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From zhaoj...@apache.org
Subject [13/50] [abbrv] incubator-singa git commit: SINGA-176 - Add loss and metric base classes
Date Mon, 13 Jun 2016 13:20:06 GMT
SINGA-176 - Add loss and metric base classes

Rename layer.proto to model.proto, which includes proto messages for classes under model/


Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/3171459b
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/3171459b
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/3171459b

Branch: refs/heads/master
Commit: 3171459b0ce39722c42b5eef96ae4892e274cb5c
Parents: a1c3437
Author: Wei Wang <wangwei@comp.nus.edu.sg>
Authored: Thu May 26 14:06:50 2016 +0800
Committer: Wei Wang <wangwei@comp.nus.edu.sg>
Committed: Thu May 26 14:11:18 2016 +0800

----------------------------------------------------------------------
 include/singa/model/layer.h     |   2 +-
 include/singa/model/loss.h      |   2 +-
 include/singa/model/metric.h    |   2 +-
 src/model/layer/cudnn_dropout.h |   1 -
 src/proto/layer.proto           | 852 -----------------------------------
 src/proto/model.proto           | 852 +++++++++++++++++++++++++++++++++++
 6 files changed, 855 insertions(+), 856 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/3171459b/include/singa/model/layer.h
----------------------------------------------------------------------
diff --git a/include/singa/model/layer.h b/include/singa/model/layer.h
index 084c42e..5803295 100644
--- a/include/singa/model/layer.h
+++ b/include/singa/model/layer.h
@@ -24,7 +24,7 @@
 #include <stack>
 #include <utility>
 #include "singa/core/tensor.h"
-#include "singa/proto/layer.pb.h"
+#include "singa/proto/model.pb.h"
 
 namespace singa {
 

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/3171459b/include/singa/model/loss.h
----------------------------------------------------------------------
diff --git a/include/singa/model/loss.h b/include/singa/model/loss.h
index 6c79e7b..6a23067 100644
--- a/include/singa/model/loss.h
+++ b/include/singa/model/loss.h
@@ -18,7 +18,7 @@
 
 #ifndef SINGA_MODEL_LOSS_H_
 #define SINGA_MODEL_LOSS_H_
-#include "singa/proto/layer.pb.h"
+#include "singa/proto/model.pb.h"
 #include "singa/core/tensor.h"
 namespace singa {
 

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/3171459b/include/singa/model/metric.h
----------------------------------------------------------------------
diff --git a/include/singa/model/metric.h b/include/singa/model/metric.h
index 6519028..b99ff0d 100644
--- a/include/singa/model/metric.h
+++ b/include/singa/model/metric.h
@@ -19,7 +19,7 @@
 #ifndef SINGA_MODEL_METRIC_H_
 #define SINGA_MODEL_METRIC_H_
 #include "singa/core/tensor.h"
-#include "singa/proto/layer.pb.h"
+#include "singa/proto/model.pb.h"
 namespace singa {
 
 /// The base metric class, which declares the APIs for computing the performance

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/3171459b/src/model/layer/cudnn_dropout.h
----------------------------------------------------------------------
diff --git a/src/model/layer/cudnn_dropout.h b/src/model/layer/cudnn_dropout.h
index d3b3de6..7cb185b 100644
--- a/src/model/layer/cudnn_dropout.h
+++ b/src/model/layer/cudnn_dropout.h
@@ -30,7 +30,6 @@
 #include <vector>
 
 #include "singa/model/layer.h"
-#include "singa/proto/core.pb.h"
 
 namespace singa {
 class CudnnDropout : public Dropout {

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/3171459b/src/proto/layer.proto
----------------------------------------------------------------------
diff --git a/src/proto/layer.proto b/src/proto/layer.proto
deleted file mode 100644
index 51225ee..0000000
--- a/src/proto/layer.proto
+++ /dev/null
@@ -1,852 +0,0 @@
-/**
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package singa;
-
-/// \file layer.proto is adapted from [Caffe](https://github.com/BVLC/caffe/)'s
-/// proto file with commit id c419f8517b1e1b3d7a07fe212fc6c90a70b519ea. We
-/// use caffe's protocol for configuring layer hyper-parameters for easy
-/// transporting Caffe model into SINGA. Specifically, we do the following
-/// changes:
-/// 1. we rename LayerParameter to LayerConf to differentiate model parameters
-/// 2. we rename xxxParameter to xxxConf for fields of LayerParameter
-/// 3. we comment out some fields (using /*...*/) not used in SINGA layer but
-///    reserve their tags.
