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diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 8bbd300..c16f7dc 100644 (file)
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -6,13 +6,17 @@ input_dim: 1
 input_dim: 3
 input_dim: 32
 input_dim: 32
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -20,9 +24,9 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -31,31 +35,35 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -63,15 +71,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -80,21 +88,21 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -104,15 +112,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -121,22 +129,26 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 10
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip1"
   top: "prob"
 }

diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index 38cc04f..d45fc61 100644 (file)
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -1,41 +1,49 @@
 name: "CIFAR10_full"
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -50,9 +58,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -61,31 +69,35 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -100,15 +112,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -117,21 +129,21 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -148,15 +160,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -165,15 +177,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -185,17 +201,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip1"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip1"
   bottom: "label"
   top: "loss"

diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 505158f..1ad190e 100644 (file)
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -4,13 +4,17 @@ input_dim: 1
 input_dim: 3
 input_dim: 32
 input_dim: 32
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -18,9 +22,9 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -29,19 +33,23 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -49,15 +57,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -66,13 +74,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -80,15 +92,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -97,31 +109,39 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 64
   }
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip2"
   top: "prob"
 }

diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index 074bb00..2317739 100644 (file)
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -1,41 +1,49 @@
 name: "CIFAR10_quick"
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -50,9 +58,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -61,19 +69,23 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -88,15 +100,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -105,13 +117,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -126,15 +142,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -143,13 +159,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 64
     weight_filler {
@@ -161,13 +181,17 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -179,17 +203,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip2"
   bottom: "label"
   top: "loss"

diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 83fe8c1..b0a1cef 100644 (file)
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -1,24 +1,24 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "examples/_temp/file_list.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -27,15 +27,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -44,9 +44,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -55,9 +55,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -67,15 +67,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -84,9 +84,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -95,9 +95,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -106,15 +106,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -124,15 +124,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -142,15 +142,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -159,79 +159,79 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
   inner_product_param {
     num_output: 1000
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "prob"
   bottom: "label"
   top: "accuracy"
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"

diff --git a/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
index 5cd605b..9dd2120 100644 (file)
--- a/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
@@ -1,9 +1,17 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: WINDOW_DATA
+  type: "WindowData"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   window_data_param {
     source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
     batch_size: 128
@@ -13,18 +21,20 @@ layers {
     context_pad: 16
     crop_mode: "warp"
   }
+}
+layer {
+  name: "data"
+  type: "WindowData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
   transform_param {
     mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
   }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: WINDOW_DATA
-  top: "data"
-  top: "label"
   window_data_param {
     source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
     batch_size: 128
@@ -34,22 +44,20 @@ layers {
     context_pad: 16
     crop_mode: "warp"
   }
-  transform_param {
-    mirror: true
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -64,15 +72,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -81,9 +89,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -92,15 +100,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -116,15 +128,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -133,9 +145,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -144,15 +156,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -167,21 +183,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -197,21 +217,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -227,15 +251,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -244,15 +268,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -265,30 +293,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -301,30 +333,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_pascal"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_pascal"
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 21
     weight_filler {
@@ -337,17 +373,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8_pascal"
   bottom: "label"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8_pascal"
   bottom: "label"
   top: "accuracy"
-  include { phase: TEST }
+  include {
+    phase: TEST
+  }
 }

diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
index b55b664..b9ccc1a 100644 (file)
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -1,35 +1,43 @@
 name: "LogisticRegressionNet"
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "fc1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "data"
   top: "fc1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -42,18 +50,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc1"
   bottom: "label"
   top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc1"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }

diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/train_val2.prototxt
index b6a7565..f9ef731 100644 (file)
--- a/examples/hdf5_classification/train_val2.prototxt
+++ b/examples/hdf5_classification/train_val2.prototxt
@@ -1,35 +1,43 @@
 name: "LogisticRegressionNet"
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "fc1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "data"
   top: "fc1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 40
     weight_filler {
@@ -42,21 +50,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "fc1"
   top: "fc1"
 }
-layers {
+layer {
   name: "fc2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc1"
   top: "fc2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -69,18 +81,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc2"
   bottom: "label"
   top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }

diff --git a/examples/imagenet/bvlc_caffenet_full_conv.prototxt b/examples/imagenet/bvlc_caffenet_full_conv.prototxt
index 395b0f0..7b22bfa 100644 (file)
--- a/examples/imagenet/bvlc_caffenet_full_conv.prototxt
+++ b/examples/imagenet/bvlc_caffenet_full_conv.prototxt
@@ -5,9 +5,9 @@ input_dim: 1
 input_dim: 3
 input_dim: 451
 input_dim: 451
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -16,15 +16,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -33,9 +33,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -44,9 +44,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -56,15 +56,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -73,9 +73,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -84,9 +84,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -95,15 +95,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -113,15 +113,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -131,15 +131,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -148,9 +148,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool5"
   top: "fc6-conv"
   convolution_param {
@@ -158,24 +158,24 @@ layers {
     kernel_size: 6
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6-conv"
   top: "fc6-conv"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6-conv"
   top: "fc6-conv"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "fc6-conv"
   top: "fc7-conv"
   convolution_param {
@@ -183,24 +183,24 @@ layers {
     kernel_size: 1
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7-conv"
   top: "fc7-conv"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7-conv"
   top: "fc7-conv"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "fc7-conv"
   top: "fc8-conv"
   convolution_param {
@@ -208,9 +208,9 @@ layers {
     kernel_size: 1
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8-conv"
   top: "prob"
 }

diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index 491fad1..cb42610 100644 (file)
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -4,13 +4,17 @@ input_dim: 64
 input_dim: 1
 input_dim: 28
 input_dim: 28
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -23,9 +27,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -34,13 +38,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -53,9 +61,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -64,13 +72,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -81,19 +93,23 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -104,9 +120,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip2"
   top: "prob"
 }

diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index 2bd960b..b18fc26 100644 (file)
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -1,42 +1,49 @@
 name: "LeNet"
-layers {
+layer {
   name: "mnist"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
-    batch_size: 64
+  include {
+    phase: TRAIN
   }
   transform_param {
     scale: 0.00390625
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    batch_size: 64
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "mnist"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/mnist/mnist_test_lmdb"
-    backend: LMDB
-    batch_size: 100
+  include {
+    phase: TEST
   }
   transform_param {
     scale: 0.00390625
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -49,9 +56,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -60,13 +67,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -79,9 +90,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -90,13 +101,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -107,19 +122,23 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -130,17 +149,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip2"
   bottom: "label"
   top: "loss"

diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index 0b33781..563c7c9 100644 (file)
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -1,67 +1,73 @@
 name: "MNISTAutoencoder"
-layers {
-  top: "data"
+layer {
   name: "data"
-  type: DATA
-  data_param {
-    source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
-    batch_size: 100
+  type: "Data"
+  top: "data"
+  include {
+    phase: TRAIN
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: { phase: TRAIN }
-}
-layers {
-  top: "data"
-  name: "data"
-  type: DATA
   data_param {
     source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
     batch_size: 100
+    backend: LMDB
+  }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  include {
+    phase: TEST
+    stage: "test-on-train"
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: {
-    phase: TEST
-    stage: 'test-on-train'
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    batch_size: 100
+    backend: LMDB
   }
 }
-layers {
-  top: "data"
+layer {
   name: "data"
-  type: DATA
-  data_param {
-    source: "examples/mnist/mnist_test_lmdb"
-    backend: LMDB
-    batch_size: 100
+  type: "Data"
+  top: "data"
+  include {
+    phase: TEST
+    stage: "test-on-test"
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: {
-    phase: TEST
-    stage: 'test-on-test'
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    batch_size: 100
+    backend: LMDB
   }
 }
-layers {
+layer {
+  name: "flatdata"
+  type: "Flatten"
   bottom: "data"
   top: "flatdata"
-  name: "flatdata"
-  type: FLATTEN
 }
-layers {
+layer {
+  name: "encode1"
+  type: "InnerProduct"
   bottom: "data"
   top: "encode1"
-  name: "encode1"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -75,21 +81,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode1neuron"
+  type: "Sigmoid"
   bottom: "encode1"
   top: "encode1neuron"
-  name: "encode1neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode2"
+  type: "InnerProduct"
   bottom: "encode1neuron"
   top: "encode2"
-  name: "encode2"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -103,21 +113,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode2neuron"
+  type: "Sigmoid"
   bottom: "encode2"
   top: "encode2neuron"
-  name: "encode2neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode3"
+  type: "InnerProduct"
   bottom: "encode2neuron"
   top: "encode3"
-  name: "encode3"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 250
     weight_filler {
@@ -131,21 +145,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode3neuron"
+  type: "Sigmoid"
   bottom: "encode3"
   top: "encode3neuron"
-  name: "encode3neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode4"
+  type: "InnerProduct"
   bottom: "encode3neuron"
   top: "encode4"
-  name: "encode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 30
     weight_filler {
@@ -159,15 +177,19 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode4"
+  type: "InnerProduct"
   bottom: "encode4"
   top: "decode4"
-  name: "decode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 250
     weight_filler {
@@ -181,21 +203,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode4neuron"
+  type: "Sigmoid"
   bottom: "decode4"
   top: "decode4neuron"
-  name: "decode4neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode3"
+  type: "InnerProduct"
   bottom: "decode4neuron"
   top: "decode3"
-  name: "decode3"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -209,21 +235,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode3neuron"
+  type: "Sigmoid"
   bottom: "decode3"
   top: "decode3neuron"
-  name: "decode3neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode2"
+  type: "InnerProduct"
   bottom: "decode3neuron"
   top: "decode2"
-  name: "decode2"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -237,21 +267,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode2neuron"
+  type: "Sigmoid"
   bottom: "decode2"
   top: "decode2neuron"
-  name: "decode2neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode1"
+  type: "InnerProduct"
   bottom: "decode2neuron"
   top: "decode1"
-  name: "decode1"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 784
     weight_filler {
@@ -265,25 +299,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss"
+  type: "SigmoidCrossEntropyLoss"
   bottom: "decode1"
   bottom: "flatdata"
   top: "cross_entropy_loss"
-  name: "loss"
-  type: SIGMOID_CROSS_ENTROPY_LOSS
   loss_weight: 1
 }
-layers {
+layer {
+  name: "decode1neuron"
+  type: "Sigmoid"
   bottom: "decode1"
   top: "decode1neuron"
-  name: "decode1neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "loss"
+  type: "EuclideanLoss"
   bottom: "decode1neuron"
   bottom: "flatdata"
   top: "l2_error"
-  name: "loss"
-  type: EUCLIDEAN_LOSS
   loss_weight: 0
 }

diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 8dd42e9..0e903f8 100644 (file)
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -4,23 +4,26 @@ input_dim: 10000
 input_dim: 1
 input_dim: 28
 input_dim: 28
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -29,22 +32,26 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -53,42 +60,53 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
   }
 }
-
-layers {
+layer {
   name: "feat"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2"
   top: "feat"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
   }

diff --git a/examples/siamese/mnist_siamese_train_test.prototxt b/examples/siamese/mnist_siamese_train_test.prototxt
index 92361c3..8ff864f 100644 (file)
--- a/examples/siamese/mnist_siamese_train_test.prototxt
+++ b/examples/siamese/mnist_siamese_train_test.prototxt
@@ -1,50 +1,60 @@
 name: "mnist_siamese_train_test"
-layers {
+layer {
   name: "pair_data"
-  type: DATA
+  type: "Data"
   top: "pair_data"
   top: "sim"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    scale: 0.00390625
+  }
   data_param {
     source: "examples/siamese/mnist_siamese_train_leveldb"
-    scale: 0.00390625
     batch_size: 64
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "pair_data"
-  type: DATA
+  type: "Data"
   top: "pair_data"
   top: "sim"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    scale: 0.00390625
+  }
   data_param {
     source: "examples/siamese/mnist_siamese_test_leveldb"
-    scale: 0.00390625
     batch_size: 100
   }
-  include: { phase: TEST }
 }
-layers {
-    name: "slice_pair"
-    type: SLICE
-    bottom: "pair_data"
-    top: "data"
-    top: "data_p"
-    slice_param {
-        slice_dim: 1
-        slice_point: 1
-    }
+layer {
+  name: "slice_pair"
+  type: "Slice"
+  bottom: "pair_data"
+  top: "data"
+  top: "data_p"
+  slice_param {
+    slice_dim: 1
+    slice_point: 1
+  }
 }
-
-
-
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv1_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -56,12 +66,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv1_w"
-  param: "conv1_b"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -70,13 +78,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv2_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -88,12 +102,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv2_w"
-  param: "conv2_b"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -102,13 +114,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip1_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -118,22 +136,26 @@ layers {
       type: "constant"
     }
   }
-  param: "ip1_w"
-  param: "ip1_b"
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip2_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -143,17 +165,20 @@ layers {
       type: "constant"
     }
   }
-  param: "ip2_w"
-  param: "ip2_b"
 }
-
-layers {
+layer {
   name: "feat"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2"
   top: "feat"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -163,19 +188,20 @@ layers {
       type: "constant"
     }
   }
-  param: "feat_w"
-  param: "feat_b"
 }
-
-
-
-layers {
+layer {
   name: "conv1_p"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data_p"
   top: "conv1_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv1_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -187,12 +213,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv1_w"
-  param: "conv1_b"
 }
-layers {
+layer {
   name: "pool1_p"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1_p"
   top: "pool1_p"
   pooling_param {
@@ -201,13 +225,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2_p"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1_p"
   top: "conv2_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv2_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -219,12 +249,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv2_w"
-  param: "conv2_b"
 }
-layers {
+layer {
   name: "pool2_p"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2_p"
   top: "pool2_p"
   pooling_param {
@@ -233,13 +261,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2_p"
   top: "ip1_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip1_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -249,22 +283,26 @@ layers {
       type: "constant"
     }
   }
-  param: "ip1_w"
-  param: "ip1_b"
 }
-layers {
+layer {
   name: "relu1_p"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1_p"
   top: "ip1_p"
 }
-layers {
+layer {
   name: "ip2_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1_p"
   top: "ip2_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip2_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -274,17 +312,20 @@ layers {
       type: "constant"
     }
   }
-  param: "ip2_w"
-  param: "ip2_b"
 }
-
-layers {
+layer {
   name: "feat_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2_p"
   top: "feat_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -294,20 +335,15 @@ layers {
       type: "constant"
     }
   }
-  param: "feat_w"
-  param: "feat_b"
 }
-
-
-
-layers {
-    name: "loss"
-    type: CONTRASTIVE_LOSS
-    contrastive_loss_param {
-        margin: 1.0
-    }
-    bottom: "feat"
-    bottom: "feat_p"
-    bottom: "sim"
-    top: "loss"
+layer {
+  name: "loss"
+  type: "ContrastiveLoss"
+  bottom: "feat"
+  bottom: "feat_p"
+  bottom: "sim"
+  top: "loss"
+  contrastive_loss_param {
+    margin: 1
+  }
 }

diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index d010753..ced055b 100644 (file)
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -4,241 +4,273 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
     stride: 4
   }
-  bottom: "data"
-  top: "conv1"
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv1"
-  top: "norm1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm1"
-  top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
     kernel_size: 5
     group: 2
   }
-  bottom: "pool1"
-  top: "conv2"
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv2"
-  top: "norm2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm2"
-  top: "pool2"
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
   }
-  bottom: "pool2"
-  top: "conv3"
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv3"
-  top: "conv4"
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv4"
-  top: "conv5"
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "conv5"
-  top: "pool5"
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
   }
-  bottom: "pool5"
-  top: "fc6"
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc6"
-  top: "fc6"
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  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: 4096
   }
-  bottom: "fc6"
-  top: "fc7"
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc7"
-  top: "fc7"
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
   }
-  bottom: "fc7"
-  top: "fc8"
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }

diff --git a/models/bvlc_alexnet/train_val.prototxt b/models/bvlc_alexnet/train_val.prototxt
index 717b6fa..588b4ea 100644 (file)
--- a/models/bvlc_alexnet/train_val.prototxt
+++ b/models/bvlc_alexnet/train_val.prototxt
@@ -1,47 +1,55 @@
 name: "AlexNet"
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    backend: LMDB
-    batch_size: 256
+  include {
+    phase: TRAIN
   }
   transform_param {
+    mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    backend: LMDB
-    batch_size: 50
+  include {
+    phase: TEST
   }
   transform_param {
+    mirror: false
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -56,15 +64,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "conv1"
   top: "norm1"
   lrn_param {
@@ -73,9 +81,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "norm1"
   top: "pool1"
   pooling_param {
@@ -84,15 +92,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -108,15 +120,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "conv2"
   top: "norm2"
   lrn_param {
@@ -125,9 +137,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "norm2"
   top: "pool2"
   pooling_param {
@@ -136,15 +148,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -159,21 +175,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -189,21 +209,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -219,15 +243,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -236,15 +260,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -257,30 +285,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -293,30 +325,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -329,17 +365,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"

diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index e31a4c9..4648bf2 100644 (file)
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -4,15 +4,19 @@ input_dim: 10
 input_dim: 3
 input_dim: 224
 input_dim: 224
-layers {
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
   bottom: "data"
   top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 3
@@ -28,43 +32,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
   bottom: "conv1/7x7_s2"
   top: "conv1/7x7_s2"
-  name: "conv1/relu_7x7"
-  type: RELU
 }
-layers {
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
   bottom: "conv1/7x7_s2"
   top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
   bottom: "pool1/3x3_s2"
   top: "pool1/norm1"
-  name: "pool1/norm1"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
   bottom: "pool1/norm1"
   top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -78,21 +86,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3_reduce"
-  name: "conv2/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -107,43 +119,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
   bottom: "conv2/3x3"
   top: "conv2/3x3"
-  name: "conv2/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
   bottom: "conv2/3x3"
   top: "conv2/norm2"
-  name: "conv2/norm2"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
   bottom: "conv2/norm2"
   top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -157,21 +173,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3a/1x1"
   top: "inception_3a/1x1"
-  name: "inception_3a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -185,21 +205,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 1
@@ -214,21 +238,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3a/3x3"
   top: "inception_3a/3x3"
-  name: "inception_3a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -242,21 +270,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5"
-  name: "inception_3a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -271,17 +303,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3a/5x5"
   top: "inception_3a/5x5"
-  name: "inception_3a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -289,15 +321,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
   bottom: "inception_3a/pool"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -311,30 +347,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
   bottom: "inception_3a/1x1"
   bottom: "inception_3a/3x3"
   bottom: "inception_3a/5x5"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -348,21 +388,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3b/1x1"
   top: "inception_3b/1x1"
-  name: "inception_3b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -376,21 +420,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -405,21 +453,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3b/3x3"
   top: "inception_3b/3x3"
-  name: "inception_3b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -433,21 +485,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5"
-  name: "inception_3b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     pad: 2
@@ -462,17 +518,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3b/5x5"
   top: "inception_3b/5x5"
-  name: "inception_3b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
   bottom: "inception_3a/output"
   top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -480,15 +536,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
   bottom: "inception_3b/pool"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -502,41 +562,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
   bottom: "inception_3b/1x1"
   bottom: "inception_3b/3x3"
   bottom: "inception_3b/5x5"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
   bottom: "inception_3b/output"
   top: "pool3/3x3_s2"
-  name: "pool3/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -550,21 +614,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4a/1x1"
   top: "inception_4a/1x1"
-  name: "inception_4a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -578,21 +646,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 208
     pad: 1
@@ -607,21 +679,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4a/3x3"
   top: "inception_4a/3x3"
-  name: "inception_4a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -635,21 +711,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5"
-  name: "inception_4a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     pad: 2
@@ -664,17 +744,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4a/5x5"
   top: "inception_4a/5x5"
-  name: "inception_4a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -682,15 +762,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
   bottom: "inception_4a/pool"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -704,30 +788,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
   bottom: "inception_4a/1x1"
   bottom: "inception_4a/3x3"
   bottom: "inception_4a/5x5"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -741,21 +829,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4b/1x1"
   top: "inception_4b/1x1"
-  name: "inception_4b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -769,21 +861,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 224
     pad: 1
@@ -798,21 +894,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4b/3x3"
   top: "inception_4b/3x3"
-  name: "inception_4b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -826,21 +926,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5"
-  name: "inception_4b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -855,17 +959,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4b/5x5"
   top: "inception_4b/5x5"
