title: Deploy error axis 2 out of range for alexnet
body: <p>I am using the Alexnet and try to deploy my network. But when I do so I get the following error:</p>
  
  <pre><code>I0109 15:16:56.645679  4240 net.cpp:100] Creating Layer fc6
  I0109 15:16:56.645681  4240 net.cpp:434] fc6 &lt;- pool5
  I0109 15:16:56.645684  4240 net.cpp:408] fc6 -&gt; fc6
  I0109 15:16:56.712829  4240 net.cpp:150] Setting up fc6
  I0109 15:16:56.712869  4240 net.cpp:157] Top shape: 1 4096 (4096)
  I0109 15:16:56.712873  4240 net.cpp:165] Memory required for data: 6778220
  I0109 15:16:56.712882  4240 layer_factory.hpp:77] Creating layer relu6
  I0109 15:16:56.712890  4240 net.cpp:100] Creating Layer relu6
  I0109 15:16:56.712893  4240 net.cpp:434] relu6 &lt;- fc6
  I0109 15:16:56.712915  4240 net.cpp:395] relu6 -&gt; fc6 (in-place)
  F0109 15:16:56.713158  4240 blob.hpp:122] Check failed: axis_index &lt; num_axes() (2 vs. 2) axis 2 out of range for 2-D Blob with shape 1 4096 (4096)
  *** Check failure stack trace: ***
  </code></pre>
  
  <p>I have no clue why. It has always been working for me and now this error occurs.</p>
  
  <p><strong>EDIT</strong></p>
  
  <pre><code>layer {
    name: "data"
    type: "Input"
    top: "data"
    input_param { shape: { dim: 1 dim: 3 dim: 227 dim: 227 } }
  }
  layer {
    name: "conv1"
    type: "Convolution"
    bottom: "data"
    top: "conv1"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    convolution_param {
      num_output: 96
      kernel_size: 11
      stride: 4
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0.0
      }
    }
  }
  layer {
    name: "relu1"
    type: "ReLU"
    bottom: "conv1"
    top: "conv1"
  }
  layer {
    name: "norm1"
    type: "LRN"
    bottom: "conv1"
    top: "norm1"
    lrn_param {
      local_size: 5
      alpha: 0.0001
      beta: 0.75
    }
  }
  layer {
    name: "pool1"
    type: "Pooling"
    bottom: "norm1"
    top: "pool1"
    pooling_param {
      pool: MAX
      kernel_size: 3
      stride: 2
    }
  }
  layer {
    name: "conv2"
    type: "Convolution"
    bottom: "pool1"
    top: "conv2"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    convolution_param {
      num_output: 256
      pad: 2
      kernel_size: 5
      group: 2
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0.1
      }
    }
  }
  layer {
    name: "relu2"
    type: "ReLU"
    bottom: "conv2"
    top: "conv2"
  }
  layer {
    name: "norm2"
    type: "LRN"
    bottom: "conv2"
    top: "norm2"
    lrn_param {
      local_size: 5
      alpha: 0.0001
      beta: 0.75
    }
  }
  layer {
    name: "pool2"
    type: "Pooling"
    bottom: "norm2"
    top: "pool2"
    pooling_param {
      pool: MAX
      kernel_size: 3
      stride: 2
    }
  }
  layer {
    name: "conv3"
    type: "Convolution"
    bottom: "pool2"
    top: "conv3"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    convolution_param {
      num_output: 384
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0.0
      }
    }
  }
  layer {
    name: "relu3"
    type: "ReLU"
    bottom: "conv3"
    top: "conv3"
  }
  layer {
    name: "conv4"
    type: "Convolution"
    bottom: "conv3"
    top: "conv4"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    convolution_param {
      num_output: 384
      pad: 1
      kernel_size: 3
      group: 2
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0.1
      }
    }
  }
  layer {
    name: "relu4"
    type: "ReLU"
    bottom: "conv4"
    top: "conv4"
  }
  layer {
    name: "conv5"
    type: "Convolution"
    bottom: "conv4"
    top: "conv5"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      group: 2
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0.1
      }
    }
  }
  layer {
    name: "relu5"
    type: "ReLU"
    bottom: "conv5"
    top: "conv5"
  }
  layer {
    name: "pool5"
    type: "Pooling"
    bottom: "conv5"
    top: "pool5"
    pooling_param {
      pool: MAX
      kernel_size: 3
      stride: 2
    }
  }
  layer {
    name: "fc6"
    type: "InnerProduct"
    bottom: "pool5"
    top: "fc6"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    inner_product_param {
      num_output: 4096
      weight_filler {
        type: "gaussian"
        std: 0.005
      }
      bias_filler {
        type: "constant"
        value: 0.1
      }
    }
  }
  layer {
    name: "relu6"
    type: "ReLU"
    bottom: "fc6"
    top: "fc6l"
  }
  
