SciSharp · Oceania2018 · May 30, 2019 · May 30, 2019 · May 30, 2019 · May 30, 2019
@@ -15,6 +15,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Keras.Example", "test\Keras
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Keras.UnitTest", "test\KerasNET.Test\Keras.UnitTest.csproj", "{A5839A45-A117-4BEA-898B-DE1ED6E0D58F}"
 EndProject
+Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSharp", "test\TensorFlowNET.Examples.FSharp\TensorFlowNET.Examples.FSharp.fsproj", "{62BC3801-F0D3-44A9-A0AC-712F40C8F961}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -45,6 +47,10 @@ Global
 		{A5839A45-A117-4BEA-898B-DE1ED6E0D58F}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{A5839A45-A117-4BEA-898B-DE1ED6E0D58F}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{A5839A45-A117-4BEA-898B-DE1ED6E0D58F}.Release|Any CPU.Build.0 = Release|Any CPU
+		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

@@ -368,7 +368,7 @@ private static Tensor[] _DefaultGradYs(Tensor[] grad_ys, Tensor[] ys, bool coloc
                     if (y.dtype.is_complex())
                         throw new TypeAccessException($"Gradients of complex tensors must set grad_ys (y.dtype = {y.dtype})");
                     var shape = array_ops.shape(y);
-                    var constant = constant_op.constant(1.0f, name: $"grad_ys_{i}");
+                    var constant = constant_op.constant(y.dtype == TF_DataType.TF_DOUBLE ? (object)1.0 : (object)1.0f, name: $"grad_ys_{i}");
                     var fill = gen_array_ops.fill(shape, constant);
                     new_grad_ys.Add(fill);
                 }

@@ -0,0 +1,104 @@
+module FunctionApproximation
+
+//reduced example from https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Function%20Approximation%20by%20Neural%20Network/Function%20approximation%20by%20linear%20model%20and%20deep%20network.ipynb
+
+open NumSharp
+open Tensorflow
+open System
+
+
+let run()=
+
+    let N_points = 75 // Number of points for constructing function
+    let x_min = 1.0 // Min of the range of x (feature)
+    let x_max = 15.0 // Max of the range of x (feature)
+    let noise_mean = 0.0 // Mean of the Gaussian noise adder
+    let noise_sd = 10.0 // Std.Dev of the Gaussian noise adder
+
+    let linspace points = [| for i in 0 .. (points - 1) -> x_min + (x_max - x_min)/(float)points * (float)i |]
+
+    let  func_trans(xAr:float []) = 
+        xAr
+        |>Array.map (fun (x:float) -> (20.0 * x+3.0 * System.Math.Pow(x,2.0)+0.1 * System.Math.Pow(x,3.0))*sin(x)*exp(-0.1*x))
+
+    let X_raw = linspace N_points
+    let Y_raw = func_trans(X_raw)
+    let X_mtr = Array2D.init X_raw.Length 1 (fun i j -> X_raw.[i])
+    let X = np.array(X_mtr)
+
+    let noise_x = np.random.normal(noise_mean,noise_sd,N_points)
+    let y =  np.array(Y_raw)+noise_x
+
+    let X_train = X
+    let y_train = y
+
+    let learning_rate = 0.00001
+    let training_epochs = 35000
+
+    let n_input = 1  // Number of features
+    let n_output = 1  // Regression output is a number only
+    let n_hidden_layer_1 = 25 // Hidden layer 1
+    let n_hidden_layer_2 = 25 // Hidden layer 2
+
+    let x = tf.placeholder(tf.float64, new TensorShape(N_points,n_input))
+    let y = tf.placeholder(tf.float64, new TensorShape(n_output))
+
+
+    let weights = dict[
+        "hidden_layer_1", tf.Variable(tf.random_normal([|n_input; n_hidden_layer_1|],dtype=tf.float64))
+        "hidden_layer_2", tf.Variable(tf.random_normal([|n_hidden_layer_1; n_hidden_layer_2|],dtype=tf.float64))
+        "out", tf.Variable(tf.random_normal([|n_hidden_layer_2; n_output|],dtype=tf.float64))
+    ]
+    let biases = dict[
+        "hidden_layer_1", tf.Variable(tf.random_normal([|n_hidden_layer_1|],dtype=tf.float64))
+        "hidden_layer_2", tf.Variable(tf.random_normal([|n_hidden_layer_2|],dtype=tf.float64))
+        "out", tf.Variable(tf.random_normal([|n_output|],dtype=tf.float64))
+    ]
+
+
+    // Hidden layer with RELU activation
+
+    let layer_1 = tf.add(tf.matmul(x, weights.["hidden_layer_1"]._AsTensor()),biases.["hidden_layer_1"])
+    let layer_1 = tf.nn.relu(layer_1)
+
+    let layer_2 = tf.add(tf.matmul(layer_1, weights.["hidden_layer_2"]._AsTensor()),biases.["hidden_layer_2"])
+    let layer_2 = tf.nn.relu(layer_2)
+
+    // Output layer with linear activation
+    let ops = tf.add(tf.matmul(layer_2, weights.["out"]._AsTensor()), biases.["out"])
+
+    // Define loss and optimizer
+    let cost = tf.reduce_mean(tf.square(tf.squeeze(ops)-y))
+
+    let gs = tf.Variable(1, trainable= false, name= "global_step")
+
+    let optimizer = tf.train.GradientDescentOptimizer(learning_rate=(float32)learning_rate).minimize(cost,global_step = gs)
+
+    let init = tf.global_variables_initializer()
+
+
+    Tensorflow.Python.``with``(tf.Session(), fun (sess:Session) ->
+        sess.run(init)  |> ignore  
+        // Loop over epochs
+        for epoch in [0..training_epochs] do
+            // Run optimization process (backprop) and cost function (to get loss value)
+
+            let result=sess.run([|optimizer:>ITensorOrOperation; gs._AsTensor():>ITensorOrOperation; cost:>ITensorOrOperation|], new FeedItem(x, X_train), new FeedItem(y, y_train))
+
+
+            let loss_value = (double) result.[2];
+
+            let step = (int) result.[1];
+
+            if epoch % 1000 = 0 then
+                sprintf "Step %d loss: %f" step loss_value |> Console.WriteLine
+        let w=sess.run(weights |> Array.ofSeq |> Array.map (fun pair -> pair.Value))
+        let b = sess.run(biases |> Array.ofSeq |> Array.map (fun pair -> pair.Value))
+        let yhat=sess.run([|ops:>ITensorOrOperation|],new FeedItem(x,X_train))
+        for i in [0..(N_points-1)] do
+            sprintf "pred %f real: %f" ((double)(yhat.[0].[i].[0])) ((double)Y_raw.[i]) |> Console.WriteLine
+    )
+
+
+
+
@@ -0,0 +1,8 @@
+// Learn more about F# at http://fsharp.org
+
+open System
+
+[<EntryPoint>]
+let main argv =
+    FunctionApproximation.run()
+    0 // return an integer exit code
@@ -0,0 +1,17 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>netcoreapp2.2</TargetFramework>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <Compile Include="FunctionApproximation.fs" />
+    <Compile Include="Program.fs" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
+  </ItemGroup>
+
+</Project>