how to change the AlexNet into FCNs ?

How to change the AlexNet into FCNs ?

FCNs is a network that only contain convolution layers and no fc layer at all. It's structure can be shown as the following figures:

This image from the paper : <Fully Convolutional Networks for Semantic Segmentation>  CVPR 2015.

　　It could locate the location of object target perfectly as shown in above images and it doesn't need to resize the resolution of input images, which is the mostly different from traditional CNNs. First, Let's review some related network parameters about AlexNet, related structure can be shown as following:

　　As we can see from the above figure, the input of images must be resized into a fixed resolution, like 224*224, due to the existance of fc_layer. The specific pipeline could be found in this blog, web link: http://blog.csdn.net/sunbaigui/article/details/39938097

　　The output of Conv 5 is: 6*6*256, we want to obtain the final results: 1*1*1000 (take the 1k classes for an example). How could we use the middle Conv 6, Conv 7, Conv 8 layers to bridge the two results ? Do we need the pool layers added ? How to set the middle parameters in each layers ? Does it really work ?

　　Let's do it now. We just add 3 Convolution layers for an example. The function used for change the width*height*channel (actually, it only about the width, due to width == height, and the channel only related to the output of each layer.) is :

(W- F + 2P)/S + 1

where W denotes the width of images from bottom layer, F denotes the size of Convolution filter, P means the padding you want to add, this mainly contribute to the same resolution of input and output, S denotes the stride.

　　Thus, the following layers needed to add to the prototxt files:

　　　　from: 6*6*256 --->  3*3*4096 ---> 1*1*4096 ---> 1*1*43 (take my experiments for an example.)

####################################################################
## the output of Pool 5 is **
####################################################################
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    # group:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}

layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv6"
  top: "conv7"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    # group:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}

layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    # group:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}

　　Then, make your caffe file and waiting for something amazing happens...

　　Actually, at first, I always run a wrong result, i.e. 2*2*43 ... It really confused me, the function is wrong ? It does not make scene. Because it really worked at the begining of the Network. Lastly, I found I make a stupid mistake, due to I add the Conv 6 from Conv 5, not Pool 5. Thus, it is really important for us to be careful and more careful.

　　Ok, the all pipeline has done, and due to my ACER lap-top only have a GTX960M, it warning me out of memory. The results running on the terminal are here :

