title: Caffe : train network accuracy = 1 constant ! Accuracy issue
body: <p>Right now, I am train network with with 2 class data... but accuracy is constant 1 after first iteration ! </p>
  
  <p>Input data is grayscale images. both class images are randomly selected when HDF5Data creation. </p>
  
  <p>Why is that happened ? What's wrong or where is mistake ! </p>
  
  <p>network.prototxt :</p>
  
  <pre><code>name: "brainMRI"
  layer {
    name: "data"
    type: "HDF5Data"
    top: "data"
    top: "label"
    include: {
      phase: TRAIN
    }
    hdf5_data_param {
      source: "/home/shivangpatel/caffe/brainMRI1/train_file_location.txt"
      batch_size: 10
    }
  }
  layer {
    name: "data"
    type: "HDF5Data"
    top: "data"
    top: "label"
    include: {
      phase: TEST
    }
    hdf5_data_param {
      source: "/home/shivangpatel/caffe/brainMRI1/test_file_location.txt"
      batch_size: 10
    }
  }
  
  layer {
    name: "conv1"
    type: "Convolution"
    bottom: "data"
    top: "conv1"
    param {
      lr_mult: 1
    }
    param {
      lr_mult: 2
    }
    convolution_param {
      num_output: 20
      kernel_size: 5
      stride: 1
      weight_filler {
        type: "xavier"
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "pool1"
    type: "Pooling"
    bottom: "conv1"
    top: "pool1"
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layer {
    name: "conv2"
    type: "Convolution"
    bottom: "pool1"
    top: "conv2"
    param {
      lr_mult: 1
    }
    param {
      lr_mult: 2
    }
    convolution_param {
      num_output: 50
      kernel_size: 5
      stride: 1
      weight_filler {
        type: "xavier"
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "pool2"
    type: "Pooling"
    bottom: "conv2"
    top: "pool2"
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layer {
    name: "ip1"
    type: "InnerProduct"
    bottom: "pool2"
    top: "ip1"
    param {
      lr_mult: 1
    }
    param {
      lr_mult: 2
    }
    inner_product_param {
      num_output: 500
      weight_filler {
        type: "xavier"
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu1"
    type: "ReLU"
    bottom: "ip1"
    top: "ip1"
  }
  layer {
    name: "ip2"
    type: "InnerProduct"
    bottom: "ip1"
    top: "ip2"
    param {
      lr_mult: 1
    }
    param {
      lr_mult: 2
    }
    inner_product_param {
      num_output: 2
      weight_filler {
        type: "xavier"
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "softmax"
    type: "Softmax"
    bottom: "ip2"
    top: "smip2"
  }
  
  layer {
    name: "loss"
    type: "SoftmaxWithLoss"
    bottom: "ip2"
    bottom: "label"
    top: "loss"
  }
  
  layer {
    name: "accuracy"
    type: "Accuracy"
    bottom: "smip2"
    bottom: "label"
    top: "accuracy"
    include {
      phase: TEST
    }
  }
  </code></pre>
  
  <p>Output : </p>
  
  <blockquote>
  <pre><code>I0217 17:41:07.912580  2913 net.cpp:270] This network produces output loss
  I0217 17:41:07.912607  2913 net.cpp:283] Network initialization done.
  I0217 17:41:07.912739  2913 solver.cpp:60] Solver scaffolding done.
  I0217 17:41:07.912789  2913 caffe.cpp:212] Starting Optimization
  I0217 17:41:07.912813  2913 solver.cpp:288] Solving brainMRI
  I0217 17:41:07.912832  2913 solver.cpp:289] Learning Rate Policy: inv
  I0217 17:41:07.920737  2913 solver.cpp:341] Iteration 0, Testing net (#0)
  I0217 17:41:08.235076  2913 solver.cpp:409]     Test net output #0: accuracy = 0.98
  I0217 17:41:08.235194  2913 solver.cpp:409]     Test net output #1: loss = 0.0560832 (* 1 = 0.0560832 loss)
  I0217 17:41:35.831647  2913 solver.cpp:341] Iteration 100, Testing net (#0)
  I0217 17:41:36.140849  2913 solver.cpp:409]     Test net output #0: accuracy = 1
  I0217 17:41:36.140949  2913 solver.cpp:409]     Test net output #1: loss = 0.00757247 (* 1 = 0.00757247 loss)
  I0217 17:42:05.465395  2913 solver.cpp:341] Iteration 200, Testing net (#0)
  I0217 17:42:05.775877  2913 solver.cpp:409]     Test net output #0: accuracy = 1
  I0217 17:42:05.776000  2913 solver.cpp:409]     Test net output #1: loss = 0.0144996 (* 1 = 0.0144996 loss)
  .............
  .............
  </code></pre>
  </blockquote>