fix of the namespace reference errors

Data_science/MachineLearning/DeepLearning/CNN/Simple/ConvolutionalNN.vb

@@ -1,9 +1,8 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.losslayers
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.layers
-Imports Microsoft.VisualBasic.MachineLearning.CNN
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.layers
+Imports Microsoft.VisualBasic.MachineLearning.CNN.losslayers
 
-Namespace ConsoleApp1
+Namespace CNN
 
     ''' <summary>
     ''' A network class holding the layers and some helper functions

Data_science/MachineLearning/DeepLearning/CNN/Simple/LayerBuilder.vb

@@ -1,7 +1,7 @@
 Imports System.Runtime.CompilerServices
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.layers
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.losslayers
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.layers
+Imports Microsoft.VisualBasic.MachineLearning.CNN.losslayers
 
 <Assembly: InternalsVisibleTo("MLkit")>
 

Data_science/MachineLearning/DeepLearning/CNN/Simple/data/BackPropResult.vb

@@ -1,4 +1,4 @@
-Namespace ConsoleApp1.data
+Namespace CNN.data
 
     ''' <summary>
     ''' When we have done a back propagation of the network we will receive a

Data_science/MachineLearning/DeepLearning/CNN/Simple/data/DataBlock.vb

@@ -2,7 +2,7 @@
 Imports randf = Microsoft.VisualBasic.Math.RandomExtensions
 Imports std = System.Math
 
-Namespace ConsoleApp1.data
+Namespace CNN.data
 
     ''' <summary>
     ''' Holding all the data handled by the network. So a layer will receive

Data_science/MachineLearning/DeepLearning/CNN/Simple/data/OutputDefinition.vb

@@ -1,4 +1,6 @@
-Namespace ConsoleApp1.data
+Imports Microsoft.VisualBasic.Serialization.JSON
+
+Namespace CNN.data
 
     ''' <summary>
     ''' This class will hold the definitions that bridge two layers.
@@ -12,6 +14,10 @@
         Public Overridable Property OutY As Integer
         Public Overridable Property Depth As Integer
 
+        Public Overrides Function ToString() As String
+            Return Me.GetJson
+        End Function
+
     End Class
 
 End Namespace

Data_science/MachineLearning/DeepLearning/CNN/Simple/data/TrainResult.vb

@@ -1,6 +1,6 @@
 Imports System.Text
 
-Namespace ConsoleApp1.data
+Namespace CNN.data
 
     ''' <summary>
     ''' Created by danielp on 1/27/17.

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/ConvolutionLayer.vb

@@ -1,8 +1,7 @@
-
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' This layer uses different filters to find attributes of the data that

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/DropoutLayer.vb

@@ -1,7 +1,7 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports randf = Microsoft.VisualBasic.Math.RandomExtensions
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' This layer will remove some random activations in order to

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/FullyConnectedLayer.vb

@@ -1,6 +1,6 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' Neurons in a fully connected layer have full connections to all

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/InputLayer.vb

@@ -1,6 +1,6 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' The input layer is a simple layer that will pass the data though and

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/Layer.vb

@@ -1,6 +1,6 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' A convolution neural network is built of layers that the data traverses

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/LocalResponseNormalizationLayer.vb

@@ -1,7 +1,7 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' This layer is useful when we are dealing with ReLU neurons. Why is that?

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/MaxoutLayer.vb

@@ -1,8 +1,8 @@
 Imports Microsoft.VisualBasic.Language.Java
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' Implements Maxout nonlinearity that computes x to max(x)

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/PoolingLayer.vb

@@ -1,9 +1,9 @@
 Imports Microsoft.VisualBasic.Language.Java
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' This layer will reduce the dataset by creating a smaller zoomed out

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/RectifiedLinearUnitsLayer.vb

@@ -1,8 +1,6 @@
-Imports System
-Imports System.Collections.Generic
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' This is a layer of neurons that applies the non-saturating activation

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/SigmoidLayer.vb

@@ -1,8 +1,7 @@
-Imports std = System.Math
-Imports System.Collections.Generic
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
+Imports std = System.Math
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' Implements Sigmoid nonlinearity elementwise x to 1/(1+e^(-x))