-  name: "inception_4b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
   bottom: "inception_4a/output"
   top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -873,15 +977,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
   bottom: "inception_4b/pool"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -895,30 +1003,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
   bottom: "inception_4b/1x1"
   bottom: "inception_4b/3x3"
   bottom: "inception_4b/5x5"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -932,21 +1044,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4c/1x1"
   top: "inception_4c/1x1"
-  name: "inception_4c/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -960,21 +1076,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -989,21 +1109,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4c/3x3"
   top: "inception_4c/3x3"
-  name: "inception_4c/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -1017,21 +1141,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5"
-  name: "inception_4c/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1046,17 +1174,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4c/5x5"
   top: "inception_4c/5x5"
-  name: "inception_4c/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
   bottom: "inception_4b/output"
   top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1064,15 +1192,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
   bottom: "inception_4c/pool"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1086,30 +1218,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
   bottom: "inception_4c/1x1"
   bottom: "inception_4c/3x3"
   bottom: "inception_4c/5x5"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -1123,21 +1259,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4d/1x1"
   top: "inception_4d/1x1"
-  name: "inception_4d/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 144
     kernel_size: 1
@@ -1151,21 +1291,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 288
     pad: 1
@@ -1180,21 +1324,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4d/3x3"
   top: "inception_4d/3x3"
-  name: "inception_4d/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1208,21 +1356,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5"
-  name: "inception_4d/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1237,17 +1389,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4d/5x5"
   top: "inception_4d/5x5"
-  name: "inception_4d/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
   bottom: "inception_4c/output"
   top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1255,15 +1407,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
   bottom: "inception_4d/pool"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1277,30 +1433,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
   bottom: "inception_4d/1x1"
   bottom: "inception_4d/3x3"
   bottom: "inception_4d/5x5"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/1x1"
-  name: "inception_4e/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1314,21 +1474,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4e/1x1"
   top: "inception_4e/1x1"
-  name: "inception_4e/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1342,21 +1506,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1371,21 +1539,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4e/3x3"
   top: "inception_4e/3x3"
-  name: "inception_4e/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1399,21 +1571,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5"
-  name: "inception_4e/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1428,17 +1604,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4e/5x5"
   top: "inception_4e/5x5"
-  name: "inception_4e/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
   bottom: "inception_4d/output"
   top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1446,15 +1622,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
   bottom: "inception_4e/pool"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1468,41 +1648,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
   bottom: "inception_4e/1x1"
   bottom: "inception_4e/3x3"
   bottom: "inception_4e/5x5"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
   bottom: "inception_4e/output"
   top: "pool4/3x3_s2"
-  name: "pool4/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1516,21 +1700,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5a/1x1"
   top: "inception_5a/1x1"
-  name: "inception_5a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1544,21 +1732,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1573,21 +1765,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5a/3x3"
   top: "inception_5a/3x3"
-  name: "inception_5a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1601,21 +1797,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5"
-  name: "inception_5a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1630,17 +1830,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5a/5x5"
   top: "inception_5a/5x5"
-  name: "inception_5a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1648,15 +1848,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
   bottom: "inception_5a/pool"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1670,30 +1874,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
   bottom: "inception_5a/1x1"
   bottom: "inception_5a/3x3"
   bottom: "inception_5a/5x5"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     kernel_size: 1
@@ -1707,21 +1915,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5b/1x1"
   top: "inception_5b/1x1"
-  name: "inception_5b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -1735,21 +1947,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -1764,21 +1980,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5b/3x3"
   top: "inception_5b/3x3"
-  name: "inception_5b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     kernel_size: 1
@@ -1792,21 +2012,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5"
-  name: "inception_5b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1821,17 +2045,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5b/5x5"
   top: "inception_5b/5x5"
-  name: "inception_5b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
   bottom: "inception_5a/output"
   top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1839,15 +2063,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
   bottom: "inception_5b/pool"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1861,50 +2089,54 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
   bottom: "inception_5b/1x1"
   bottom: "inception_5b/3x3"
   bottom: "inception_5b/5x5"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
   bottom: "inception_5b/output"
   top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 7
     stride: 1
   }
 }
-layers {
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
   bottom: "pool5/7x7_s1"
   top: "pool5/7x7_s1"
-  name: "pool5/drop_7x7_s1"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.4
   }
 }
-layers {
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
   bottom: "pool5/7x7_s1"
   top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -1916,9 +2148,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "loss3/classifier"
   top: "prob"
-}
\ No newline at end of file
+}

diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
index cd8f38a..79ede2b 100644 (file)
--- a/models/bvlc_googlenet/train_val.prototxt
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -1,14 +1,9 @@
 name: "GoogleNet"
-layers {
+layer {
+  name: "data"
+  type: "Data"
   top: "data"
   top: "label"
-  name: "data"
-  type: DATA
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    batch_size: 32
-    backend: LMDB
-  }
   include {
     phase: TRAIN
   }
@@ -19,17 +14,17 @@ layers {
     mean_value: 117
     mean_value: 123
   }
-}
-layers {
-  top: "data"
-  top: "label"
-  name: "data"
-  type: DATA
   data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    batch_size: 50
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 32
     backend: LMDB
   }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
   include {
     phase: TEST
   }
@@ -40,16 +35,25 @@ layers {
     mean_value: 117
     mean_value: 123
   }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
   bottom: "data"
   top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 3
@@ -65,43 +69,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
   bottom: "conv1/7x7_s2"
   top: "conv1/7x7_s2"
-  name: "conv1/relu_7x7"
-  type: RELU
 }
-layers {
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
   bottom: "conv1/7x7_s2"
   top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
   bottom: "pool1/3x3_s2"
   top: "pool1/norm1"
-  name: "pool1/norm1"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
   bottom: "pool1/norm1"
   top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -115,21 +123,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3_reduce"
-  name: "conv2/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -144,43 +156,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
   bottom: "conv2/3x3"
   top: "conv2/3x3"
-  name: "conv2/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
   bottom: "conv2/3x3"
   top: "conv2/norm2"
-  name: "conv2/norm2"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
   bottom: "conv2/norm2"
   top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -194,21 +210,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3a/1x1"
   top: "inception_3a/1x1"
-  name: "inception_3a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -222,21 +242,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 1
@@ -251,21 +275,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3a/3x3"
   top: "inception_3a/3x3"
-  name: "inception_3a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -279,21 +307,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5"
-  name: "inception_3a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -308,17 +340,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3a/5x5"
   top: "inception_3a/5x5"
-  name: "inception_3a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -326,15 +358,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
   bottom: "inception_3a/pool"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -348,30 +384,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
   bottom: "inception_3a/1x1"
   bottom: "inception_3a/3x3"
   bottom: "inception_3a/5x5"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -385,21 +425,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3b/1x1"
   top: "inception_3b/1x1"
-  name: "inception_3b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -413,21 +457,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -442,21 +490,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3b/3x3"
   top: "inception_3b/3x3"
-  name: "inception_3b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -470,21 +522,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5"
-  name: "inception_3b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     pad: 2
@@ -499,17 +555,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3b/5x5"
   top: "inception_3b/5x5"
-  name: "inception_3b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
   bottom: "inception_3a/output"
   top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -517,15 +573,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
   bottom: "inception_3b/pool"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -539,41 +599,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
   bottom: "inception_3b/1x1"
   bottom: "inception_3b/3x3"
   bottom: "inception_3b/5x5"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
   bottom: "inception_3b/output"
   top: "pool3/3x3_s2"
-  name: "pool3/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -587,21 +651,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4a/1x1"
   top: "inception_4a/1x1"
-  name: "inception_4a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -615,21 +683,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 208
     pad: 1
@@ -644,21 +716,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4a/3x3"
   top: "inception_4a/3x3"
-  name: "inception_4a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -672,21 +748,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5"
-  name: "inception_4a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     pad: 2
@@ -701,17 +781,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4a/5x5"
   top: "inception_4a/5x5"
-  name: "inception_4a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -719,15 +799,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
   bottom: "inception_4a/pool"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -741,41 +825,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
   bottom: "inception_4a/1x1"
   bottom: "inception_4a/3x3"
   bottom: "inception_4a/5x5"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "loss1/ave_pool"
+  type: "Pooling"
   bottom: "inception_4a/output"
   top: "loss1/ave_pool"
-  name: "loss1/ave_pool"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 5
     stride: 3
   }
 }
-layers {
+layer {
+  name: "loss1/conv"
+  type: "Convolution"
   bottom: "loss1/ave_pool"
   top: "loss1/conv"
-  name: "loss1/conv"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -789,21 +877,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss1/relu_conv"
+  type: "ReLU"
   bottom: "loss1/conv"
   top: "loss1/conv"
-  name: "loss1/relu_conv"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss1/fc"
+  type: "InnerProduct"
   bottom: "loss1/conv"
   top: "loss1/fc"
-  name: "loss1/fc"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1024
     weight_filler {
@@ -816,30 +908,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss1/relu_fc"
+  type: "ReLU"
   bottom: "loss1/fc"
   top: "loss1/fc"
-  name: "loss1/relu_fc"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss1/drop_fc"
+  type: "Dropout"
   bottom: "loss1/fc"
   top: "loss1/fc"
-  name: "loss1/drop_fc"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.7
   }
 }
-layers {
+layer {
+  name: "loss1/classifier"
+  type: "InnerProduct"
   bottom: "loss1/fc"
   top: "loss1/classifier"
-  name: "loss1/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -852,46 +948,50 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
   bottom: "loss1/classifier"
   bottom: "label"
   top: "loss1/loss1"
-  name: "loss1/loss"
-  type: SOFTMAX_LOSS
   loss_weight: 0.3
 }
-layers {
+layer {
+  name: "loss1/top-1"
+  type: "Accuracy"
   bottom: "loss1/classifier"
   bottom: "label"
   top: "loss1/top-1"
-  name: "loss1/top-1"
-  type: ACCURACY
   include {
     phase: TEST
   }
 }
-layers {
+layer {
+  name: "loss1/top-5"
+  type: "Accuracy"
   bottom: "loss1/classifier"
   bottom: "label"
   top: "loss1/top-5"
-  name: "loss1/top-5"
-  type: ACCURACY
-  accuracy_param {
-    top_k: 5
-  }
   include {
     phase: TEST
   }
+  accuracy_param {
+    top_k: 5
+  }
 }
-layers {
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -905,21 +1005,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4b/1x1"
   top: "inception_4b/1x1"
-  name: "inception_4b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -933,21 +1037,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 224
     pad: 1
@@ -962,21 +1070,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4b/3x3"
   top: "inception_4b/3x3"
-  name: "inception_4b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -990,21 +1102,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5"
-  name: "inception_4b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1019,17 +1135,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4b/5x5"
   top: "inception_4b/5x5"
-  name: "inception_4b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
   bottom: "inception_4a/output"
   top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1037,15 +1153,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
   bottom: "inception_4b/pool"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1059,30 +1179,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
   bottom: "inception_4b/1x1"
   bottom: "inception_4b/3x3"
   bottom: "inception_4b/5x5"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1096,21 +1220,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4c/1x1"
   top: "inception_4c/1x1"
-  name: "inception_4c/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1124,21 +1252,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -1153,21 +1285,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4c/3x3"