I0423 ::24.421512   caffe.cpp:] Using GPUs
I0423 ::24.431041   caffe.cpp:] GPU : GeForce GTX 960M
I0423 ::24.565281   solver.cpp:] Initializing solver from parameters:
test_iter:
test_interval:
base_lr: 0.001
display:
max_iter:
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize:
snapshot:
snapshot_prefix: "/media/wangxiao/Acer/caffe_models_/"
solver_mode: GPU
device_id:
net: "/home/wangxiao/Downloads/fcn-caffe-master/wangxiao/train_val.prototxt"
test_initialization: false
I0423 ::24.621829   solver.cpp:] Creating training net from net file: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/train_val.prototxt
I0423 ::24.622601   net.cpp:] The NetState phase () differed from the phase () specified by a rule in layer data
I0423 ::24.622632   net.cpp:] The NetState phase () differed from the phase () specified by a rule in layer accuracy
I0423 ::24.622828   net.cpp:] Initializing net from parameters:
name: "AlexNet"
state {
  phase: TRAIN
}
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: false
  }
  image_data_param {
    source: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/train_data/newAdd_attribute_label.txt"
    batch_size:
    root_folder: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/train_data/227_227_images/"
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value:
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size:
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size:
    stride:
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    group:
    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:
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size:
    stride:
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value:
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    group:
    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:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    group:
    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:
    stride:
  }
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv6"
  top: "conv7"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "sigmoid"
  type: "Sigmoid"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "conv8"
  bottom: "label"
  top: "loss"
}
I0423 ::24.622962   layer_factory.hpp:] Creating layer data
I0423 ::24.623002   net.cpp:] Creating Layer data
I0423 ::24.623009   net.cpp:] data -> data
I0423 ::24.623034   net.cpp:] data -> label
I0423 ::24.623051   image_data_layer.cpp:] Opening file /media/wangxiao/247317a3-e6b5-45d4-81d1-/---------------/new_born_data/train_data/newAdd_attribute_label.txt
I0423 ::25.535037   image_data_layer.cpp:] A total of  images.
I0423 ::25.543112   image_data_layer.cpp:] output data size: ,,,
I0423 ::25.554397   net.cpp:] Setting up data
I0423 ::25.554425   net.cpp:] Top shape:     ()
I0423 ::25.554431   net.cpp:] Top shape:     ()
I0423 ::25.554435   net.cpp:] Memory required for data:
I0423 ::25.554451   layer_factory.hpp:] Creating layer conv1
I0423 ::25.554476   net.cpp:] Creating Layer conv1
I0423 ::25.554481   net.cpp:] conv1 <- data
I0423 ::25.554492   net.cpp:] conv1 -> conv1
I0423 ::25.556519   net.cpp:] Setting up conv1
I0423 ::25.556534   net.cpp:] Top shape:     ()
I0423 ::25.556537   net.cpp:] Memory required for data:
I0423 ::25.556556   layer_factory.hpp:] Creating layer relu1
I0423 ::25.556565   net.cpp:] Creating Layer relu1
I0423 ::25.556568   net.cpp:] relu1 <- conv1
I0423 ::25.556573   net.cpp:] relu1 -> conv1 (in-place)
I0423 ::25.556583   net.cpp:] Setting up relu1
I0423 ::25.556587   net.cpp:] Top shape:     ()
I0423 ::25.556591   net.cpp:] Memory required for data:
I0423 ::25.556594   layer_factory.hpp:] Creating layer norm1
I0423 ::25.556602   net.cpp:] Creating Layer norm1
I0423 ::25.556604   net.cpp:] norm1 <- conv1
I0423 ::25.556609   net.cpp:] norm1 -> norm1
I0423 ::25.556646   net.cpp:] Setting up norm1
I0423 ::25.556653   net.cpp:] Top shape:     ()
I0423 ::25.556689   net.cpp:] Memory required for data:
I0423 ::25.556692   layer_factory.hpp:] Creating layer pool1
I0423 ::25.556700   net.cpp:] Creating Layer pool1
I0423 ::25.556704   net.cpp:] pool1 <- norm1
I0423 ::25.556710   net.cpp:] pool1 -> pool1
I0423 ::25.556749   net.cpp:] Setting up pool1
I0423 ::25.556766   net.cpp:] Top shape:     ()
I0423 ::25.556769   net.cpp:] Memory required for data:
I0423 ::25.556772   layer_factory.hpp:] Creating layer conv2
I0423 ::25.556792   net.cpp:] Creating Layer conv2