-/// 4. we add new fields (commented like 'singa field..') to support our own
-///   functionalities.
-/// TODO(wangwei) write a proto converter to automatically load caffe models
-/// using Python (or C++/Java).
-
-// Specifies the shape (dimensions) of a Blob.
-message BlobShape {
-  repeated int64 dim = 1 [packed = true];
-}
-
-message BlobProto {
-  optional BlobShape shape = 7;
-  repeated float data = 5 [packed = true];
-  repeated float diff = 6 [packed = true];
-  repeated double double_data = 8 [packed = true];
-  repeated double double_diff = 9 [packed = true];
-
-  // 4D dimensions -- deprecated.  Use "shape" instead.
-  optional int32 num = 1 [default = 0];
-  optional int32 channels = 2 [default = 0];
-  optional int32 height = 3 [default = 0];
-  optional int32 width = 4 [default = 0];
-}
-
-message FillerConf {
-  // The filler type.
-  optional string type = 1 [default = 'constant'];
-  optional float value = 2 [default = 0]; // the value in constant filler
-  optional float min = 3 [default = 0]; // the min value in uniform filler
-  optional float max = 4 [default = 1]; // the max value in uniform filler
-  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
-  optional float std = 6 [default = 1]; // the std value in Gaussian filler
-  // The expected number of non-zero output weights for a given input in
-  // Gaussian filler -- the default -1 means don't perform sparsification.
-  /* optional int32 sparse = 7 [default = -1]; */
-  // Normalize the filler variance by fan_in, fan_out, or their average.
-  // Applies to 'xavier' and 'msra' fillers.
-  enum VarianceNorm {
-    FAN_IN = 0;
-    FAN_OUT = 1;
-    AVERAGE = 2;
-  }
-  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
-}
-
-// Specifies training parameters (multipliers on global learning constants,
-// and the name and other settings used for weight sharing).
-message ParamSpec {
-  // The names of the parameter blobs -- useful for sharing parameters among
-  // layers, but never required otherwise.  To share a parameter between two
-  // layers, give it a (non-empty) name.
-  optional string name = 1;
-
-  // Whether to require shared weights to have the same shape, or just the same
-  // count -- defaults to STRICT if unspecified.
-  /*
-  optional DimCheckMode share_mode = 2;
-  enum DimCheckMode {
-    // STRICT (default) requires that num, channels, height, width each match.
-    STRICT = 0;
-    // PERMISSIVE requires only the count (num*channels*height*width) to match.
-    PERMISSIVE = 1;
-  }
-  */
-
-  // The multiplier on the global learning rate for this parameter.
-  optional float lr_mult = 3 [default = 1.0];
-
-  // The multiplier on the global weight decay for this parameter.
-  optional float decay_mult = 4 [default = 1.0];
-
-  // SINGA uses this filed internally. Users just configure the fillers in
-  // Layer specific conf message as caffe (style).
-  optional FillerConf filler = 20;
-}
-
-enum Phase {
-  kTrain = 4;
-  kEval = 8;
-}
-// NOTE
-// Update the next available ID when you add a new LayerConf field.
-//
-// LayerConf next available layer-specific ID: 139 (last added: tile_param)
-message LayerConf {
-  optional string name = 1; // the layer name
-  optional string type = 2; // the layer type
-  /* repeated string bottom = 3; // the name of each bottom blob */
-  /* repeated string top = 4; // the name of each top blob */
-
-  // The train / test phase for computation.
-  // optional Phase phase = 10;
-
-  // The amount of weight to assign each top blob in the objective.
-  // Each layer assigns a default value, usually of either 0 or 1,
-  // to each top blob.
-  /* repeated float loss_weight = 5; */
-
-  // Specifies training parameters (multipliers on global learning constants,
-  // and the name and other settings used for weight sharing).
-  repeated ParamSpec param = 6;
-
-  // The blobs containing the numeric parameters of the layer.