   top: "inception_4c/3x3"
-  name: "inception_4c/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -1181,21 +1317,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5"
-  name: "inception_4c/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1210,17 +1350,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4c/5x5"
   top: "inception_4c/5x5"
-  name: "inception_4c/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
   bottom: "inception_4b/output"
   top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1228,15 +1368,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
   bottom: "inception_4c/pool"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1250,30 +1394,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
   bottom: "inception_4c/1x1"
   bottom: "inception_4c/3x3"
   bottom: "inception_4c/5x5"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -1287,21 +1435,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4d/1x1"
   top: "inception_4d/1x1"
-  name: "inception_4d/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 144
     kernel_size: 1
@@ -1315,21 +1467,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 288
     pad: 1
@@ -1344,21 +1500,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4d/3x3"
   top: "inception_4d/3x3"
-  name: "inception_4d/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1372,21 +1532,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5"
-  name: "inception_4d/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1401,17 +1565,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4d/5x5"
   top: "inception_4d/5x5"
-  name: "inception_4d/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
   bottom: "inception_4c/output"
   top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1419,15 +1583,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
   bottom: "inception_4d/pool"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1441,41 +1609,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
   bottom: "inception_4d/1x1"
   bottom: "inception_4d/3x3"
   bottom: "inception_4d/5x5"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "loss2/ave_pool"
+  type: "Pooling"
   bottom: "inception_4d/output"
   top: "loss2/ave_pool"
-  name: "loss2/ave_pool"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 5
     stride: 3
   }
 }
-layers {
+layer {
+  name: "loss2/conv"
+  type: "Convolution"
   bottom: "loss2/ave_pool"
   top: "loss2/conv"
-  name: "loss2/conv"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1489,21 +1661,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss2/relu_conv"
+  type: "ReLU"
   bottom: "loss2/conv"
   top: "loss2/conv"
-  name: "loss2/relu_conv"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss2/fc"
+  type: "InnerProduct"
   bottom: "loss2/conv"
   top: "loss2/fc"
-  name: "loss2/fc"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1024
     weight_filler {
@@ -1516,30 +1692,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss2/relu_fc"
+  type: "ReLU"
   bottom: "loss2/fc"
   top: "loss2/fc"
-  name: "loss2/relu_fc"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss2/drop_fc"
+  type: "Dropout"
   bottom: "loss2/fc"
   top: "loss2/fc"
-  name: "loss2/drop_fc"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.7
   }
 }
-layers {
+layer {
+  name: "loss2/classifier"
+  type: "InnerProduct"
   bottom: "loss2/fc"
   top: "loss2/classifier"
-  name: "loss2/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -1552,46 +1732,50 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
   bottom: "loss2/classifier"
   bottom: "label"
   top: "loss2/loss1"
-  name: "loss2/loss"
-  type: SOFTMAX_LOSS
   loss_weight: 0.3
 }
-layers {
+layer {
+  name: "loss2/top-1"
+  type: "Accuracy"
   bottom: "loss2/classifier"
   bottom: "label"
   top: "loss2/top-1"
-  name: "loss2/top-1"
-  type: ACCURACY
   include {
     phase: TEST
   }
 }
-layers {
+layer {
+  name: "loss2/top-5"
+  type: "Accuracy"
   bottom: "loss2/classifier"
   bottom: "label"
   top: "loss2/top-5"
-  name: "loss2/top-5"
-  type: ACCURACY
-  accuracy_param {
-    top_k: 5
-  }
   include {
     phase: TEST
   }
+  accuracy_param {
+    top_k: 5
+  }
 }
-layers {
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/1x1"
-  name: "inception_4e/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1605,21 +1789,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4e/1x1"
   top: "inception_4e/1x1"
-  name: "inception_4e/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1633,21 +1821,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1662,21 +1854,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4e/3x3"
   top: "inception_4e/3x3"
-  name: "inception_4e/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1690,21 +1886,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5"
-  name: "inception_4e/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1719,17 +1919,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4e/5x5"
   top: "inception_4e/5x5"
-  name: "inception_4e/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
   bottom: "inception_4d/output"
   top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1737,15 +1937,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
   bottom: "inception_4e/pool"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1759,41 +1963,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
   bottom: "inception_4e/1x1"
   bottom: "inception_4e/3x3"
   bottom: "inception_4e/5x5"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
   bottom: "inception_4e/output"
   top: "pool4/3x3_s2"
-  name: "pool4/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1807,21 +2015,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5a/1x1"
   top: "inception_5a/1x1"
-  name: "inception_5a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1835,21 +2047,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1864,21 +2080,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5a/3x3"
   top: "inception_5a/3x3"
-  name: "inception_5a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1892,21 +2112,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5"
-  name: "inception_5a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1921,17 +2145,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5a/5x5"
   top: "inception_5a/5x5"
-  name: "inception_5a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1939,15 +2163,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
   bottom: "inception_5a/pool"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1961,30 +2189,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
   bottom: "inception_5a/1x1"
   bottom: "inception_5a/3x3"
   bottom: "inception_5a/5x5"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     kernel_size: 1
@@ -1998,21 +2230,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5b/1x1"
   top: "inception_5b/1x1"
-  name: "inception_5b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -2026,21 +2262,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -2055,21 +2295,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5b/3x3"
   top: "inception_5b/3x3"
-  name: "inception_5b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     kernel_size: 1
@@ -2083,21 +2327,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5"
-  name: "inception_5b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -2112,17 +2360,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5b/5x5"
   top: "inception_5b/5x5"
-  name: "inception_5b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
   bottom: "inception_5a/output"
   top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -2130,15 +2378,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
   bottom: "inception_5b/pool"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -2152,50 +2404,54 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
   bottom: "inception_5b/1x1"
   bottom: "inception_5b/3x3"
   bottom: "inception_5b/5x5"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
   bottom: "inception_5b/output"
   top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 7
     stride: 1
   }
 }
-layers {
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
   bottom: "pool5/7x7_s1"
   top: "pool5/7x7_s1"
-  name: "pool5/drop_7x7_s1"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.4
   }
 }
-layers {
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
   bottom: "pool5/7x7_s1"
   top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -2207,34 +2463,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss3/loss3"
+  type: "SoftmaxWithLoss"
   bottom: "loss3/classifier"
   bottom: "label"
   top: "loss3/loss3"
-  name: "loss3/loss3"
-  type: SOFTMAX_LOSS
   loss_weight: 1
 }
-layers {
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
   bottom: "loss3/classifier"
   bottom: "label"
   top: "loss3/top-1"
-  name: "loss3/top-1"
-  type: ACCURACY
   include {
     phase: TEST
   }
 }
-layers {
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
   bottom: "loss3/classifier"
   bottom: "label"
   top: "loss3/top-5"
-  name: "loss3/top-5"
-  type: ACCURACY
-  accuracy_param {
-    top_k: 5
-  }
   include {
     phase: TEST
   }
+  accuracy_param {
+    top_k: 5
+  }
 }

diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 4e494f4..29ccf14 100644 (file)
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -4,9 +4,9 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -15,15 +15,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -32,9 +32,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -43,9 +43,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -55,15 +55,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -72,9 +72,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -83,9 +83,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -94,15 +94,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -112,15 +112,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -130,15 +130,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -147,66 +147,66 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
   inner_product_param {
     num_output: 1000
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }

diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index 00fcc08..c79472e 100644 (file)
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -1,18 +1,16 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    backend: LMDB
-    batch_size: 256
+  include {
+    phase: TRAIN
   }
   transform_param {
+    mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
   }
 # mean pixel / channel-wise mean instead of mean image
 #  transform_param {
@@ -22,22 +20,24 @@ layers {
 #    mean_value: 123
 #    mirror: true
 #  }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    backend: LMDB
-    batch_size: 50
+  include {
+    phase: TEST
   }
   transform_param {
+    mirror: false
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
   }
 # mean pixel / channel-wise mean instead of mean image
 #  transform_param {
@@ -47,17 +47,25 @@ layers {
 #    mean_value: 123
 #    mirror: true
 #  }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -72,15 +80,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -89,9 +97,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -100,15 +108,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -124,15 +136,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -141,9 +153,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -152,15 +164,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -175,21 +191,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -205,21 +225,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -235,15 +259,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -252,15 +276,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -273,30 +301,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -309,30 +341,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -345,17 +381,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"

diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ef75a0a..ea9cf98 100644 (file)
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -4,9 +4,9 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -15,15 +15,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -32,9 +32,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -43,9 +43,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -55,15 +55,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -72,9 +72,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -83,9 +83,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -94,15 +94,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -112,15 +112,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -130,15 +130,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -147,48 +147,48 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
@@ -196,9 +196,9 @@ layers {
   }
 }
 # R-CNN classification layer made from R-CNN ILSVRC13 SVMs.
-layers {
+layer {
   name: "fc-rcnn"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc-rcnn"
   inner_product_param {

diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index aa2ad96..4a924f7 100644 (file)
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -4,15 +4,19 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -27,15 +31,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -44,9 +48,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -55,15 +59,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -79,15 +87,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -96,9 +104,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -107,15 +115,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -130,21 +142,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -160,21 +176,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -190,15 +210,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -207,15 +227,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -228,31 +252,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -265,31 +293,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_flickr"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_flickr"
-  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 20
     weight_filler {
@@ -302,9 +334,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8_flickr"
   top: "prob"
 }

diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index 7155c49..aa9c73e 100644 (file)
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -1,49 +1,57 @@
 name: "FlickrStyleCaffeNet"
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "data/flickr_style/train.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "data/flickr_style/test.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -58,15 +66,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -75,9 +83,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -86,15 +94,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -110,15 +122,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -127,9 +139,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -138,15 +150,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -161,21 +177,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -191,21 +211,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -221,15 +245,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -238,15 +262,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -259,31 +287,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -296,31 +328,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_flickr"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_flickr"
-  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 20
     weight_filler {
@@ -333,17 +369,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8_flickr"
   bottom: "label"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8_flickr"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }