From: Yangqing Jia <jiayq84@gmail.com>
Date: Tue, 19 Nov 2013 00:06:33 +0000 (-0800)
Subject: imagenet deploy prototxt: instead of having a data layer, this network proto takes... 
X-Git-Tag: submit/tizen/20180823.020014~856^2~1
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=91254090bd348398bffb7672a5d4e23ecd09e23c;p=platform%2Fupstream%2Fcaffeonacl.git

imagenet deploy prototxt: instead of having a data layer, this network proto takes an external blob called data. The shape is hardcoded to accompany the imagenet python wrapper at python/caffe/imagenet/wrapper.py. You need to change the shape if you intend to use other shapes.
---

diff --git a/examples/imagenet_deploy.prototxt b/examples/imagenet_deploy.prototxt
new file mode 100644
index 0000000..6257914
--- /dev/null
+++ b/examples/imagenet_deploy.prototxt
@@ -0,0 +1,355 @@
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layers {
+  layer {
+    name: "conv1"
+    type: "conv"
+    num_output: 96
+    kernelsize: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "data"
+  top: "conv1"
+}
+layers {
+  layer {
+    name: "relu1"
+    type: "relu"
+  }
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  layer {
+    name: "pool1"
+    type: "pool"
+    pool: MAX
+    kernelsize: 3
+    stride: 2
+  }
+  bottom: "conv1"
+  top: "pool1"
+}
+layers {
+  layer {
+    name: "norm1"
+    type: "lrn"
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+  bottom: "pool1"
+  top: "norm1"
+}
+layers {
+  layer {
+    name: "pad2"
+    type: "padding"
+    pad: 2
+  }
+  bottom: "norm1"
+  top: "pad2"
+}
+layers {
+  layer {
+    name: "conv2"
+    type: "conv"
+    num_output: 256
+    group: 2
+    kernelsize: 5
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "pad2"
+  top: "conv2"
+}
+layers {
+  layer {
+    name: "relu2"
+    type: "relu"
+  }
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  layer {
+    name: "pool2"
+    type: "pool"
+    pool: MAX
+    kernelsize: 3
+    stride: 2
+  }
+  bottom: "conv2"
+  top: "pool2"
+}
+layers {
+  layer {
+    name: "norm2"
+    type: "lrn"
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+  bottom: "pool2"
+  top: "norm2"
+}
+layers {
+  layer {
+    name: "pad3"
+    type: "padding"
+    pad: 1
+  }
+  bottom: "norm2"
+  top: "pad3"
+}
+layers {
+  layer {
+    name: "conv3"
+    type: "conv"
+    num_output: 384
+    kernelsize: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "pad3"
+  top: "conv3"
+}
+layers {
+  layer {
+    name: "relu3"
+    type: "relu"
+  }
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  layer {
+    name: "pad4"
+    type: "padding"
+    pad: 1
+  }
+  bottom: "conv3"
+  top: "pad4"
+}
+layers {
+  layer {
+    name: "conv4"
+    type: "conv"
+    num_output: 384
+    group: 2
+    kernelsize: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "pad4"
+  top: "conv4"
+}
+layers {
+  layer {
+    name: "relu4"
+    type: "relu"
+  }
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  layer {
+    name: "pad5"
+    type: "padding"
+    pad: 1
+  }
+  bottom: "conv4"
+  top: "pad5"
+}
+layers {
+  layer {
+    name: "conv5"
+    type: "conv"
+    num_output: 256
+    group: 2
+    kernelsize: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "pad5"
+  top: "conv5"
+}
+layers {
+  layer {
+    name: "relu5"
+    type: "relu"
+  }
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  layer {
+    name: "pool5"
+    type: "pool"
+    kernelsize: 3
+    pool: MAX
+    stride: 2
+  }
+  bottom: "conv5"
+  top: "pool5"
+}
+layers {
+  layer {
+    name: "fc6"
+    type: "innerproduct"
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "pool5"
+  top: "fc6"
+}
+layers {
+  layer {
+    name: "relu6"
+    type: "relu"
+  }
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  layer {
+    name: "drop6"
+    type: "dropout"
+    dropout_ratio: 0.5
+  }
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  layer {
+    name: "fc7"
+    type: "innerproduct"
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "fc6"
+  top: "fc7"
+}
+layers {
+  layer {
+    name: "relu7"
+    type: "relu"
+  }
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  layer {
+    name: "drop7"
+    type: "dropout"
+    dropout_ratio: 0.5
+  }
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  layer {
+    name: "fc8"
+    type: "innerproduct"
+    num_output: 1000
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+    blobs_lr: 1.
+    blobs_lr: 2.
+    weight_decay: 1.
+    weight_decay: 0.
+  }
+  bottom: "fc7"
+  top: "fc8"
+}
+layers {
+  layer {
+    name: "prob"
+    type: "softmax"
+  }
+  bottom: "fc8"
+  top: "prob"
+}