title: Unexpected results from CNTK
body: <p>I'm running a fairly simple test of CNTK but not getting results that make much sense. My training/test data consist of one feature and one label. The feature is a decimal and the label will be an integer between 0-5. In a majority of cases the value of the label will be 0 or 1 and get increasingly rare as the value gets higher.  5 appears in about 16/30,000 cases.</p>
  
  <p>What's odd is that when I output the results they indicate that each possible label has about an equal chance of occurring.  I would expect 0 or one to be the most likely and 5 the be extremely unlikely.  I was hoping SO could shed some light on what I might be doing wrong here.  I included some sample data, sample output and a config file below.</p>
  
  <p>Config:</p>
  
  <pre><code># Parameters can be overwritten on the command line
  # for example: cntk configFile=myConfigFile RootDir=../..
  # For running from Visual Studio add
  # currentDirectory=$(SolutionDir)/&lt;path to corresponding data folder&gt;
  RootDir = ".."
  
  ConfigDir = "$RootDir$/Config"
  DataDir = "$RootDir$/Data"
  OutputDir = "$RootDir$/Output"
  ModelDir = "$OutputDir$/Models"
  
  # deviceId=-1 for CPU, &gt;=0 for GPU devices, "auto" chooses the best GPU, or CPU if no usable GPU is available
  deviceId = 0
  
  command = Simple_Demo_Train:Simple_Demo_Train
  
  precision = "float"
  traceLevel = 1
  modelPath = "$ModelDir$/simple.dnn"
  outputNodeNames = ScaledLogLikelihood
  
  #######################################
  #  TRAINING CONFIG                    #
  #######################################
  
  Simple_Demo_Train = [
      action = "train"
  
      # Notation xxx:yyy*n:zzz is equivalent to xxx, then yyy repeated n times, then zzz
      # Example: 10:20*3:5 is equivalent to 10:20:20:20:5
      SimpleNetworkBuilder = [
          # 2 input, 2 50-element hidden, 2 output
          layerSizes = 1:50*3:6
          trainingCriterion = "CrossEntropyWithSoftmax"
          evalCriterion = "ErrorPrediction"
          layerTypes = "Sigmoid"
          initValueScale = 1.0
          applyMeanVarNorm = true
          uniformInit = true
          needPrior = true
      ]
  
      SGD = [
          # epochSize = 0 means epochSize is the size of the training set
          epochSize = 0
          minibatchSize = 25
          learningRatesPerMB = 0.5:0.2*20:0.1
          momentumPerMB = 0.9
          dropoutRate = 0.0
          maxEpochs = 10000
      ]
  
      # Parameter values for the reader
      reader = [
          readerType = "UCIFastReader"
          file = "$DataDir$/train.txt"
          miniBatchMode = "partial"
          randomize = "none"
          verbosity = 1
  
          features = [
              dim = 1        # two-dimensional input data
              start = 0      # Start with first element on line
          ]
  
          labels = [
              start = 1      # Skip two elements
              dim = 1        # One label dimension
              labelDim = 5   # Two labels possible
              labelMappingFile = "$DataDir$/mapping.txt"
          ]
      ]
  ]
  
  ########################################
  #  TEST RESULTS                        #
  #  (computes prediction error and      #
  #   perplexity on a test set and       #
  #   writes the output to the console.) #
  ########################################
  
  Simple_Demo_Test = [
      action = "test"
  
      # Parameter values for the reader
      reader = [
          readerType = "UCIFastReader"
          file = "$DataDir$/test.txt"
          miniBatchMode = "partial"
          randomize = "none"
          verbosity = 1
  
          features = [
              dim = 1        # two-dimensional input data
              start = 0      # Start with first element on line
          ]
  
          labels = [
              start = 1      # Skip two elements
              dim = 1        # One label dimension
              labelDim = 5   # Two labels possible
              labelMappingFile = "$DataDir$/mapping.txt"
          ]
      ]
  ]
  
  ########################################
  #  OUTPUT RESULTS                      #
  #  (Computes the labels for a test set #
  #   and writes the results to a file.) #
  ########################################
  
  Simple_Demo_Output=[
      action = "write"
  
      # Parameter values for the reader
      reader = [
          readerType = "UCIFastReader"
          file = "$DataDir$/test.txt"
          miniBatchMode = "partial"
          randomize = "none"
          verbosity = 1
  
          features = [
              dim = 1        # two-dimensional input data
              start = 0      # Start with first element on line
          ]
  
          labels = [
              start = 1      # Skip two elements
              dim = 1        # One label dimension
              labelDim = 5   # Two labels possible
              labelMappingFile = "$DataDir$/mapping.txt"
          ]
      ]
  
      outputPath = "$OutputDir$/SimpleOutput"    # Dump output as text
  ]
  </code></pre>
  
  <p>Sample Training Data:</p>
  
  <pre><code>0.86 2
  0.84 0
  6.818182 0
  1.34 1
  1 1
  0.92 0
  0.7692308 0
  0.755102 1
  0.86 2
  5.466667 0
  0.96 0
  0.9459459 1
  1 4
  1 0
  0.8421053 2
  5.5 0
  0.84 2
  1.2 2
  1.32 1
  0.98 0
  1 1
  1.2 2
  5.4 1
  1.06 2
  0.98 1
  1.041667 3
  0.82 2
  7.333333 0
  </code></pre>
  
  <p>Sample Output:</p>
  
  <pre><code>3.18673 3.18266 3.19894 3.18264 3.2388 3.235 
  3.18683 3.18272 3.19895 3.18264 3.23872 3.23491 
  3.18668 3.18263 3.19894 3.18263 3.23884 3.23505 
  3.18653 3.18255 3.19893 3.18263 3.23895 3.23518 
  6.53459 4.97457 3.46288 3.3192 0.668835 0.204602 
  3.18667 3.18263 3.19894 3.18263 3.23884 3.23505 
  3.18657 3.18258 3.19893 3.18263 3.23892 3.23515 
  3.18655 3.18257 3.19893 3.18263 3.23894 3.23516 
  3.18665 3.18262 3.19894 3.18263 3.23886 3.23507 
  3.18656 3.18257 3.19893 3.18263 3.23893 3.23515 
  3.18654 3.18256 3.19893 3.18263 3.23895 3.23517 
  3.18688 3.18274 3.19895 3.18264 3.23869 3.23487 
  3.18675 3.18267 3.19894 3.18264 3.23879 3.23498 
  3.18679 3.18269 3.19895 3.18264 3.23875 3.23494 
  3.1866 3.18259 3.19893 3.18263 3.2389 3.23512 
  3.18655 3.18256 3.19893 3.18263 3.23894 3.23517 
  3.18652 3.18255 3.19893 3.18263 3.23896 3.23519 
  3.18656 3.18257 3.19893 3.18263 3.23893 3.23515 
  3.18656 3.18257 3.19893 3.18263 3.23894 3.23516 
  3.18688 3.18274 3.19895 3.18264 3.23869 3.23487 
  3.18698 3.1828 3.19896 3.18265 3.23861 3.23477 
  </code></pre>
  
  <p>Mapping File:</p>
  
  <pre><code>0
  1
  2
  3
  4
  5
  </code></pre>