I0423 ::25.556795   net.cpp:] conv2 <- pool1
I0423 ::25.556802   net.cpp:] conv2 -> conv2
I0423 ::25.565610   net.cpp:] Setting up conv2
I0423 ::25.565634   net.cpp:] Top shape:     ()
I0423 ::25.565637   net.cpp:] Memory required for data:
I0423 ::25.565651   layer_factory.hpp:] Creating layer relu2
I0423 ::25.565660   net.cpp:] Creating Layer relu2
I0423 ::25.565665   net.cpp:] relu2 <- conv2
I0423 ::25.565672   net.cpp:] relu2 -> conv2 (in-place)
I0423 ::25.565681   net.cpp:] Setting up relu2
I0423 ::25.565686   net.cpp:] Top shape:     ()
I0423 ::25.565690   net.cpp:] Memory required for data:
I0423 ::25.565692   layer_factory.hpp:] Creating layer norm2
I0423 ::25.565699   net.cpp:] Creating Layer norm2
I0423 ::25.565702   net.cpp:] norm2 <- conv2
I0423 ::25.565708   net.cpp:] norm2 -> norm2
I0423 ::25.565742   net.cpp:] Setting up norm2
I0423 ::25.565747   net.cpp:] Top shape:     ()
I0423 ::25.565750   net.cpp:] Memory required for data:
I0423 ::25.565753   layer_factory.hpp:] Creating layer pool2
I0423 ::25.565762   net.cpp:] Creating Layer pool2
I0423 ::25.565764   net.cpp:] pool2 <- norm2
I0423 ::25.565769   net.cpp:] pool2 -> pool2
I0423 ::25.565798   net.cpp:] Setting up pool2
I0423 ::25.565804   net.cpp:] Top shape:     ()
I0423 ::25.565809   net.cpp:] Memory required for data:
I0423 ::25.565811   layer_factory.hpp:] Creating layer conv3
I0423 ::25.565821   net.cpp:] Creating Layer conv3
I0423 ::25.565824   net.cpp:] conv3 <- pool2
I0423 ::25.565831   net.cpp:] conv3 -> conv3
I0423 ::25.590066   net.cpp:] Setting up conv3
I0423 ::25.590090   net.cpp:] Top shape:     ()
I0423 ::25.590092   net.cpp:] Memory required for data:
I0423 ::25.590116   layer_factory.hpp:] Creating layer relu3
I0423 ::25.590126   net.cpp:] Creating Layer relu3
I0423 ::25.590131   net.cpp:] relu3 <- conv3
I0423 ::25.590137   net.cpp:] relu3 -> conv3 (in-place)
I0423 ::25.590145   net.cpp:] Setting up relu3
I0423 ::25.590149   net.cpp:] Top shape:     ()
I0423 ::25.590152   net.cpp:] Memory required for data:
I0423 ::25.590155   layer_factory.hpp:] Creating layer conv4
I0423 ::25.590167   net.cpp:] Creating Layer conv4
I0423 ::25.590169   net.cpp:] conv4 <- conv3
I0423 ::25.590176   net.cpp:] conv4 -> conv4
I0423 ::25.608953   net.cpp:] Setting up conv4
I0423 ::25.608975   net.cpp:] Top shape:     ()
I0423 ::25.608979   net.cpp:] Memory required for data:
I0423 ::25.608989   layer_factory.hpp:] Creating layer relu4
I0423 ::25.609007   net.cpp:] Creating Layer relu4
I0423 ::25.609011   net.cpp:] relu4 <- conv4
I0423 ::25.609019   net.cpp:] relu4 -> conv4 (in-place)
I0423 ::25.609027   net.cpp:] Setting up relu4
I0423 ::25.609031   net.cpp:] Top shape:     ()
I0423 ::25.609047   net.cpp:] Memory required for data:
I0423 ::25.609050   layer_factory.hpp:] Creating layer conv5
I0423 ::25.609061   net.cpp:] Creating Layer conv5
I0423 ::25.609066   net.cpp:] conv5 <- conv4
I0423 ::25.609071   net.cpp:] conv5 -> conv5
I0423 ::25.621208   net.cpp:] Setting up conv5
I0423 ::25.621229   net.cpp:] Top shape:     ()
I0423 ::25.621233   net.cpp:] Memory required for data:
I0423 ::25.621258   layer_factory.hpp:] Creating layer relu5
I0423 ::25.621268   net.cpp:] Creating Layer relu5
I0423 ::25.621273   net.cpp:] relu5 <- conv5
I0423 ::25.621279   net.cpp:] relu5 -> conv5 (in-place)
I0423 ::25.621286   net.cpp:] Setting up relu5
I0423 ::25.621290   net.cpp:] Top shape:     ()
I0423 ::25.621294   net.cpp:] Memory required for data:
I0423 ::25.621297   layer_factory.hpp:] Creating layer pool5
I0423 ::25.621304   net.cpp:] Creating Layer pool5
I0423 ::25.621306   net.cpp:] pool5 <- conv5
I0423 ::25.621314   net.cpp:] pool5 -> pool5
I0423 ::25.621347   net.cpp:] Setting up pool5
I0423 ::25.621354   net.cpp:] Top shape:     ()
I0423 ::25.621357   net.cpp:] Memory required for data:
I0423 ::25.621361   layer_factory.hpp:] Creating layer conv6
I0423 ::25.621373   net.cpp:] Creating Layer conv6
I0423 ::25.621377   net.cpp:] conv6 <- pool5
I0423 ::25.621384   net.cpp:] conv6 -> conv6
I0423 ::25.731640   net.cpp:] Setting up conv6
I0423 ::25.731675   net.cpp:] Top shape:     ()
I0423 ::25.731679   net.cpp:] Memory required for data:
I0423 ::25.731688   layer_factory.hpp:] Creating layer relu6
I0423 ::25.731709   net.cpp:] Creating Layer relu6
I0423 ::25.731714   net.cpp:] relu6 <- conv6
I0423 ::25.731721   net.cpp:] relu6 -> conv6 (in-place)