-  repeated BlobProto blobs = 7;
-
-  // Specifies on which bottoms the backpropagation should be skipped.
-  // The size must be either 0 or equal to the number of bottoms.
-  /* repeated bool propagate_down = 11; */
-
-  // Rules controlling whether and when a layer is included in the network,
-  // based on the current NetState.  You may specify a non-zero number of rules
-  // to include OR exclude, but not both.  If no include or exclude rules are
-  // specified, the layer is always included.  If the current NetState meets
-  // ANY (i.e., one or more) of the specified rules, the layer is
-  // included/excluded.
-  /* repeated NetStateRule include = 8; */
-  /* repeated NetStateRule exclude = 9; */
-
-  // Confs for data pre-processing.
-  /* optional TransformationConf transform_param = 100; */
-
-  // Confs shared by loss layers.
-  /* optional LossConf loss_param = 101; */
-
-  // Layer type-specific parameters.
-  //
-  // Note: certain layers may have more than one computational engine
-  // for their implementation. These layers include an Engine type and
-  // engine parameter for selecting the implementation.
-  // The default for the engine is set by the ENGINE switch at compile-time.
-  //optional AccuracyConf accuracy_conf = 102;
-  optional ArgMaxConf argmax_conf = 103;
-  optional ConcatConf concat_conf = 104;
-  optional ContrastiveLossConf contrastive_loss_conf = 105;
-  optional ConvolutionConf convolution_conf = 106;
-  // optional DataConf data_conf = 107;
-  optional DropoutConf dropout_conf = 108;
-  // optional DummyDataConf dummy_data_conf = 109;
-  optional EltwiseConf eltwise_conf = 110;
-  optional EmbedConf embed_conf = 137;
-  optional ExpConf exp_conf = 111;
-  optional FlattenConf flatten_conf = 135;
-  // optional HDF5DataConf hdf5_data_conf = 112;
-  // optional HDF5OutputConf hdf5_output_conf = 113;
-  optional HingeLossConf hinge_loss_conf = 114;
-  // optional ImageDataConf image_data_conf = 115;
-  optional InfogainLossConf infogain_loss_conf = 116;
-  optional InnerProductConf inner_product_conf = 117;
-  optional LogConf log_conf = 134;
-  optional LRNConf lrn_conf = 118;
-  // Used in SINGA
-  optional MetricConf metric_conf = 200;
-  // optional MemoryDataConf memory_data_conf = 119;
-  optional MVNConf mvn_conf = 120;
-  optional PoolingConf pooling_conf = 121;
-  optional PowerConf power_conf = 122;
-  optional PReLUConf prelu_conf = 131;
-  // optional PythonConf python_conf = 130;
-  optional ReductionConf reduction_conf = 136;
-  optional ReLUConf relu_conf = 123;
-  optional ReshapeConf reshape_conf = 133;
-  optional SigmoidConf sigmoid_conf = 124;
-  optional SoftmaxConf softmax_conf = 125;
-  optional SPPConf spp_conf = 132;
-  optional SliceConf slice_conf = 126;
-  optional TanHConf tanh_conf = 127;
-  optional ThresholdConf threshold_conf = 128;
-  optional TileConf tile_conf = 138;
-  //optional WindowDataConf window_data_conf = 129;
-}
-
-// Message that stores hyper-parameters used to apply transformation
-// to the data layer's data
-/*
-message TransformationConf {
-  // For data pre-processing, we can do simple scaling and subtracting the
-  // data mean, if provided. Note that the mean subtraction is always carried
-  // out before scaling.
-  optional float scale = 1 [default = 1];
-  // Specify if we want to randomly mirror data.
-  optional bool mirror = 2 [default = false];
-  // Specify if we would like to randomly crop an image.
-  optional uint32 crop_size = 3 [default = 0];
-  // mean_file and mean_value cannot be specified at the same time
-  optional string mean_file = 4;
-  // if specified can be repeated once (would substract it from all the channels)
-  // or can be repeated the same number of times as channels
-  // (would subtract them from the corresponding channel)
-  repeated float mean_value = 5;
-  // Force the decoded image to have 3 color channels.
-  optional bool force_color = 6 [default = false];
-  // Force the decoded image to have 1 color channels.
