something wrong with train/deploy prototxt files

[Ehab Albadawy]

I'm trying to build a network but something wrong is going with it, no matter how many iteration it has I always get the same output in testing

## train.prototxt

layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  transform_param {
    mirror: true
  }
  image_data_param {
    source: "/path/to/train.txt"
    batch_size: 1500
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool1"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv4"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool2"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "score"
  type: "InnerProduct"
  bottom: "fc7"
  top: "score"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 5
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "score"
  bottom: "label"
  top: "loss"
}
layer {
  name: "acc"
  type: "Accuracy"
  bottom: "score"
  bottom: "label"
  top: "acc"
}
layer {
  name: "probs"
  type: "Softmax"
  bottom: "score"
  top: "probs"
}

## deploy.prototxt

layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 33 dim: 33 } }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool1"
  top: "conv3"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv4"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool2"
  top: "fc6"
  inner_product_param {
    num_output: 256
  }
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  inner_product_param {
    num_output: 256
  }
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "score"
  type: "InnerProduct"
  bottom: "fc7"
  top: "score"
  inner_product_param {
    num_output: 5
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "score"
  top: "prob"
}

[Jonathan R. Williford]

Can you show the evolution of accuracy and loss during training?

Jonathan

--
You received this message because you are subscribed to the Google Groups "Caffe Users" group.
To unsubscribe from this group and stop receiving emails from it, send an email to caffe-users+unsubscribe@googlegroups.com.
To post to this group, send email to caffe...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/caffe-users/e6f0331d-6af2-4f02-b7ac-e153a8393055%40googlegroups.com.
For more options, visit https://groups.google.com/d/optout.

[Ehab Albadawy]

Here it is: http://pastie.org/10963692

To unsubscribe from this group and stop receiving emails from it, send an email to caffe-users...@googlegroups.com.

[Ehab Albadawy]

Problem is solved! I've found that I was doing something very silly, I gave it the classes with index 1 while it expects them with index 0 ! GOD it should have print an error msg or something instead of waisting the whole day trying to figuring out what's wrong!!!!

[Matt Marshall]

How did you obtain that history?  I've tried tee but I guess it doesn't work if training is interrupted with Ctrl+c.

On Wednesday, November 16, 2016 at 12:23:43 PM UTC-5, Ehab Albadawy wrote:

[Ehab Albadawy]

you can easily get it if you do the training for terminal (if i got you right) and it's OK if you interrupt the training, the history is still there.