I0423 ::25.731731   net.cpp:] Setting up relu6
I0423 ::25.731735   net.cpp:] Top shape:     ()
I0423 ::25.731739   net.cpp:] Memory required for data:
I0423 ::25.731741   layer_factory.hpp:] Creating layer conv7
I0423 ::25.731752   net.cpp:] Creating Layer conv7
I0423 ::25.731757   net.cpp:] conv7 <- conv6
I0423 ::25.731765   net.cpp:] conv7 -> conv7
I0423 ::29.661667   net.cpp:] Setting up conv7
I0423 ::29.661705   net.cpp:] Top shape:     ()
I0423 ::29.661710   net.cpp:] Memory required for data:
I0423 ::29.661720   layer_factory.hpp:] Creating layer relu7
I0423 ::29.661741   net.cpp:] Creating Layer relu7
I0423 ::29.661746   net.cpp:] relu7 <- conv7
I0423 ::29.661752   net.cpp:] relu7 -> conv7 (in-place)
I0423 ::29.661761   net.cpp:] Setting up relu7
I0423 ::29.661767   net.cpp:] Top shape:     ()
I0423 ::29.661769   net.cpp:] Memory required for data:
I0423 ::29.661772   layer_factory.hpp:] Creating layer conv8
I0423 ::29.661783   net.cpp:] Creating Layer conv8
I0423 ::29.661788   net.cpp:] conv8 <- conv7
I0423 ::29.661795   net.cpp:] conv8 -> conv8
I0423 ::29.666793   net.cpp:] Setting up conv8
I0423 ::29.666815   net.cpp:] Top shape:     ()
I0423 ::29.666818   net.cpp:] Memory required for data:
I0423 ::29.666826   layer_factory.hpp:] Creating layer relu8
I0423 ::29.666841   net.cpp:] Creating Layer relu8
I0423 ::29.666844   net.cpp:] relu8 <- conv8
I0423 ::29.666849   net.cpp:] relu8 -> conv8 (in-place)
I0423 ::29.666856   net.cpp:] Setting up relu8
I0423 ::29.666860   net.cpp:] Top shape:     ()
I0423 ::29.666877   net.cpp:] Memory required for data:
I0423 ::29.666882   layer_factory.hpp:] Creating layer sigmoid
I0423 ::29.666888   net.cpp:] Creating Layer sigmoid
I0423 ::29.666892   net.cpp:] sigmoid <- conv8
I0423 ::29.666895   net.cpp:] sigmoid -> conv8 (in-place)
I0423 ::29.666901   net.cpp:] Setting up sigmoid
I0423 ::29.666905   net.cpp:] Top shape:     ()
I0423 ::29.666908   net.cpp:] Memory required for data:
I0423 ::29.666911   layer_factory.hpp:] Creating layer loss
I0423 ::29.666918   net.cpp:] Creating Layer loss
I0423 ::29.666920   net.cpp:] loss <- conv8
I0423 ::29.666924   net.cpp:] loss <- label
I0423 ::29.666931   net.cpp:] loss -> loss
I0423 ::29.666975   net.cpp:] Setting up loss
I0423 ::29.666990   net.cpp:] Top shape: ()
I0423 ::29.666992   net.cpp:]     with loss weight
I0423 ::29.667017   net.cpp:] Memory required for data:
I0423 ::29.667031   net.cpp:] loss needs backward computation.
I0423 ::29.667034   net.cpp:] sigmoid needs backward computation.
I0423 ::29.667038   net.cpp:] relu8 needs backward computation.
I0423 ::29.667040   net.cpp:] conv8 needs backward computation.
I0423 ::29.667043   net.cpp:] relu7 needs backward computation.
I0423 ::29.667047   net.cpp:] conv7 needs backward computation.
I0423 ::29.667050   net.cpp:] relu6 needs backward computation.
I0423 ::29.667053   net.cpp:] conv6 needs backward computation.
I0423 ::29.667057   net.cpp:] pool5 needs backward computation.
I0423 ::29.667060   net.cpp:] relu5 needs backward computation.
I0423 ::29.667063   net.cpp:] conv5 needs backward computation.
I0423 ::29.667068   net.cpp:] relu4 needs backward computation.
I0423 ::29.667070   net.cpp:] conv4 needs backward computation.
I0423 ::29.667073   net.cpp:] relu3 needs backward computation.
I0423 ::29.667076   net.cpp:] conv3 needs backward computation.
I0423 ::29.667080   net.cpp:] pool2 needs backward computation.
I0423 ::29.667084   net.cpp:] norm2 needs backward computation.
I0423 ::29.667088   net.cpp:] relu2 needs backward computation.
I0423 ::29.667091   net.cpp:] conv2 needs backward computation.
I0423 ::29.667094   net.cpp:] pool1 needs backward computation.
I0423 ::29.667098   net.cpp:] norm1 needs backward computation.
I0423 ::29.667101   net.cpp:] relu1 needs backward computation.
I0423 ::29.667104   net.cpp:] conv1 needs backward computation.
I0423 ::29.667109   net.cpp:] data does not need backward computation.
I0423 ::29.667111   net.cpp:] This network produces output loss
I0423 ::29.667127   net.cpp:] Network initialization done.
I0423 ::29.667804   solver.cpp:] Creating test net (#) specified by net file: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/train_val.prototxt
I0423 ::29.667937   net.cpp:] The NetState phase () differed from the phase () specified by a rule in layer data
I0423 ::29.668148   net.cpp:] Initializing net from parameters:
name: "AlexNet"
state {
  phase: TEST
