Refactor LayerParameter into per-layer Parameter messages

docs/feature_extraction.md

@@ -22,7 +22,7 @@ We're going to use the images that ship with caffe.
 
     find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt
 
-The `ImagesLayer` we'll use expects labels after each filenames, so let's add a 0 to the end of each line
+The `ImageDataLayer` we'll use expects labels after each filenames, so let's add a 0 to the end of each line
 
     sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt
 
@@ -37,7 +37,7 @@ Download the mean image of the ILSVRC dataset.
 We will use `data/ilsvrc212/imagenet_mean.binaryproto` in the network definition prototxt.
 
 Let's copy and modify the network definition.
-We'll be using the `ImagesLayer`, which will load and resize images for us.
+We'll be using the `ImageDataLayer`, which will load and resize images for us.
 
     cp examples/feature_extraction/imagenet_val.prototxt examples/_temp
 

docs/imagenet_training.md

@@ -55,7 +55,7 @@ Network Definition
 The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
 
     source: "ilvsrc12_train_leveldb"
-    meanfile: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
 
 to point to your own leveldb and image mean. Likewise, do the same for `examples/imagenet/imagenet_val.prototxt`.
 

docs/mnist_prototxt.md

@@ -17,11 +17,11 @@ Writing the Data Layer
 Currently, we will read the MNIST data from the leveldb we created earlier in the demo. This is defined by a data layer:
 
     layers {
-      layer {
-        name: "mnist"
-        type: "data"
+      name: "mnist"
+      type: DATA
+      data_param {
         source: "mnist-train-leveldb"
-        batchsize: 64
+        batch_size: 64
         scale: 0.00390625
       }
       top: "data"
@@ -35,9 +35,11 @@ Writing the Convolution Layer
 Let's define the first convolution layer:
 
     layers {
-      layer {
-        name: "conv1"
-        type: "conv"
+      name: "conv1"
+      type: CONVOLUTION
+      blobs_lr: 1.
+      blobs_lr: 2.
+      convolution_param {
         num_output: 20
         kernelsize: 5
         stride: 1
@@ -47,8 +49,6 @@ Let's define the first convolution layer:
         bias_filler {
           type: "constant"
         }
-        blobs_lr: 1.
-        blobs_lr: 2.
       }
       bottom: "data"
       top: "conv1"
@@ -65,10 +65,10 @@ Writing the Pooling Layer
 Phew. Pooling layers are actually much easier to define:
 
     layers {
-      layer {
-        name: "pool1"
-        type: "pool"
-        kernelsize: 2
+      name: "pool1"
+      type: POOLING
+      pooling_param {
+        kernel_size: 2
         stride: 2
         pool: MAX
       }
@@ -82,21 +82,21 @@ Similarly, you can write up the second convolution and pooling layers. Check `da
 
 Writing the Fully Connected Layer
 ----------------------------------
-Writing a fully connected layers is also simple:
+Writing a fully connected layer is also simple:
 
     layers {
-      layer {
-        name: "ip1"
-        type: "innerproduct"
+      name: "ip1"
+      type: INNER_PRODUCT
+      blobs_lr: 1.
+      blobs_lr: 2.
+      inner_product_param {
         num_output: 500
         weight_filler {
           type: "xavier"
         }
         bias_filler {
           type: "constant"
         }
-        blobs_lr: 1.
-        blobs_lr: 2.
       }
       bottom: "pool2"
       top: "ip1"
@@ -109,10 +109,8 @@ Writing the ReLU Layer
 A ReLU Layer is also simple:
 
     layers {
-      layer {
-        name: "relu1"
-        type: "relu"
-      }
+      name: "relu1"
+      type: RELU
       bottom: "ip1"
       top: "ip1"
     }
@@ -122,18 +120,18 @@ Since ReLU is an element-wise operation, we can do *in-place* operations to save
 After the ReLU layer, we will write another innerproduct layer:
 
     layers {
-      layer {
-        name: "ip2"
-        type: "innerproduct"
+      name: "ip2"
+      type: INNER_PRODUCT
+      blobs_lr: 1.
+      blobs_lr: 2.
+      inner_product_param {
         num_output: 10
         weight_filler {
           type: "xavier"
         }
         bias_filler {
           type: "constant"
         }
-        blobs_lr: 1.
-        blobs_lr: 2.
       }
       bottom: "ip1"
       top: "ip2"
@@ -144,10 +142,8 @@ Writing the Loss Layer
 Finally, we will write the loss!
 
     layers {
-      layer {
-        name: "loss"
-        type: "softmax_loss"
-      }
+      name: "loss"
+      type: SOFTMAX_LOSS
       bottom: "ip2"
       bottom: "label"
     }