-  optional bool force_gray = 7 [default = false];
-}
-*/
-
-// Message that stores hyper-parameters shared by loss layers
-message LossConf {
-  // If specified, ignore instances with the given label.
-  optional int32 ignore_label = 1;
-  // If true, normalize each batch across all instances (including spatial
-  // dimesions, but not ignored instances); else, divide by batch size only.
-  optional bool normalize = 2 [default = true];
-}
-
-message MetricConf {
-  // When computing accuracy, count as correct by comparing the true label to
-  // the top k scoring classes.  By default, only compare to the top scoring
-  // class (i.e. argmax).
-  optional uint32 top_k = 1 [default = 1];
-
-  // The "label" axis of the prediction blob, whose argmax corresponds to the
-  // predicted label -- may be negative to index from the end (e.g., -1 for the
-  // last axis).  For example, if axis == 1 and the predictions are
-  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
-  // labels with integer values in {0, 1, ..., C-1}.
-  optional int32 axis = 2 [default = 1];
-
-  // If specified, ignore instances with the given label.
-  optional int32 ignore_label = 3;
-}
-// Messages that store hyper-parameters used by individual layer types follow, in
-// alphabetical order.
-
-
-
-message ArgMaxConf {
-  // If true produce pairs (argmax, maxval)
-  optional bool out_max_val = 1 [default = false];
-  optional uint32 top_k = 2 [default = 1];
-  // The axis along which to maximise -- may be negative to index from the
-  // end (e.g., -1 for the last axis).
-  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
-  // for each index of the first / num dimension.
-  optional int32 axis = 3;
-}
-
-message ConcatConf {
-  // The axis along which to concatenate -- may be negative to index from the
-  // end (e.g., -1 for the last axis).  Other axes must have the
-  // same dimension for all the bottom blobs.
-  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
-  optional int32 axis = 2 [default = 1];
-
-  // DEPRECATED: alias for "axis" -- does not support negative indexing.
-  optional uint32 concat_dim = 1 [default = 1];
-}
-
-message ContrastiveLossConf {
-  // margin for dissimilar pair
-  optional float margin = 1 [default = 1.0];
-  // The first implementation of this cost did not exactly match the cost of
-  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
-  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
-  // Hadsell paper. New models should probably use this version.
-  // legacy_version = true uses (margin - d^2). This is kept to support /
-  // reproduce existing models and results
-  optional bool legacy_version = 2 [default = false];
-}
-
-message ConvolutionConf {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  optional bool bias_term = 2 [default = true]; // whether to have bias terms
-
-  // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in all spatial dimensions, or once per spatial dimension.
-  repeated uint32 pad = 3; // The padding size; defaults to 0
-  repeated uint32 kernel_size = 4; // The kernel size
-  repeated uint32 stride = 6; // The stride; defaults to 1
-
-  // For 2D convolution only, the *_h and *_w versions may also be used to
-  // specify both spatial dimensions.
-  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
-  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
-  optional uint32 kernel_h = 11; // The kernel height (2D only)
-  optional uint32 kernel_w = 12; // The kernel width (2D only)
-  optional uint32 stride_h = 13; // The stride height (2D only)
-  optional uint32 stride_w = 14; // The stride width (2D only)
-
-  optional uint32 group = 5 [default = 1]; // The group size for group conv
-
-  optional FillerConf weight_filler = 7; // The filler for the weight
-  optional FillerConf bias_filler = 8; // The filler for the bias
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 15 [default = DEFAULT];
-
-  // The axis to interpret as "channels" when performing convolution.
-  // Preceding dimensions are treated as independent inputs;
-  // succeeding dimensions are treated as "spatial".
-  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
-  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
-  // groups g>1) filters across the spatial axes (H, W) of the input.
-  // With (N, C, D, H, W) inputs, and axis == 1, we perform
-  // N independent 3D convolutions, sliding (C/g)-channels
-  // filters across the spatial axes (D, H, W) of the input.
-  optional int32 axis = 16 [default = 1];
-
-  // Whether to force use of the general ND convolution, even if a specific
-  // implementation for blobs of the appropriate number of spatial dimensions
-  // is available. (Currently, there is only a 2D-specific convolution
-  // implementation; for input blobs with num_axes != 2, this option is
-  // ignored and the ND implementation will be used.)
-  optional bool force_nd_im2col = 17 [default = false];
-}
-
-/*
-message DataConf {
-  enum DB {
-    LEVELDB = 0;
-    LMDB = 1;
-  }
-  // Specify the data source.
-  optional string source = 1;
-  // Specify the batch size.
-  optional uint32 batch_size = 4;
-  // The rand_skip variable is for the data layer to skip a few data points
-  // to avoid all asynchronous sgd clients to start at the same point. The skip
-  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the database.
-  // DEPRECATED. Each solver accesses a different subset of the database.
-  optional uint32 rand_skip = 7 [default = 0];
-  optional DB backend = 8 [default = LEVELDB];
-  // DEPRECATED. See TransformationConf. For data pre-processing, we can do
-  // simple scaling and subtracting the data mean, if provided. Note that the
-  // mean subtraction is always carried out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
-  // DEPRECATED. See TransformationConf. Specify if we would like to randomly
-  // crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // DEPRECATED. See TransformationConf. Specify if we want to randomly mirror
-  // data.
-  optional bool mirror = 6 [default = false];
-  // Force the encoded image to have 3 color channels
-  optional bool force_encoded_color = 9 [default = false];
-  // Prefetch queue (Number of batches to prefetch to host memory, increase if
-  // data access bandwidth varies).
-  optional uint32 prefetch = 10 [default = 4];
-}
-*/
-
-message DropoutConf {
-  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
-}
-
-// DummyDataLayer fills any number of arbitrarily shaped blobs with random
-// (or constant) data generated by "Fillers" (see "message FillerConf").
-message DummyDataConf {
-  // This layer produces N >= 1 top blobs.  DummyDataConf must specify 1 or N
-  // shape fields, and 0, 1 or N data_fillers.
-  //
-  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
-  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
-  // specified, the ith is applied to the ith top blob.
-  repeated FillerConf data_filler = 1;
-  repeated BlobShape shape = 6;
-
-  // 4D dimensions -- deprecated.  Use "shape" instead.
-  repeated uint32 num = 2;
-  repeated uint32 channels = 3;
-  repeated uint32 height = 4;
-  repeated uint32 width = 5;
-}
-
-message EltwiseConf {
-  enum EltwiseOp {
-    PROD = 0;
-    SUM = 1;
-    MAX = 2;
-  }
-  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
-  repeated float coeff = 2; // blob-wise coefficient for SUM operation
-
-  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
-  // of computing the gradient for the PROD operation. (No effect for SUM op.)
-  optional bool stable_prod_grad = 3 [default = true];
-}
-
-// Message that stores hyper-parameters used by EmbedLayer
-message EmbedConf {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  // The input is given as integers to be interpreted as one-hot
-  // vector indices with dimension num_input.  Hence num_input should be
-  // 1 greater than the maximum possible input value.
-  optional uint32 input_dim = 2;
-
-  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
-  optional FillerConf weight_filler = 4; // The filler for the weight
-  optional FillerConf bias_filler = 5; // The filler for the bias
-
-}
-
-// Message that stores hyper-parameters used by ExpLayer
-message ExpConf {
-  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
-  // Or if base is set to the default (-1), base is set to e,
-  // so y = exp(shift + scale * x).
-  optional float base = 1 [default = -1.0];
-  optional float scale = 2 [default = 1.0];
-  optional float shift = 3 [default = 0.0];
-}
-
-/// Message that stores hyper-parameters used by FlattenLayer
-message FlattenConf {
-  // The first axis to flatten: all preceding axes are retained in the output.
-  // May be negative to index from the end (e.g., -1 for the last axis).
-  optional int32 axis = 1 [default = 1];
-
-  // The last axis to flatten: all following axes are retained in the output.
-  // May be negative to index from the end (e.g., the default -1 for the last
-  // axis).
-  optional int32 end_axis = 2 [default = -1];
-}
-
-/*
-// Message that stores hyper-parameters used by HDF5DataLayer
-message HDF5DataConf {
-  // Specify the data source.
-  optional string source = 1;
-  // Specify the batch size.
-  optional uint32 batch_size = 2;
-
-  // Specify whether to shuffle the data.