}
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
  }
  image_data_param {
    source: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/test_data/newAdd_attribute_label_test.txt"
    batch_size:
    root_folder: "/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/---------------/new_born_data/test_data/227_227_test_images/"
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value:
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size:
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size:
    stride:
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    group:
    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:
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size:
    stride:
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value:
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    group:
    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:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    pad:
    kernel_size:
    group:
    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:
    stride:
  }
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv6"
  top: "conv7"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  param {
    lr_mult:
    decay_mult:
  }
  param {
    lr_mult:
    decay_mult:
  }
  convolution_param {
    num_output:
    kernel_size:
    stride:
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "sigmoid"
  type: "Sigmoid"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "conv8"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "conv8"
  bottom: "label"
  top: "loss"
}
I0423 ::29.668323   layer_factory.hpp:] Creating layer data
I0423 ::29.668349   net.cpp:] Creating Layer data
I0423 ::29.668355   net.cpp:] data -> data
I0423 ::29.668373   net.cpp:] data -> label
I0423 ::29.668382   image_data_layer.cpp:] Opening file /media/wangxiao/247317a3-e6b5-45d4-81d1-/---------------/new_born_data/test_data/newAdd_attribute_label_test.txt
I0423 ::29.696005   image_data_layer.cpp:] A total of  images.
I0423 ::29.697830   image_data_layer.cpp:] output data size: ,,,
I0423 ::29.699980   net.cpp:] Setting up data
I0423 ::29.700013   net.cpp:] Top shape:     ()
I0423 ::29.700019   net.cpp:] Top shape:     ()
I0423 ::29.700022   net.cpp:] Memory required for data:
I0423 ::29.700028   layer_factory.hpp:] Creating layer label_data_1_split
I0423 ::29.700040   net.cpp:] Creating Layer label_data_1_split
I0423 ::29.700048   net.cpp:] label_data_1_split <- label
I0423 ::29.700060   net.cpp:] label_data_1_split -> label_data_1_split_0
I0423 ::29.700075   net.cpp:] label_data_1_split -> label_data_1_split_1
I0423 ::29.700141   net.cpp:] Setting up label_data_1_split
I0423 ::29.700151   net.cpp:] Top shape:     ()
I0423 ::29.700160   net.cpp:] Top shape:     ()
I0423 ::29.700176   net.cpp:] Memory required for data:
I0423 ::29.700181   layer_factory.hpp:] Creating layer conv1
I0423 ::29.700196   net.cpp:] Creating Layer conv1
I0423 ::29.700199   net.cpp:] conv1 <- data
I0423 ::29.700206   net.cpp:] conv1 -> conv1
I0423 ::29.701347   net.cpp:] Setting up conv1
I0423 ::29.701369   net.cpp:] Top shape:     ()
I0423 ::29.701372   net.cpp:] Memory required for data:
I0423 ::29.701383   layer_factory.hpp:] Creating layer relu1
I0423 ::29.701390   net.cpp:] Creating Layer relu1
I0423 ::29.701395   net.cpp:] relu1 <- conv1
I0423 ::29.701400   net.cpp:] relu1 -> conv1 (in-place)
I0423 ::29.701406   net.cpp:] Setting up relu1
I0423 ::29.701412   net.cpp:] Top shape:     ()
I0423 ::29.701416   net.cpp:] Memory required for data:
I0423 ::29.701418   layer_factory.hpp:] Creating layer norm1
I0423 ::29.701426   net.cpp:] Creating Layer norm1
I0423 ::29.701429   net.cpp:] norm1 <- conv1
I0423 ::29.701434   net.cpp:] norm1 -> norm1
I0423 ::29.701464   net.cpp:] Setting up norm1
I0423 ::29.701479   net.cpp:] Top shape:     ()
I0423 ::29.701483   net.cpp:] Memory required for data:
I0423 ::29.701486   layer_factory.hpp:] Creating layer pool1
I0423 ::29.701503   net.cpp:] Creating Layer pool1
I0423 ::29.701505   net.cpp:] pool1 <- norm1
I0423 ::29.701510   net.cpp:] pool1 -> pool1
I0423 ::29.701537   net.cpp:] Setting up pool1
I0423 ::29.701544   net.cpp:] Top shape:     ()
I0423 ::29.701545   net.cpp:] Memory required for data:
I0423 ::29.701550   layer_factory.hpp:] Creating layer conv2
I0423 ::29.701557   net.cpp:] Creating Layer conv2
I0423 ::29.701561   net.cpp:] conv2 <- pool1
