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Baby steps with CNTK and F#
// Code for the post http://brandewinder.com/2017/12/23/baby-steps-with-cntk-and-fsharp/
open System
open System.IO
open System.Collections.Generic
Environment.SetEnvironmentVariable("Path",
Environment.GetEnvironmentVariable("Path") + ";" + __SOURCE_DIRECTORY__)
let dependencies = [
"./packages/CNTK.CPUOnly/lib/net45/x64/"
"./packages/CNTK.CPUOnly/support/x64/Dependency/"
"./packages/CNTK.CPUOnly/support/x64/Dependency/Release/"
"./packages/CNTK.CPUOnly/support/x64/Release/"
]
dependencies
|> Seq.iter (fun dep ->
let path = Path.Combine(__SOURCE_DIRECTORY__,dep)
Environment.SetEnvironmentVariable("Path",
Environment.GetEnvironmentVariable("Path") + ";" + path)
)
#I "./packages/CNTK.CPUOnly/lib/net45/x64/"
#I "./packages/CNTK.CPUOnly/support/x64/Dependency/"
#I "./packages/CNTK.CPUOnly/support/x64/Dependency/Release/"
#I "./packages/CNTK.CPUOnly/support/x64/Release/"
#r "./packages/CNTK.CPUOnly/lib/net45/x64/Cntk.Core.Managed-2.3.1.dll"
open CNTK
let inputDim = 2
let outputDim = 1
let input = Variable.InputVariable(NDShape.CreateNDShape [inputDim], DataType.Double, "input")
let output = Variable.InputVariable(NDShape.CreateNDShape [outputDim], DataType.Double, "output")
let device = DeviceDescriptor.CPUDevice
let predictor =
let dim = input.Shape.[0]
let weights = new Parameter(NDShape.CreateNDShape [dim], DataType.Double, 0.0, device, "weights")
// create an intermediate Function
let product = CNTKLib.TransposeTimes(input, weights)
let constant = new Parameter(NDShape.CreateNDShape [ outputDim ], DataType.Double, 0.0, device, "constant")
CNTKLib.Plus(new Variable(product), constant)
let inputValue = Value.CreateBatch(NDShape.CreateNDShape [inputDim], [| 3.0; 5.0 |], device)
let inputMap =
let map = Dictionary<Variable,Value>()
map.Add(input, inputValue)
map
let predictedOutput = predictor.Output
let weights =
predictor.Parameters ()
|> Seq.find (fun p -> p.Name = "weights")
let constant =
predictor.Parameters ()
|> Seq.find (fun p -> p.Name = "constant")
let outputMap =
let map = Dictionary<Variable,Value>()
map.Add(predictedOutput, null)
map.Add(weights, null)
map.Add(constant, null)
map
predictor.Evaluate(inputMap,outputMap,device)
let currentPrediction =
outputMap.[predictedOutput].GetDenseData<float>(predictedOutput)
|> Seq.map (fun x -> x |> Seq.toArray)
|> Seq.toArray
let currentWeights =
outputMap.[weights].GetDenseData<float>(weights)
|> Seq.map (fun x -> x |> Seq.toArray)
|> Seq.toArray
let currentConstant =
outputMap.[constant].GetDenseData<float>(constant)
|> Seq.map (fun x -> x |> Seq.toArray)
|> Seq.toArray
// training : we need to supply labels, and loss function
let batchInputValue = Value.CreateBatch(NDShape.CreateNDShape [inputDim], [| 3.0; 5.0 |], device)
let batchOutputValue = Value.CreateBatch(NDShape.CreateNDShape [outputDim], [| 10.0 |], device)
let batchInputValue = Value.CreateBatch(NDShape.CreateNDShape [inputDim], [| 3.0; 5.0; 1.0; 7.0 |], device)
let batchOutputValue = Value.CreateBatch(NDShape.CreateNDShape [outputDim], [| 10.0; 7.0 |], device)
let batch =
[
input,batchInputValue
output,batchOutputValue
]
|> dict
let loss = CNTKLib.SquaredError(new Variable(predictor), output, "loss")
let evaluation = CNTKLib.SquaredError(new Variable(predictor), output, "evaluation")
let learningRatePerSample = new TrainingParameterScheduleDouble(0.01, uint32 1)
let learners =
ResizeArray<Learner>(
[
Learner.SGDLearner(predictor.Parameters(), learningRatePerSample)
]
)
let trainer = Trainer.CreateTrainer(predictor, loss, evaluation, learners)
for i in 0 .. 10 do
let _ = trainer.TrainMinibatch(batch,true,device)
trainer.PreviousMinibatchLossAverage () |> printfn "Loss: %f"
trainer.PreviousMinibatchEvaluationAverage () |> printfn "Eval: %f"
// train on a batch
module BatchExample =
let inputDim = 2
let outputDim = 1
let input = Variable.InputVariable(NDShape.CreateNDShape [inputDim], DataType.Double, "input")
let output = Variable.InputVariable(NDShape.CreateNDShape [outputDim], DataType.Double, "output")
let device = DeviceDescriptor.CPUDevice
let predictor =
let dim = input.Shape.[0]
let weights = new Parameter(NDShape.CreateNDShape [dim], DataType.Double, 0.0, device, "weights")
let product = CNTKLib.TransposeTimes(input, weights)
let constant = new Parameter(NDShape.CreateNDShape [ outputDim ], DataType.Double, 0.0, device, "constant")
CNTKLib.Plus(new Variable(product), constant)
let realModel (features:float[]) =
3.0 * features.[0] - 2.0 * features.[1] + 5.0
let rng = Random(123456)
let batch () =
let batchSize = 32
let features = [| rng.NextDouble(); rng.NextDouble() |]
let labels = [| realModel features |]
let inputValues = Value.CreateBatch(NDShape.CreateNDShape [inputDim], features, device)
let outputValues = Value.CreateBatch(NDShape.CreateNDShape [outputDim], labels, device)
[
input,inputValues
output,outputValues
]
|> dict
let loss = CNTKLib.SquaredError(new Variable(predictor), output, "loss")
let evaluation = CNTKLib.SquaredError(new Variable(predictor), output, "evaluation")
let learningRatePerSample = new TrainingParameterScheduleDouble(0.05, uint32 1)
let learners =
ResizeArray<Learner>(
[
Learner.SGDLearner(predictor.Parameters(), learningRatePerSample)
]
)
let trainer = Trainer.CreateTrainer(predictor, loss, evaluation, learners)
#time "on"
for _ in 1 .. 1000 do
let example = batch ()
trainer.TrainMinibatch(example,true,device) |> ignore
trainer.PreviousMinibatchLossAverage () |> printfn "Loss: %f"
open System.Collections.Generic
let inputMap =
let map = Dictionary<Variable,Value>()
map
let weights =
predictor.Parameters ()
|> Seq.find (fun p -> p.Name = "weights")
let constant =
predictor.Parameters ()
|> Seq.find (fun p -> p.Name = "constant")
let outputMap =
let map = Dictionary<Variable,Value>()
map.Add(weights, null)
map.Add(constant, null)
map
predictor.Evaluate(inputMap,outputMap,device)
let currentWeights =
outputMap.[weights].GetDenseData<float>(weights)
|> Seq.map (fun x -> x |> Seq.toArray)
|> Seq.toArray
let currentConstant =
outputMap.[constant].GetDenseData<float>(constant)
|> Seq.map (fun x -> x |> Seq.toArray)
|> Seq.toArray
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