-  // If shuffle == true, the ordering of the HDF5 files is shuffled,
-  // and the ordering of data within any given HDF5 file is shuffled,
-  // but data between different files are not interleaved; all of a file's
-  // data are output (in a random order) before moving onto another file.
-  optional bool shuffle = 3 [default = false];
-}
-
-message HDF5OutputConf {
-  optional string file_name = 1;
-}
-*/
-
-message HingeLossConf {
-  enum Norm {
-    L1 = 1;
-    L2 = 2;
-  }
-  // Specify the Norm to use L1 or L2
-  optional Norm norm = 1 [default = L1];
-}
-
-/*
-message ImageDataConf {
-  // Specify the data source.
-  optional string source = 1;
-  // Specify the batch size.
-  optional uint32 batch_size = 4 [default = 1];
-  // The rand_skip variable is for the data layer to skip a few data points
-  // to avoid all asynchronous sgd clients to start at the same point. The skip
-  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the database.
-  optional uint32 rand_skip = 7 [default = 0];
-  // Whether or not ImageLayer should shuffle the list of files at every epoch.
-  optional bool shuffle = 8 [default = false];
-  // It will also resize images if new_height or new_width are not zero.
-  optional uint32 new_height = 9 [default = 0];
-  optional uint32 new_width = 10 [default = 0];
-  // Specify if the images are color or gray
-  optional bool is_color = 11 [default = true];
-  // DEPRECATED. See TransformationConf. For data pre-processing, we can do
-  // simple scaling and subtracting the data mean, if provided. Note that the
-  // mean subtraction is always carried out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
-  // DEPRECATED. See TransformationConf. Specify if we would like to randomly
-  // crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // DEPRECATED. See TransformationConf. Specify if we want to randomly mirror
-  // data.
-  optional bool mirror = 6 [default = false];
-  optional string root_folder = 12 [default = ""];
-}
-*/
-
-message InfogainLossConf {
-  // Specify the infogain matrix source.
-  optional string source = 1;
-}
-
-message InnerProductConf {
-  optional uint32 num_output = 1; // The number of outputs for the layer
-  optional bool bias_term = 2 [default = true]; // whether to have bias terms
-  optional FillerConf weight_filler = 3; // The filler for the weight
-  optional FillerConf bias_filler = 4; // The filler for the bias
-
-  // The first axis to be lumped into a single inner product computation;
-  // all preceding axes are retained in the output.
-  // May be negative to index from the end (e.g., -1 for the last axis).
-  optional int32 axis = 5 [default = 1];
-}
-
-// Message that stores hyper-parameters used by LogLayer
-message LogConf {
-  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
-  // Or if base is set to the default (-1), base is set to e,
-  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
-  optional float base = 1 [default = -1.0];
-  optional float scale = 2 [default = 1.0];
-  optional float shift = 3 [default = 0.0];
-}
-
-// Message that stores hyper-parameters used by LRNLayer
-message LRNConf {
-  optional uint32 local_size = 1 [default = 5];
-  optional float alpha = 2 [default = 1.];
-  optional float beta = 3 [default = 0.75];
-  enum NormRegion {
-    ACROSS_CHANNELS = 0;
-    WITHIN_CHANNEL = 1;
-  }
-  optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
-  optional float k = 5 [default = 1.];
-}
-
-message MemoryDataConf {
-  optional uint32 batch_size = 1;
-  optional uint32 channels = 2;
-  optional uint32 height = 3;
-  optional uint32 width = 4;
-}
-
-message MVNConf {
-  // This parameter can be set to false to normalize mean only
-  optional bool normalize_variance = 1 [default = true];
-
-  // This parameter can be set to true to perform DNN-like MVN
-  optional bool across_channels = 2 [default = false];
-
-  // Epsilon for not dividing by zero while normalizing variance
-  optional float eps = 3 [default = 1e-9];
-}
-
-message PoolingConf {
-  enum PoolMethod {
-    MAX = 0;
-    AVE = 1;
-    STOCHASTIC = 2;
-  }
-  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
-  // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in height and width or as Y, X pairs.
