update minimal example

Author: cbovar

README.md

@@ -49,42 +49,55 @@ Tag [v0.2.0](https://github.com/cbovar/ConvNetSharp/tree/v0.2.0) was created jus
 Here's a minimum example of defining a **2-layer neural network** and training
 it on a single data point:
 ```c#
-  // species a 2-layer neural network with one hidden layer of 20 neurons
-  var net = new Net<double>();
-  
-  // input layer declares size of input. here: 2-D data
-  // ConvNetJS works on 3-Dimensional volumes (width, height, depth), but if you're not dealing with images
-  // then the first two dimensions (width, height) will always be kept at size 1
-  net.AddLayer(new InputLayer(1, 1, 2));
-  
-  // declare 20 neurons
-  net.AddLayer(new FullyConnLayer(20));
-  
-  // declare a ReLU (rectified linear unit non-linearity)
-  net.AddLayer(new ReluLayer());
-  
-  // declare a fully connected layer that will be used by the softmax layer
-  net.AddLayer(new FullyConnLayer(10));
-  
-  // declare the linear classifier on top of the previous hidden layer
-  net.AddLayer(new SoftmaxLayer(10));
-  
-  // forward a random data point through the network
-  var x = new Volume(new[] { 0.3, -0.5 }, new Shape(2));
-  
-  var prob = net.Forward(x);
-  
-  // prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
-  Console.WriteLine("probability that x is class 0: " + prob.Get(0)); // prints e.g. 0.50101
-  
-  var trainer = new SgdTrainer(net) { LearningRate = 0.01, L2Decay = 0.001 };
-  trainer.Train(x, new Volume(new[] { 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, new Shape(1, 1, 10, 1))); // train the network, specifying that x is class zero
-  
-  var prob2 = net.Forward(x);
-  Console.WriteLine("probability that x is class 0: " + prob2.Get(0));
-  // now prints 0.50374, slightly higher than previous 0.50101: the networks
-  // weights have been adjusted by the Trainer to give a higher probability to
-  // the class we trained the network with (zero)
+using ConvNetSharp.Core.Training.Double;
+using ConvNetSharp.Volume;
+using ConvNetSharp.Volume.Double;
+
+namespace MinimalExample
+{
+    internal class Program
+    {
+        private static void Main()
+        {
+            // species a 2-layer neural network with one hidden layer of 20 neurons
+            var net = new Net<double>();
+
+            // input layer declares size of input. here: 2-D data
+            // ConvNetJS works on 3-Dimensional volumes (width, height, depth), but if you're not dealing with images
+            // then the first two dimensions (width, height) will always be kept at size 1
+            net.AddLayer(new InputLayer(1, 1, 2));
+
+            // declare 20 neurons
+            net.AddLayer(new FullyConnLayer(20));
+
+            // declare a ReLU (rectified linear unit non-linearity)
+            net.AddLayer(new ReluLayer());
+
+            // declare a fully connected layer that will be used by the softmax layer
+            net.AddLayer(new FullyConnLayer(10));
+
+            // declare the linear classifier on top of the previous hidden layer
+            net.AddLayer(new SoftmaxLayer(10));
+
+            // forward a random data point through the network
+            var x =  BuilderInstance.Volume.From(new[] { 0.3, -0.5 }, new Shape(2));
+
+            var prob = net.Forward(x);
+
+            // prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
+            Console.WriteLine("probability that x is class 0: " + prob.Get(0)); // prints e.g. 0.50101
+
+            var trainer = new SgdTrainer(net) { LearningRate = 0.01, L2Decay = 0.001 };
+            trainer.Train(x, BuilderInstance.Volume.From(new[] { 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, new Shape(1, 1, 10, 1))); // train the network, specifying that x is class zero
+
+            var prob2 = net.Forward(x);
+            Console.WriteLine("probability that x is class 0: " + prob2.Get(0));
+            // now prints 0.50374, slightly higher than previous 0.50101: the networks
+            // weights have been adjusted by the Trainer to give a higher probability to
+            // the class we trained the network with (zero)
+        }
+    }
+}
 ```
 
 ## Fluent API (see [FluentMnistDemo](https://github.com/cbovar/ConvNetSharp/tree/master/Examples/FluentMnistDemo))