action for building nodes in a TensorFlow graph. Used to manage
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-- build state internally as part of the <tt>Session</tt> monad.
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data BuildT m a
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-- | An action for building nodes in a TensorFlow graph.
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type Build = BuildT Identity
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-- | Registers the given node to be executed before the next <a>run</a>.
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addInitializer :: ControlNode -> Build ()
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-- | This is Control.Monad.Morph.hoist sans the dependency.
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hoistBuildT :: (forall a. m a -> n a) -> BuildT m b -> BuildT n b
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evalBuildT :: Monad m => BuildT m a -> m a
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runBuildT :: BuildT m a -> m (a, GraphState)
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-- | Produce a GraphDef proto representation of the nodes that are rendered
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-- in the given <a>Build</a> action.
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asGraphDef :: Build a -> GraphDef
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addGraphDef :: GraphDef -> Build ()
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-- | Get all the initializers that have accumulated so far, and clear that
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-- buffer.
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flushInitializers :: Monad m => BuildT m [NodeName]
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-- | Get all the NodeDefs that have accumulated so far, and clear that
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-- buffer.
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flushNodeBuffer :: Monad m => BuildT m [NodeDef]
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-- | Render the given op if it hasn't been rendered already, and return its
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-- name.
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getOrAddOp :: Op -> Build NodeName
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-- | Add a new node for a given <a>OpDef</a>. This is used for making
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-- "stateful" ops which are not safe to dedup (e.g, "variable" and
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-- "assign").
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addNewOp :: OpDef -> Build NodeDef
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-- | Render an <a>Output</a> and return a string representation for the
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-- TensorFlow foreign APIs.
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renderOutput :: Output -> Build Text
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-- | Places all nodes rendered in the given <a>Build</a> action on the same
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-- device as the given Tensor (see also <a>withDevice</a>). Make sure
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-- that the action has side effects of rendering the desired tensors. A
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-- pure return would not have the desired effect.
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colocateWith :: Tensor v b -> Build a -> Build a
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-- | Modify some part of the state, run an action, and restore the state
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-- after that action is done.
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withStateLens :: MonadState s m => Lens' s a -> (a -> a) -> m b -> m b
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-- | Set a device for all nodes rendered in the given <a>Build</a> action
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-- (unless further overridden by another use of withDevice).
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withDevice :: Maybe Device -> Build a -> Build a
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-- | Prepend a scope to all nodes rendered in the given <a>Build</a>
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-- action.
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withNameScope :: Text -> Build a -> Build a
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-- | Add control inputs to all nodes rendered in the given <a>Build</a>
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-- action.
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withNodeDependencies :: Set NodeName -> Build a -> Build a
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-- | Records the given summary action in Build for retrieval with
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-- <a>collectAllSummaries</a>. The summary op is required to produce a
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-- Summary protocol buffer in string form. For safety, use the
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-- pre-composed functions: Logging.scalarSummary and
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-- Logging.histogramSummary.
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addSummary :: SummaryTensor -> Build ()
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-- | Synonym for the tensors that return serialized Summary proto.
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type SummaryTensor = Tensor Value ByteString
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-- | Retrieves the summary ops collected thus far. Typically this only
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-- happens once, but if <a>buildWithSummary</a> is used repeatedly, the
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-- values accumulate.