I0423 ::29.701566   net.cpp:] conv2 -> conv2
I0423 ::29.709951   net.cpp:] Setting up conv2
I0423 ::29.709987   net.cpp:] Top shape:     ()
I0423 ::29.709992   net.cpp:] Memory required for data:
I0423 ::29.710005   layer_factory.hpp:] Creating layer relu2
I0423 ::29.710014   net.cpp:] Creating Layer relu2
I0423 ::29.710018   net.cpp:] relu2 <- conv2
I0423 ::29.710026   net.cpp:] relu2 -> conv2 (in-place)
I0423 ::29.710033   net.cpp:] Setting up relu2
I0423 ::29.710039   net.cpp:] Top shape:     ()
I0423 ::29.710042   net.cpp:] Memory required for data:
I0423 ::29.710046   layer_factory.hpp:] Creating layer norm2
I0423 ::29.710057   net.cpp:] Creating Layer norm2
I0423 ::29.710060   net.cpp:] norm2 <- conv2
I0423 ::29.710067   net.cpp:] norm2 -> norm2
I0423 ::29.710100   net.cpp:] Setting up norm2
I0423 ::29.710108   net.cpp:] Top shape:     ()
I0423 ::29.710110   net.cpp:] Memory required for data:
I0423 ::29.710114   layer_factory.hpp:] Creating layer pool2
I0423 ::29.710120   net.cpp:] Creating Layer pool2
I0423 ::29.710124   net.cpp:] pool2 <- norm2
I0423 ::29.710129   net.cpp:] pool2 -> pool2
I0423 ::29.710155   net.cpp:] Setting up pool2
I0423 ::29.710171   net.cpp:] Top shape:     ()
I0423 ::29.710175   net.cpp:] Memory required for data:
I0423 ::29.710187   layer_factory.hpp:] Creating layer conv3
I0423 ::29.710197   net.cpp:] Creating Layer conv3
I0423 ::29.710201   net.cpp:] conv3 <- pool2
I0423 ::29.710207   net.cpp:] conv3 -> conv3
I0423 ::29.733366   net.cpp:] Setting up conv3
I0423 ::29.733403   net.cpp:] Top shape:     ()
I0423 ::29.733407   net.cpp:] Memory required for data:
I0423 ::29.733420   layer_factory.hpp:] Creating layer relu3
I0423 ::29.733439   net.cpp:] Creating Layer relu3
I0423 ::29.733444   net.cpp:] relu3 <- conv3
I0423 ::29.733453   net.cpp:] relu3 -> conv3 (in-place)
I0423 ::29.733461   net.cpp:] Setting up relu3
I0423 ::29.733466   net.cpp:] Top shape:     ()
I0423 ::29.733469   net.cpp:] Memory required for data:
I0423 ::29.733472   layer_factory.hpp:] Creating layer conv4
I0423 ::29.733484   net.cpp:] Creating Layer conv4
I0423 ::29.733489   net.cpp:] conv4 <- conv3
I0423 ::29.733494   net.cpp:] conv4 -> conv4
I0423 ::29.750310   net.cpp:] Setting up conv4
I0423 ::29.750344   net.cpp:] Top shape:     ()
I0423 ::29.750349   net.cpp:] Memory required for data:
I0423 ::29.750357   layer_factory.hpp:] Creating layer relu4
I0423 ::29.750366   net.cpp:] Creating Layer relu4
I0423 ::29.750370   net.cpp:] relu4 <- conv4
I0423 ::29.750376   net.cpp:] relu4 -> conv4 (in-place)
I0423 ::29.750393   net.cpp:] Setting up relu4
I0423 ::29.750397   net.cpp:] Top shape:     ()
I0423 ::29.750401   net.cpp:] Memory required for data:
I0423 ::29.750403   layer_factory.hpp:] Creating layer conv5
I0423 ::29.750414   net.cpp:] Creating Layer conv5
I0423 ::29.750418   net.cpp:] conv5 <- conv4
I0423 ::29.750423   net.cpp:] conv5 -> conv5
I0423 ::29.762544   net.cpp:] Setting up conv5
I0423 ::29.762580   net.cpp:] Top shape:     ()
I0423 ::29.762584   net.cpp:] Memory required for data:
I0423 ::29.762598   layer_factory.hpp:] Creating layer relu5
I0423 ::29.762609   net.cpp:] Creating Layer relu5
I0423 ::29.762614   net.cpp:] relu5 <- conv5
I0423 ::29.762619   net.cpp:] relu5 -> conv5 (in-place)
I0423 ::29.762629   net.cpp:] Setting up relu5
I0423 ::29.762646   net.cpp:] Top shape:     ()
I0423 ::29.762650   net.cpp:] Memory required for data:
I0423 ::29.762653   layer_factory.hpp:] Creating layer pool5
I0423 ::29.762662   net.cpp:] Creating Layer pool5
I0423 ::29.762665   net.cpp:] pool5 <- conv5
I0423 ::29.762671   net.cpp:] pool5 -> pool5
I0423 ::29.762707   net.cpp:] Setting up pool5
I0423 ::29.762724   net.cpp:] Top shape:     ()
I0423 ::29.762727   net.cpp:] Memory required for data:
I0423 ::29.762740   layer_factory.hpp:] Creating layer conv6
I0423 ::29.762753   net.cpp:] Creating Layer conv6
I0423 ::29.762755   net.cpp:] conv6 <- pool5
I0423 ::29.762761   net.cpp:] conv6 -> conv6
I0423 ::29.868270   net.cpp:] Setting up conv6
I0423 ::29.868306   net.cpp:] Top shape:     ()
I0423 ::29.868311   net.cpp:] Memory required for data:
I0423 ::29.868320   layer_factory.hpp:] Creating layer relu6
I0423 ::29.868330   net.cpp:] Creating Layer relu6
I0423 ::29.868335   net.cpp:] relu6 <- conv6
I0423 ::29.868342   net.cpp:] relu6 -> conv6 (in-place)
I0423 ::29.868350   net.cpp:] Setting up relu6
I0423 ::29.868355   net.cpp:] Top shape:     ()
