Upgrade existing nets using upgrade_net_proto_text tool

Restore comments afterwards

14 files changed, 993 insertions, 730 deletions

diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 8bbd3000..c16f7dca 100644
--- 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 38cc04f4..d45fc61e 100644
--- 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 505158f7..1ad190e1 100644
--- 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 074bb001..23177393 100644
--- 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 83fe8c1a..b0a1cefa 100644
--- 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 5cd605bb..9dd2120a 100644
--- 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 b55b6644..b9ccc1a9 100644
--- 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 b6a75650..f9ef731f 100644
--- 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 395b0f01..7b22bfa1 100644
--- 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 491fad1b..cb42610f 100644
--- 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 2bd960b5..b18fc26c 100644
--- 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 0b33781a..563c7c91 100644
--- 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 8dd42e9c..0e903f85 100644
--- 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 92361c31..8ff864f5 100644
--- 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
+  }
 }