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collectAllSummaries :: Monad m => BuildT m [SummaryTensor]
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instance GHC.Base.Monad m => Control.Monad.State.Class.MonadState TensorFlow.Build.GraphState (TensorFlow.Build.BuildT m)
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instance Control.Monad.Trans.Class.MonadTrans TensorFlow.Build.BuildT
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instance Control.Monad.IO.Class.MonadIO m => Control.Monad.IO.Class.MonadIO (TensorFlow.Build.BuildT m)
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instance GHC.Base.Monad m => GHC.Base.Monad (TensorFlow.Build.BuildT m)
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instance GHC.Base.Monad m => GHC.Base.Applicative (TensorFlow.Build.BuildT m)
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instance GHC.Base.Functor m => GHC.Base.Functor (TensorFlow.Build.BuildT m)
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instance GHC.Classes.Ord TensorFlow.Build.PendingNode
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instance GHC.Classes.Eq TensorFlow.Build.PendingNode
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instance Data.String.IsString TensorFlow.Build.Scope
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instance GHC.Classes.Ord TensorFlow.Build.Scope
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instance GHC.Classes.Eq TensorFlow.Build.Scope
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instance GHC.Enum.Enum TensorFlow.Build.Unique
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instance GHC.Classes.Ord TensorFlow.Build.Unique
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instance GHC.Classes.Eq TensorFlow.Build.Unique
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instance GHC.Show.Show TensorFlow.Build.Scope
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module TensorFlow.BuildOp
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-- | Class of types that can be used as op outputs.
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class OpResult a
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-- | Class of types that can be used as op functions.
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class BuildOp f
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-- | Starts an operation that returns a structured set of tensors
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-- (singletons or tuples).
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buildOp :: BuildOp f => OpDef -> f
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-- | Starts an operation that returns a list of tensors.
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buildListOp :: BuildOp f => [Int64] -> OpDef -> f
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-- | Returns true if all the integers in each tuple are identical. Throws
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-- an error with a descriptive message if not.
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eqLengthGuard :: [(String, [(String, Int)])] -> Bool
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instance GHC.Show.Show TensorFlow.BuildOp.ResultState
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instance (TensorFlow.BuildOp.OpResult a1, TensorFlow.BuildOp.OpResult a2) => TensorFlow.BuildOp.OpResult (a1, a2)
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instance (TensorFlow.BuildOp.OpResult a1, TensorFlow.BuildOp.OpResult a2, TensorFlow.BuildOp.OpResult a3) => TensorFlow.BuildOp.OpResult (a1, a2, a3)
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instance (TensorFlow.BuildOp.OpResult a1, TensorFlow.BuildOp.OpResult a2, TensorFlow.BuildOp.OpResult a3, TensorFlow.BuildOp.OpResult a4) => TensorFlow.BuildOp.OpResult (a1, a2, a3, a4)
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instance (TensorFlow.BuildOp.OpResult a1, TensorFlow.BuildOp.OpResult a2, TensorFlow.BuildOp.OpResult a3, TensorFlow.BuildOp.OpResult a4, TensorFlow.BuildOp.OpResult a5) => TensorFlow.BuildOp.OpResult (a1, a2, a3, a4, a5)
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instance (TensorFlow.BuildOp.OpResult a1, TensorFlow.BuildOp.OpResult a2, TensorFlow.BuildOp.OpResult a3, TensorFlow.BuildOp.OpResult a4, TensorFlow.BuildOp.OpResult a5, TensorFlow.BuildOp.OpResult a6) => TensorFlow.BuildOp.OpResult (a1, a2, a3, a4, a5, a6)
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instance TensorFlow.BuildOp.OpResult (TensorFlow.Tensor.Tensor TensorFlow.Tensor.Value a)
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instance TensorFlow.BuildOp.OpResult (TensorFlow.Tensor.Tensor TensorFlow.Tensor.Ref a)
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instance TensorFlow.BuildOp.OpResult TensorFlow.Output.ControlNode
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instance TensorFlow.BuildOp.OpResult a => TensorFlow.BuildOp.OpResult [a]
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instance TensorFlow.BuildOp.BuildOp TensorFlow.Output.ControlNode
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instance TensorFlow.BuildOp.BuildOp (TensorFlow.Tensor.Tensor TensorFlow.Tensor.Value a)
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instance TensorFlow.BuildOp.BuildOp (TensorFlow.Tensor.Tensor TensorFlow.Tensor.Ref a)
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instance TensorFlow.BuildOp.BuildOp [TensorFlow.Tensor.Tensor TensorFlow.Tensor.Value a]
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instance (TensorFlow.BuildOp.OpResult t1, TensorFlow.BuildOp.OpResult t2) => TensorFlow.BuildOp.BuildOp (t1, t2)
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instance (TensorFlow.BuildOp.OpResult t1, TensorFlow.BuildOp.OpResult t2, TensorFlow.BuildOp.OpResult t3) => TensorFlow.BuildOp.BuildOp (t1, t2, t3)
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instance (TensorFlow.BuildOp.OpResult t1, TensorFlow.BuildOp.OpResult t2, TensorFlow.BuildOp.OpResult t3, TensorFlow.BuildOp.OpResult t4) => TensorFlow.BuildOp.BuildOp (t1, t2, t3, t4)
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instance (TensorFlow.BuildOp.OpResult t1, TensorFlow.BuildOp.OpResult t2, TensorFlow.BuildOp.OpResult t3, TensorFlow.BuildOp.OpResult t4, TensorFlow.BuildOp.OpResult t5) => TensorFlow.BuildOp.BuildOp (t1, t2, t3, t4, t5)
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instance (TensorFlow.BuildOp.OpResult t1, TensorFlow.BuildOp.OpResult t2, TensorFlow.BuildOp.OpResult t3, TensorFlow.BuildOp.OpResult t4, TensorFlow.BuildOp.OpResult t5, TensorFlow.BuildOp.OpResult t6) => TensorFlow.BuildOp.BuildOp (t1, t2, t3, t4, t5, t6)
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instance TensorFlow.BuildOp.OpResult a => TensorFlow.BuildOp.BuildOp (TensorFlow.Build.Build a)
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instance TensorFlow.BuildOp.BuildOp f => TensorFlow.BuildOp.BuildOp (TensorFlow.Tensor.Tensor v a -> f)
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instance TensorFlow.BuildOp.BuildOp f => TensorFlow.BuildOp.BuildOp ([TensorFlow.Tensor.Tensor v a] -> f)
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module TensorFlow.Nodes
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-- | Types that contain ops which can be run.
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class Nodes t
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getNodes :: Nodes t => t -> Build (Set NodeName)
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-- | Types that tensor representations (e.g. <a>Tensor</a>,
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-- <a>ControlNode</a>) can be fetched into.
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--
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-- Includes collections of tensors (e.g. tuples).
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class Nodes t => Fetchable t a
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getFetch :: Fetchable t a => t -> Build (Fetch a)
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-- | Fetch action. Keeps track of what needs to be fetched and how to
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-- decode the fetched data.
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data Fetch a
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Fetch :: Set Text -> (Map Text TensorData -> a) -> Fetch a
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-- | Nodes to fetch
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[fetches] :: Fetch a -> Set Text
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-- | Function to create an <tt>a</tt> from the fetched data.
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[fetchRestore] :: Fetch a -> Map Text TensorData -> a
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nodesUnion :: (Monoid b, Traversable t, Applicative f) => t (f b) -> f b
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fetchTensorList :: TensorType a => Tensor v a -> Build (Fetch (Shape, [a]))
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fetchTensorVector :: TensorType a => Tensor v a -> Build (Fetch (Shape, Vector a))
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newtype Scalar a
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Scalar :: a -> Scalar a
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[unScalar] :: Scalar a -> a
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instance Data.String.IsString a => Data.String.IsString (TensorFlow.Nodes.Scalar a)
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instance GHC.Real.RealFrac a => GHC.Real.RealFrac (TensorFlow.Nodes.Scalar a)
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instance GHC.Float.RealFloat a => GHC.Float.RealFloat (TensorFlow.Nodes.Scalar a)
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instance GHC.Real.Real a => GHC.Real.Real (TensorFlow.Nodes.Scalar a)
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instance GHC.Float.Floating a => GHC.Float.Floating (TensorFlow.Nodes.Scalar a)
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instance GHC.Real.Fractional a => GHC.Real.Fractional (TensorFlow.Nodes.Scalar a)
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instance GHC.Num.Num a => GHC.Num.Num (TensorFlow.Nodes.Scalar a)
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instance GHC.Classes.Ord a => GHC.Classes.Ord (TensorFlow.Nodes.Scalar a)
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instance GHC.Classes.Eq a => GHC.Classes.Eq (TensorFlow.Nodes.Scalar a)
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instance GHC.Show.Show a => GHC.Show.Show (TensorFlow.Nodes.Scalar a)
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instance GHC.Base.Functor TensorFlow.Nodes.Fetch
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instance GHC.Base.Applicative TensorFlow.Nodes.Fetch
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instance (TensorFlow.Nodes.Nodes t1, TensorFlow.Nodes.Nodes t2) => TensorFlow.Nodes.Nodes (t1, t2)
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instance (TensorFlow.Nodes.Nodes t1, TensorFlow.Nodes.Nodes t2, TensorFlow.Nodes.Nodes t3) => TensorFlow.Nodes.Nodes (t1, t2, t3)
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instance (TensorFlow.Nodes.Fetchable t1 a1, TensorFlow.Nodes.Fetchable t2 a2) => TensorFlow.Nodes.Fetchable (t1, t2) (a1, a2)
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instance (TensorFlow.Nodes.Fetchable t1 a1, TensorFlow.Nodes.Fetchable t2 a2, TensorFlow.Nodes.Fetchable t3 a3) => TensorFlow.Nodes.Fetchable (t1, t2, t3) (a1, a2, a3)
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instance TensorFlow.Nodes.Nodes t => TensorFlow.Nodes.Nodes [t]
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instance TensorFlow.Nodes.Fetchable t a => TensorFlow.Nodes.Fetchable [t] [a]
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instance TensorFlow.Nodes.Nodes TensorFlow.Output.ControlNode
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instance (a ~ ()) => TensorFlow.Nodes.Fetchable TensorFlow.Output.ControlNode a
|
|
instance TensorFlow.Nodes.Nodes (TensorFlow.Tensor.Tensor v a)
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|
instance (TensorFlow.Types.TensorType a, a ~ a') => TensorFlow.Nodes.Fetchable (TensorFlow.Tensor.Tensor v a) (Data.Vector.Vector a')
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instance (TensorFlow.Types.TensorType a, a ~ a') => TensorFlow.Nodes.Fetchable (TensorFlow.Tensor.Tensor v a) (TensorFlow.Nodes.Scalar a')
|
|
|
|
module TensorFlow.ControlFlow
|
|
|
|
-- | Modify a <a>Build</a> action, such that all new ops rendered in it
|
|
-- will depend on the nodes in the first argument.
|
|
withControlDependencies :: Nodes t => t -> Build a -> Build a
|
|
|
|
-- | Create an op that groups multiple operations.
|
|
--
|
|
-- When this op finishes, all ops in the input <tt>n</tt> have finished.
|
|
-- This op has no output.
|
|
group :: Nodes t => t -> Build ControlNode
|
|
|
|
-- | Returns a <a>Tensor</a> with the same shape and contents as the input.
|
|
identity :: TensorType a => Tensor v a -> Tensor v a
|
|
|
|
-- | Does nothing. Only useful as a placeholder for control edges.
|
|
noOp :: ControlNode
|
|
|
|
-- | Returns a <a>Tensor</a> with a given name and the same shape and
|
|
-- contents as the input.
|
|
--
|
|
-- TODO(judahjacobson): This breaks when used with uninitialize
|
|
-- <tt>Tensor Ref</tt>s, since <tt>RefIdentity</tt> doesn't have
|
|
-- SetAllowsUninitializedInput(). Look into whether we can change that
|
|
-- op.