-  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
-  optional uint32 pad_h = 9 [default = 0]; // The padding height
-  optional uint32 pad_w = 10 [default = 0]; // The padding width
-  optional uint32 kernel_size = 2; // The kernel size (square)
-  optional uint32 kernel_h = 5; // The kernel height
-  optional uint32 kernel_w = 6; // The kernel width
-  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
-  optional uint32 stride_h = 7; // The stride height
-  optional uint32 stride_w = 8; // The stride width
-  /*
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 11 [default = DEFAULT];
-  */
-  // If global_pooling then it will pool over the size of the bottom by doing
-  // kernel_h = bottom->height and kernel_w = bottom->width
-  optional bool global_pooling = 12 [default = false];
-}
-
-message PowerConf {
-  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
-  optional float power = 1 [default = 1.0];
-  optional float scale = 2 [default = 1.0];
-  optional float shift = 3 [default = 0.0];
-}
-/*
-message PythonConf {
-  optional string module = 1;
-  optional string layer = 2;
-  // This value is set to the attribute `param_str` of the `PythonLayer` object
-  // in Python before calling the `setup()` method. This could be a number,
-  // string, dictionary in Python dict format, JSON, etc. You may parse this
-  // string in `setup` method and use it in `forward` and `backward`.
-  optional string param_str = 3 [default = ''];
-  // Whether this PythonLayer is shared among worker solvers during data parallelism.
-  // If true, each worker solver sequentially run forward from this layer.
-  // This value should be set true if you are using it as a data layer.
-  optional bool share_in_parallel = 4 [default = false];
-}
-*/
-
-// Message that stores hyper-parameters used by ReductionLayer
-message ReductionConf {
-  enum ReductionOp {
-    SUM = 1;
-    ASUM = 2;
-    SUMSQ = 3;
-    MEAN = 4;
-  }
-
-  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
-
-  // The first axis to reduce to a scalar -- may be negative to index from the
-  // end (e.g., -1 for the last axis).
-  // (Currently, only reduction along ALL "tail" axes is supported; reduction
-  // of axis M through N, where N < num_axes - 1, is unsupported.)
-  // Suppose we have an n-axis bottom Blob with shape:
-  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
-  // If axis == m, the output Blob will have shape
-  //     (d0, d1, d2, ..., d(m-1)),
-  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
-  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
-  // If axis == 0 (the default), the output Blob always has the empty shape
-  // (count 1), performing reduction across the entire input --
-  // often useful for creating new loss functions.
-  optional int32 axis = 2 [default = 0];
-
-  optional float coeff = 3 [default = 1.0]; // coefficient for output
-}
-
-// Message that stores hyper-parameters used by ReLULayer
-message ReLUConf {
-  // Allow non-zero slope for negative inputs to speed up optimization
-  // Described in:
-  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
-  // improve neural network acoustic models. In ICML Workshop on Deep Learning
-  // for Audio, Speech, and Language Processing.
-  optional float negative_slope = 1 [default = 0];
-  /*
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 2 [default = DEFAULT];
-  */
-}
-
-message ReshapeConf {
-  // Specify the output dimensions. If some of the dimensions are set to 0,
-  // the corresponding dimension from the bottom layer is used (unchanged).
-  // Exactly one dimension may be set to -1, in which case its value is
-  // inferred from the count of the bottom blob and the remaining dimensions.
-  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
-  //
-  //   layer {
-  //     type: "Reshape" bottom: "input" top: "output"
-  //     reshape_param { ... }
-  //   }
-  //
-  // If "input" is 2D with shape 2 x 8, then the following reshape_param
-  // specifications are all equivalent, producing a 3D blob "output" with shape
-  // 2 x 2 x 4:
-  //
-  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
-  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
-  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
-  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
-  //
-  optional BlobShape shape = 1;
-
-  // axis and num_axes control the portion of the bottom blob's shape that are
-  // replaced by (included in) the reshape. By default (axis == 0 and
-  // num_axes == -1), the entire bottom blob shape is included in the reshape,
-  // and hence the shape field must specify the entire output shape.
-  //
-  // axis may be non-zero to retain some portion of the beginning of the input
-  // shape (and may be negative to index from the end; e.g., -1 to begin the
-  // reshape after the last axis, including nothing in the reshape,
-  // -2 to include only the last axis, etc.).
-  //
-  // For example, suppose "input" is a 2D blob with shape 2 x 8.