I0423 ::29.868358   net.cpp:] Memory required for data:
I0423 ::29.868361   layer_factory.hpp:] Creating layer conv7
I0423 ::29.868372   net.cpp:] Creating Layer conv7
I0423 ::29.868376   net.cpp:] conv7 <- conv6
I0423 ::29.868381   net.cpp:] conv7 -> conv7
I0423 ::33.773138   net.cpp:] Setting up conv7
I0423 ::33.773177   net.cpp:] Top shape:     ()
I0423 ::33.773182   net.cpp:] Memory required for data:
I0423 ::33.773192   layer_factory.hpp:] Creating layer relu7
I0423 ::33.773203   net.cpp:] Creating Layer relu7
I0423 ::33.773219   net.cpp:] relu7 <- conv7
I0423 ::33.773232   net.cpp:] relu7 -> conv7 (in-place)
I0423 ::33.773247   net.cpp:] Setting up relu7
I0423 ::33.773257   net.cpp:] Top shape:     ()
I0423 ::33.773265   net.cpp:] Memory required for data:
I0423 ::33.773269   layer_factory.hpp:] Creating layer conv8
I0423 ::33.773283   net.cpp:] Creating Layer conv8
I0423 ::33.773286   net.cpp:] conv8 <- conv7
I0423 ::33.773293   net.cpp:] conv8 -> conv8
I0423 ::33.778169   net.cpp:] Setting up conv8
I0423 ::33.778193   net.cpp:] Top shape:     ()
I0423 ::33.778198   net.cpp:] Memory required for data:
I0423 ::33.778203   layer_factory.hpp:] Creating layer relu8
I0423 ::33.778221   net.cpp:] Creating Layer relu8
I0423 ::33.778226   net.cpp:] relu8 <- conv8
I0423 ::33.778233   net.cpp:] relu8 -> conv8 (in-place)
I0423 ::33.778239   net.cpp:] Setting up relu8
I0423 ::33.778244   net.cpp:] Top shape:     ()
I0423 ::33.778246   net.cpp:] Memory required for data:
I0423 ::33.778249   layer_factory.hpp:] Creating layer sigmoid
I0423 ::33.778255   net.cpp:] Creating Layer sigmoid
I0423 ::33.778260   net.cpp:] sigmoid <- conv8
I0423 ::33.778265   net.cpp:] sigmoid -> conv8 (in-place)
I0423 ::33.778270   net.cpp:] Setting up sigmoid
I0423 ::33.778275   net.cpp:] Top shape:     ()
I0423 ::33.778277   net.cpp:] Memory required for data:
I0423 ::33.778295   layer_factory.hpp:] Creating layer conv8_sigmoid_0_split
I0423 ::33.778301   net.cpp:] Creating Layer conv8_sigmoid_0_split
I0423 ::33.778303   net.cpp:] conv8_sigmoid_0_split <- conv8
I0423 ::33.778318   net.cpp:] conv8_sigmoid_0_split -> conv8_sigmoid_0_split_0
I0423 ::33.778339   net.cpp:] conv8_sigmoid_0_split -> conv8_sigmoid_0_split_1
I0423 ::33.778373   net.cpp:] Setting up conv8_sigmoid_0_split
I0423 ::33.778389   net.cpp:] Top shape:     ()
I0423 ::33.778393   net.cpp:] Top shape:     ()
I0423 ::33.778408   net.cpp:] Memory required for data:
I0423 ::33.778411   layer_factory.hpp:] Creating layer accuracy
I0423 ::33.778419   net.cpp:] Creating Layer accuracy
I0423 ::33.778422   net.cpp:] accuracy <- conv8_sigmoid_0_split_0
I0423 ::33.778426   net.cpp:] accuracy <- label_data_1_split_0
I0423 ::33.778432   net.cpp:] accuracy -> accuracy
I0423 ::33.778439   net.cpp:] Setting up accuracy
I0423 ::33.778446   net.cpp:] Top shape: ()
I0423 ::33.778452   net.cpp:] Memory required for data:
I0423 ::33.778457   layer_factory.hpp:] Creating layer loss
I0423 ::33.778477   net.cpp:] Creating Layer loss
I0423 ::33.778496   net.cpp:] loss <- conv8_sigmoid_0_split_1
I0423 ::33.778503   net.cpp:] loss <- label_data_1_split_1
I0423 ::33.778513   net.cpp:] loss -> loss
I0423 ::33.778563   net.cpp:] Setting up loss
I0423 ::33.778573   net.cpp:] Top shape: ()
I0423 ::33.778578   net.cpp:]     with loss weight
I0423 ::33.778602   net.cpp:] Memory required for data:
I0423 ::33.778609   net.cpp:] loss needs backward computation.
I0423 ::33.778616   net.cpp:] accuracy does not need backward computation.
I0423 ::33.778621   net.cpp:] conv8_sigmoid_0_split needs backward computation.
I0423 ::33.778625   net.cpp:] sigmoid needs backward computation.
I0423 ::33.778627   net.cpp:] relu8 needs backward computation.
I0423 ::33.778630   net.cpp:] conv8 needs backward computation.
I0423 ::33.778633   net.cpp:] relu7 needs backward computation.
I0423 ::33.778636   net.cpp:] conv7 needs backward computation.
I0423 ::33.778640   net.cpp:] relu6 needs backward computation.
I0423 ::33.778642   net.cpp:] conv6 needs backward computation.
I0423 ::33.778646   net.cpp:] pool5 needs backward computation.
I0423 ::33.778651   net.cpp:] relu5 needs backward computation.
I0423 ::33.778655   net.cpp:] conv5 needs backward computation.
I0423 ::33.778657   net.cpp:] relu4 needs backward computation.
I0423 ::33.778661   net.cpp:] conv4 needs backward computation.
I0423 ::33.778664   net.cpp:] relu3 needs backward computation.