Data_science/MachineLearning/DeepLearning/CNN/Simple/layers/TanhLayer.vb

@@ -1,7 +1,7 @@
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.layers
+Namespace CNN.layers
 
     ''' <summary>
     ''' Implements Tanh nonlinearity elementwise x to tanh(x)

Data_science/MachineLearning/DeepLearning/CNN/Simple/losslayers/LossLayer.vb

@@ -1,9 +1,8 @@
 
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.layers
 
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.layers
-
-Namespace ConsoleApp1.losslayers
+Namespace CNN.losslayers
 
     ''' <summary>
     ''' Created by danielp on 1/25/17.

Data_science/MachineLearning/DeepLearning/CNN/Simple/losslayers/RegressionLayer.vb

@@ -1,7 +1,6 @@
-Imports System
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 
-Namespace ConsoleApp1.losslayers
+Namespace CNN.losslayers
 
     ''' <summary>
     ''' Regression layer is used when your output is an area of data.

Data_science/MachineLearning/DeepLearning/CNN/Simple/losslayers/SVMLayer.vb

@@ -1,7 +1,6 @@
-Imports System
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 
-Namespace ConsoleApp1.losslayers
+Namespace CNN.losslayers
 
     ''' <summary>
     ''' This layer uses the input area trying to find a line to

Data_science/MachineLearning/DeepLearning/CNN/Simple/losslayers/SoftMaxLayer.vb

@@ -1,8 +1,8 @@
 Imports Microsoft.VisualBasic.Language.Java
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.losslayers
+Namespace CNN.losslayers
 
 
     ''' <summary>

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/AdaDeltaTrainer.vb

@@ -1,8 +1,8 @@
 Imports Microsoft.VisualBasic.Language.Java
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
     ''' <summary>
     ''' Adaptive delta will look at the differences between the expected result and the current result to train the network.

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/AdaGradTrainer.vb

@@ -1,6 +1,6 @@
 Imports std = System.Math
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
 
     ''' <summary>

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/AdamTrainer.vb

@@ -1,8 +1,8 @@
 Imports Microsoft.VisualBasic.Language.Java
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
     ''' <summary>
     ''' Adaptive Moment Estimation is an update to RMSProp optimizer. In this running average of both the

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/NesterovTrainer.vb

@@ -1,5 +1,5 @@
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
     ''' <summary>
     ''' Another extension of gradient descent is due to Yurii Nesterov from 1983,[7] and has been subsequently generalized

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/SGDTrainer.vb

@@ -1,5 +1,5 @@
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
     ''' <summary>
     ''' Stochastic gradient descent (often shortened in SGD), also known as incremental gradient descent, is a

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/Trainer.vb

@@ -1,9 +1,10 @@
 Imports Microsoft.VisualBasic.Language.Java
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
+Imports Microsoft.VisualBasic.Linq
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
 Imports std = System.Math
 
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
 
     ''' <summary>

Data_science/MachineLearning/DeepLearning/CNN/Simple/trainers/WindowGradTrainer.vb

@@ -1,6 +1,6 @@
 Imports std = System.Math
 
-Namespace ConsoleApp1.trainers
+Namespace CNN.trainers
 
     ''' <summary>
     ''' This is AdaGrad but with a moving window weighted average

Data_science/MachineLearning/DeepLearning/test/MnistTest.vb

@@ -2,12 +2,9 @@
 Imports Microsoft.VisualBasic.Language
 Imports Microsoft.VisualBasic.Language.Java
 Imports Microsoft.VisualBasic.MachineLearning.CNN
+Imports Microsoft.VisualBasic.MachineLearning.CNN.data
+Imports Microsoft.VisualBasic.MachineLearning.CNN.trainers
 Imports Microsoft.VisualBasic.MachineLearning.ComponentModel.StoreProcedure
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.data
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.layers
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.losslayers
-Imports Microsoft.VisualBasic.MachineLearning.ConsoleApp1.trainers
 
 Namespace ConsoleApp1