|
|
named :: TensorType a => Text -> Tensor v a -> Tensor v a
|
|
|
|
module TensorFlow.Session
|
|
data Session a
|
|
|
|
-- | Setting of an option for the session (see
|
|
-- <a>runSessionWithOptions</a>).
|
|
data SessionOption
|
|
|
|
-- | Uses the specified config for the created session.
|
|
sessionConfig :: ConfigProto -> SessionOption
|
|
|
|
-- | Target can be: "local", ip:port, host:port. The set of supported
|
|
-- factories depends on the linked in libraries. REQUIRES
|
|
-- "/<i>learning</i>brain/public:tensorflow_remote" dependency for the
|
|
-- binary.
|
|
sessionTarget :: ByteString -> SessionOption
|
|
|
|
-- | Run <a>Session</a> actions in a new TensorFlow session.
|
|
runSession :: Session a -> IO a
|
|
|
|
-- | Run <a>Session</a> actions in a new TensorFlow session created with
|
|
-- the given option setter actions (<a>sessionTarget</a>,
|
|
-- <a>sessionConfig</a>).
|
|
runSessionWithOptions :: [SessionOption] -> Session a -> IO a
|
|
|
|
-- | Lift a <a>Build</a> action into a <a>Session</a>, including any
|
|
-- explicit op renderings.
|
|
build :: Build a -> Session a
|
|
|
|
-- | Helper combinator for doing something with the result of a
|
|
-- <a>Build</a> action. Example usage:
|
|
--
|
|
-- <pre>
|
|
-- buildAnd run :: Fetchable t a => Build t -> Session a
|
|
-- </pre>
|
|
buildAnd :: (a -> Session b) -> Build a -> Session b
|
|
|
|
-- | Lift a <a>Build</a> action into a <a>Session</a>, including any
|
|
-- explicit op renderings. Returns the merged summary ops which can be
|
|
-- used for logging, see <a>build</a> for a convenient wrapper.
|
|
buildWithSummary :: Build a -> Session (a, [SummaryTensor])
|
|
|
|
-- | Add all pending rendered nodes to the TensorFlow graph and runs any
|
|
-- pending initializers.
|
|
--
|
|
-- Note that run, runWithFeeds, etc. will all call this function
|
|
-- implicitly.
|
|
extend :: Session ()
|
|
addGraphDef :: GraphDef -> Build ()
|
|
|
|
-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
|
|
-- already rendered, and fetch the corresponding values for <tt>a</tt>.
|
|
run :: Fetchable t a => t -> Session a
|
|
|
|
-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
|
|
-- already rendered, feed the given input values, and fetch the
|
|
-- corresponding result values for <tt>a</tt>.
|
|
runWithFeeds :: Fetchable t a => [Feed] -> t -> Session a
|
|
|
|
-- | Run a subgraph <tt>t</tt>, rendering and extending any dependent nodes
|
|
-- that aren't already rendered. This behaves like <a>run</a> except that
|
|
-- it doesn't do any fetches.
|
|
run_ :: Nodes t => t -> Session ()
|
|
|
|
-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
|
|
-- already rendered, feed the given input values, and fetch the
|
|
-- corresponding result values for <tt>a</tt>. This behaves like
|
|
-- <a>runWithFeeds</a> except that it doesn't do any fetches.
|
|
runWithFeeds_ :: Nodes t => [Feed] -> t -> Session ()
|
|
|
|
-- | Starts a concurrent thread which evaluates the given Nodes forever
|
|
-- until runSession exits or an exception occurs. Graph extension happens
|
|
-- synchronously, but the resultant run proceeds as a separate thread.
|
|
asyncProdNodes :: Nodes t => t -> Session ()
|
|
instance Control.Monad.IO.Class.MonadIO TensorFlow.Session.Session
|
|
instance GHC.Base.Monad TensorFlow.Session.Session
|
|
instance GHC.Base.Applicative TensorFlow.Session.Session
|
|
instance GHC.Base.Functor TensorFlow.Session.Session
|