  layer {
    name: "drop6"
    type: "Dropout"
    bottom: "fc6l"
    top: "fc6d"
    dropout_param {
      dropout_ratio: 0.5
    }
  }
  layer {
    name: "fc7"
    type: "InnerProduct"
    bottom: "fc6d"
    top: "fc7"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    inner_product_param {
      num_output: 4096
      weight_filler {
        type: "gaussian"
        std: 0.005
      }
      bias_filler {
        type: "constant"
        value: 0.1
      }
    }
  }
  layer {
    name: "relu7"
    type: "ReLU"
    bottom: "fc7"
    top: "fc7"
  }
  layer {
    name: "drop7"
    type: "Dropout"
    bottom: "fc7"
    top: "fc7"
    dropout_param {
      dropout_ratio: 0.5
    }
  }
  layer {
    name: "fc8"
    type: "InnerProduct"
    bottom: "fc7"
    top: "fc8"
    param {
      lr_mult: 1.0
      decay_mult: 1.0
    }
    param {
      lr_mult: 2.0
      decay_mult: 0.0
    }
    inner_product_param {
      num_output: 612
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0.0
      }
    }
  }
  layer {
    name: "softmax"
    type: "Softmax"
    bottom: "fc8"
    top: "softmax"
  }
  </code></pre>
  
  <p>Above is my .prototxt (Should be the same as Alexnet)</p>
  
  <pre><code>Check failed: axis_index &lt; num_axes() (2 vs. 2) axis 2 out of range for 2-D Blob with shape 1 4096 (4096)
  *** Check failure stack trace: ***
      @     0x7f8b8e3125cd  google::LogMessage::Fail()
      @     0x7f8b8e314433  google::LogMessage::SendToLog()
      @     0x7f8b8e31215b  google::LogMessage::Flush()
      @     0x7f8b8e314e1e  google::LogMessageFatal::~LogMessageFatal()
      @     0x7f8b8e92c86a  caffe::Blob&lt;&gt;::CanonicalAxisIndex()
      @     0x7f8b8eaa09c2  caffe::CuDNNReLULayer&lt;&gt;::Reshape()
      @     0x7f8b8e97f481  caffe::Net&lt;&gt;::Init()
      @     0x7f8b8e980d01  caffe::Net&lt;&gt;::Net()
      @     0x7f8b8eac7c5a  caffe::Solver&lt;&gt;::InitTrainNet()
      @     0x7f8b8eac8fc7  caffe::Solver&lt;&gt;::Init()
      @     0x7f8b8eac936a  caffe::Solver&lt;&gt;::Solver()
      @     0x7f8b8e960c53  caffe::Creator_SGDSolver&lt;&gt;()
      @           0x40ac89  train()
      @           0x407590  main
      @     0x7f8b8d283830  __libc_start_main
      @           0x407db9  _start
      @              (nil)  (unknown)
  Aborted (core dumped)
  </code></pre>
  
  <p><strong>Example 2:</strong></p>
  
  <pre><code>I0228 13:03:22.875816  4395 layer_factory.hpp:77] Creating layer relu6
  I0228 13:03:22.875828  4395 net.cpp:100] Creating Layer relu6
  I0228 13:03:22.875831  4395 net.cpp:434] relu6 &lt;- fc-main
  I0228 13:03:22.875855  4395 net.cpp:395] relu6 -&gt; fc-main (in-place)
  F0228 13:03:22.876565  4395 blob.hpp:122] Check failed: axis_index &lt; num_axes() (2 vs. 2) axis 2 out of range for 2-D Blob with shape 4 4096 (16384)
  *** Check failure stack trace: ***
      @     0x7fe1271d85cd  google::LogMessage::Fail()
      @     0x7fe1271da433  google::LogMessage::SendToLog()
      @     0x7fe1271d815b  google::LogMessage::Flush()
      @     0x7fe1271dae1e  google::LogMessageFatal::~LogMessageFatal()
      @     0x7fe1277f286a  caffe::Blob&lt;&gt;::CanonicalAxisIndex()
      @     0x7fe1279669c2  caffe::CuDNNReLULayer&lt;&gt;::Reshape()
      @     0x7fe127845481  caffe::Net&lt;&gt;::Init()
      @     0x7fe127846d01  caffe::Net&lt;&gt;::Net()
      @     0x7fe12798dc5a  caffe::Solver&lt;&gt;::InitTrainNet()
      @     0x7fe12798efc7  caffe::Solver&lt;&gt;::Init()
      @     0x7fe12798f36a  caffe::Solver&lt;&gt;::Solver()
      @     0x7fe127826c53  caffe::Creator_SGDSolver&lt;&gt;()
      @           0x40ac89  train()
      @           0x407590  main
      @     0x7fe126149830  __libc_start_main
      @           0x407db9  _start
      @              (nil)  (unknown)
  Aborted (core dumped)
  
  
  layer {
    name: "fc-main"
    type: "InnerProduct"
    bottom: "pool5"
    top: "fc-main"
    param {
      decay_mult: 1
    }
    param {
      decay_mult: 0
    }
    inner_product_param {
      num_output: 4096
      weight_filler {
        type: "xavier"
        std: 0.005
      }
      bias_filler {
        type: "constant"
        value: 0
      }
    }
  }
  layer {
    name: "relu6"
    type: "ReLU"
    bottom: "fc-main"
    top: "fc-main"
  }
  layer {
    name: "drop6"
   type: "Dropout"
    bottom: "fc-main"
    top: "fc-main"
    dropout_param {
      dropout_ratio: 0.5
    }
  }
  </code></pre>