I0423 ::33.778666   net.cpp:] conv3 needs backward computation.
I0423 ::33.778671   net.cpp:] pool2 needs backward computation.
I0423 ::33.778673   net.cpp:] norm2 needs backward computation.
I0423 ::33.778677   net.cpp:] relu2 needs backward computation.
I0423 ::33.778681   net.cpp:] conv2 needs backward computation.
I0423 ::33.778684   net.cpp:] pool1 needs backward computation.
I0423 ::33.778687   net.cpp:] norm1 needs backward computation.
I0423 ::33.778692   net.cpp:] relu1 needs backward computation.
I0423 ::33.778694   net.cpp:] conv1 needs backward computation.
I0423 ::33.778698   net.cpp:] label_data_1_split does not need backward computation.
I0423 ::33.778702   net.cpp:] data does not need backward computation.
I0423 ::33.778705   net.cpp:] This network produces output accuracy
I0423 ::33.778709   net.cpp:] This network produces output loss
I0423 ::33.778728   net.cpp:] Network initialization done.
I0423 ::33.778976   solver.cpp:] Solver scaffolding done.
I0423 ::33.779458   caffe.cpp:] Finetuning from /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 ::34.067591   upgrade_proto.cpp:] Attempting to upgrade input file specified using deprecated transformation parameters: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 ::34.067654   upgrade_proto.cpp:] Successfully upgraded file specified using deprecated data transformation parameters.
W0423 ::34.067659   upgrade_proto.cpp:] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0423 ::34.067752   upgrade_proto.cpp:] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 ::34.193063   upgrade_proto.cpp:] Successfully upgraded file specified using deprecated V1LayerParameter
I0423 ::34.196166   net.cpp:] Ignoring source layer fc6
I0423 ::34.196195   net.cpp:] Ignoring source layer drop6
I0423 ::34.196199   net.cpp:] Ignoring source layer fc7
I0423 ::34.196203   net.cpp:] Ignoring source layer drop7
I0423 ::34.196207   net.cpp:] Ignoring source layer fc8
I0423 ::34.491250   upgrade_proto.cpp:] Attempting to upgrade input file specified using deprecated transformation parameters: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 ::34.491279   upgrade_proto.cpp:] Successfully upgraded file specified using deprecated data transformation parameters.
W0423 ::34.491284   upgrade_proto.cpp:] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0423 ::34.491298   upgrade_proto.cpp:] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/wangxiao/Downloads/fcn-caffe-master/wangxiao/bvlc_alexnet.caffemodel
I0423 ::34.615309   upgrade_proto.cpp:] Successfully upgraded file specified using deprecated V1LayerParameter
I0423 ::34.617781   net.cpp:] Ignoring source layer fc6
I0423 ::34.617805   net.cpp:] Ignoring source layer drop6
I0423 ::34.617808   net.cpp:] Ignoring source layer fc7
I0423 ::34.617812   net.cpp:] Ignoring source layer drop7
I0423 ::34.617815   net.cpp:] Ignoring source layer fc8
I0423 ::34.619755   caffe.cpp:] Starting Optimization
I0423 ::34.619771   solver.cpp:] Solving AlexNet
I0423 ::34.619776   solver.cpp:] Learning Rate Policy: step
I0423 ::35.070583   solver.cpp:] Iteration , loss = 7.51117
I0423 ::35.070628   sgd_solver.cpp:] Iteration , lr = 0.001
F0423 ::35.071538   syncedmem.cpp:] Check failed: error == cudaSuccess ( vs. )  out of memory
*** Check failure stack trace: ***
    @     0x7f3d97747daa  (unknown)
    @     0x7f3d97747ce4  (unknown)
    @     0x7f3d977476e6  (unknown)
    @     0x7f3d9774a687  (unknown)
    @     0x7f3d97e0fbd1  caffe::SyncedMemory::to_gpu()
    @     0x7f3d97e0ef39  caffe::SyncedMemory::mutable_gpu_data()
    @     0x7f3d97e76c02  caffe::Blob<>::mutable_gpu_data()
    @     0x7f3d97e8857c  caffe::SGDSolver<>::ComputeUpdateValue()
    @     0x7f3d97e88f73  caffe::SGDSolver<>::ApplyUpdate()
    @     0x7f3d97e2827c  caffe::Solver<>::Step()
    @     0x7f3d97e288c9  caffe::Solver<>::Solve()
    @           0x408abe  train()
    @           0x405f8c  main
    @     0x7f3d96a55ec5  (unknown)
    @           0x4066c1  (unknown)
    @              (nil)  (unknown)

　

　　Later, we will concentrate on how to locate the target object and shown us the feature from each Convolution layers.

　　Waiting and Continuing ...

　　All right, the terminal shown me this, oh, my god ... Wrong ! Wrong ! Wrong !!!

　　The loss = nan , fuck, how it possible ???

Due to the base_lr = 0.001, and change into base_lr = 0.000001, the loss become normal.