define fully-convolutional imagenet model

diff --git a/examples/imagenet/imagenet_full_conv.prototxt b/examples/imagenet/imagenet_full_conv.prototxt
new file mode 100644 (file)
index 0000000..6473c1f
--- /dev/null
+++ b/examples/imagenet/imagenet_full_conv.prototxt
@@ -0,0 +1,215 @@
+name: "CaffeNetConv"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 454
+input_dim: 454
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6-conv"
+  type: CONVOLUTION
+  bottom: "pool5"
+  top: "fc6-conv"
+  convolution_param {
+    num_output: 4096
+    kernel_size: 6
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6-conv"
+  top: "fc6-conv"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6-conv"
+  top: "fc6-conv"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7-conv"
+  type: CONVOLUTION
+  bottom: "fc6-conv"
+  top: "fc7-conv"
+  convolution_param {
+    num_output: 4096
+    kernel_size: 1
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7-conv"
+  top: "fc7-conv"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7-conv"
+  top: "fc7-conv"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8-conv"
+  type: CONVOLUTION
+  bottom: "fc7-conv"
+  top: "fc8-conv"
+  convolution_param {
+    num_output: 1000
+    kernel_size: 1
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8-conv"
+  top: "prob"
+}