caffe从秃头到入门 /usr/bin/ruby -e “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master

Caffe主要由Blob，Layer，Net 和 Solver这几个部分组成。

Caffe 的安装

首先安装brew

how to install brew on Mac_利弗莫尔_truefan的博客-CSDN博客

然后直接使用命令安装

brew install caffe

Caffe的卸载

 /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/uninstall)"

caffe源代码地址：

GitHub - BVLC/caffe: Caffe: a fast open framework for deep learning.https://github.com/BVLC/caffe

Blobs,Layers,and Nets：深度网络的组成模式表示为数据块工作的内部连接层的集合。

caffe定义了数据存储，层层的网络（一个Caffe层(Layer)是一个神经网络层的本质），网络和运行方式，以及如何训练一个网络（定义了损失函数和梯度下降函数）

具体的FCN16s的caffe架构如下所示

http://dl.caffe.berkeleyvision.org/siftflow-fcn16s-heavy.caffemodel



layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "sem"
  top: "geo"
  python_param {
    module: "siftflow_layers"
    layer: "SIFTFlowSegDataLayer"
    param_str: "{\'siftflow_dir\': \'../data/sift-flow\', \'seed\': 1337, \'split\': \'trainval\'}"
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 100
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  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
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  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
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  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
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    pad: 0
    kernel_size: 7
    stride: 1
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "Convolution"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    pad: 0
    kernel_size: 1
    stride: 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: "score_fr_sem"
  type: "Convolution"
  bottom: "fc7"
  top: "score_fr_sem"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 33
    pad: 0
    kernel_size: 1
  }
}
layer {
  name: "upscore2_sem"
  type: "Deconvolution"
  bottom: "score_fr_sem"
  top: "upscore2_sem"
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 33
    bias_term: false
    kernel_size: 4
    stride: 2
  }
}
layer {
  name: "score_pool4_sem"
  type: "Convolution"
  bottom: "pool4"
  top: "score_pool4_sem"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 33
    pad: 0
    kernel_size: 1
  }
}
layer {
  name: "score_pool4_semc"
  type: "Crop"
  bottom: "score_pool4_sem"
  bottom: "upscore2_sem"
  top: "score_pool4_semc"
  crop_param {
    axis: 2
    offset: 5
  }
}
layer {
  name: "fuse_pool4_sem"
  type: "Eltwise"
  bottom: "upscore2_sem"
  bottom: "score_pool4_semc"
  top: "fuse_pool4_sem"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "upscore16_sem"
  type: "Deconvolution"
  bottom: "fuse_pool4_sem"
  top: "upscore16_sem"
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 33
    bias_term: false
    kernel_size: 32
    stride: 16
  }
}
layer {
  name: "score_sem"
  type: "Crop"
  bottom: "upscore16_sem"
  bottom: "data"
  top: "score_sem"
  crop_param {
    axis: 2
    offset: 27
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "score_sem"
  bottom: "sem"
  top: "loss"
  loss_param {
    ignore_label: 255
    normalize: false
  }
}
layer {
  name: "score_fr_geo"
  type: "Convolution"
  bottom: "fc7"
  top: "score_fr_geo"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 3
    pad: 0
    kernel_size: 1
  }
}
layer {
  name: "upscore2_geo"
  type: "Deconvolution"
  bottom: "score_fr_geo"
  top: "upscore2_geo"
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 3
    bias_term: false
    kernel_size: 4
    stride: 2
  }
}
layer {
  name: "score_pool4_geo"
  type: "Convolution"
  bottom: "pool4"
  top: "score_pool4_geo"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 3
    pad: 0
    kernel_size: 1
  }
}
layer {
  name: "score_pool4_geoc"
  type: "Crop"
  bottom: "score_pool4_geo"
  bottom: "upscore2_geo"
  top: "score_pool4_geoc"
  crop_param {
    axis: 2
    offset: 5
  }
}
layer {
  name: "fuse_pool4_geo"
  type: "Eltwise"
  bottom: "upscore2_geo"
  bottom: "score_pool4_geoc"
  top: "fuse_pool4_geo"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "upscore16_geo"
  type: "Deconvolution"
  bottom: "fuse_pool4_geo"
  top: "upscore16_geo"
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 3
    bias_term: false
    kernel_size: 32
    stride: 16
  }
}
layer {
  name: "score_geo"
  type: "Crop"
  bottom: "upscore16_geo"
  bottom: "data"
  top: "score_geo"
  crop_param {
    axis: 2
    offset: 27
  }
}
layer {
  name: "loss_geo"
  type: "SoftmaxWithLoss"
  bottom: "score_geo"
  bottom: "geo"
  top: "loss_geo"
  loss_param {
    ignore_label: 255
    normalize: false
  }
}