-  // Then the following ReshapeLayer specifications are all equivalent,
-  // producing a blob "output" with shape 2 x 2 x 4:
-  //
-  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
-  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
-  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
-  //
-  // num_axes specifies the extent of the reshape.
-  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
-  // input axes in the range [axis, axis+num_axes].
-  // num_axes may also be -1, the default, to include all remaining axes
-  // (starting from axis).
-  //
-  // For example, suppose "input" is a 2D blob with shape 2 x 8.
-  // Then the following ReshapeLayer specifications are equivalent,
-  // producing a blob "output" with shape 1 x 2 x 8.
-  //
-  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
-  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
-  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
-  //
-  // On the other hand, these would produce output blob shape 2 x 1 x 8:
-  //
-  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
-  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
-  //
-  optional int32 axis = 2 [default = 0];
-  optional int32 num_axes = 3 [default = -1];
-}
-
-message SigmoidConf {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
-}
-
-message SliceConf {
-  // The axis along which to slice -- may be negative to index from the end
-  // (e.g., -1 for the last axis).
-  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
-  optional int32 axis = 3 [default = 1];
-  repeated uint32 slice_point = 2;
-
-  // DEPRECATED: alias for "axis" -- does not support negative indexing.
-  optional uint32 slice_dim = 1 [default = 1];
-}
-
-// Message that stores hyper-parameters used by SoftmaxLayer, SoftmaxWithLossLayer
-message SoftmaxConf {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
-
-  // The axis along which to perform the softmax -- may be negative to index
-  // from the end (e.g., -1 for the last axis).
-  // Any other axes will be evaluated as independent softmaxes.
-  optional int32 axis = 2 [default = 1];
-}
-
-message TanHConf {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 1 [default = DEFAULT];
-}
-
-// Message that stores hyper-parameters used by TileLayer
-message TileConf {
-  // The index of the axis to tile.
-  optional int32 axis = 1 [default = 1];
-
-  // The number of copies (tiles) of the blob to output.
-  optional int32 tiles = 2;
-}
-
-// Message that stores hyper-parameters used by ThresholdLayer
-message ThresholdConf {
-  optional float threshold = 1 [default = 0]; // Strictly positive values
-}
-
-/*
-message WindowDataConf {
-  // Specify the data source.
-  optional string source = 1;
-  // For data pre-processing, we can do simple scaling and subtracting the
-  // data mean, if provided. Note that the mean subtraction is always carried
-  // out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
-  // Specify the batch size.
-  optional uint32 batch_size = 4;
-  // Specify if we would like to randomly crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // Specify if we want to randomly mirror data.
-  optional bool mirror = 6 [default = false];
-  // Foreground (object) overlap threshold
-  optional float fg_threshold = 7 [default = 0.5];
-  // Background (non-object) overlap threshold
-  optional float bg_threshold = 8 [default = 0.5];
-  // Fraction of batch that should be foreground objects
-  optional float fg_fraction = 9 [default = 0.25];
-  // Amount of contextual padding to add around a window
-  // (used only by the window_data_layer)
-  optional uint32 context_pad = 10 [default = 0];
-  // Mode for cropping out a detection window
-  // warp: cropped window is warped to a fixed size and aspect ratio
-  // square: the tightest square around the window is cropped
-  optional string crop_mode = 11 [default = "warp"];
-  // cache_images: will load all images in memory for faster access
-  optional bool cache_images = 12 [default = false];
-  // append root_folder to locate images
-  optional string root_folder = 13 [default = ""];
-}
-*/
-
-message SPPConf {
-  enum PoolMethod {
-    MAX = 0;
-    AVE = 1;
-    STOCHASTIC = 2;
-  }
-  optional uint32 pyramid_height = 1;
-  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
-  /*
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 6 [default = DEFAULT];
-  */
-}
-
-message PReLUConf {
-  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
-  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
-
-  // Initial value of a_i. Default is a_i=0.25 for all i.
-  optional FillerConf filler = 1;
-  // Whether or not slope paramters are shared across channels.
-  optional bool channel_shared = 2 [default = false];
-}

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/3171459b/src/proto/model.proto
----------------------------------------------------------------------
diff --git a/src/proto/model.proto b/src/proto/model.proto
new file mode 100644
index 0000000..51225ee
--- /dev/null
+++ b/src/proto/model.proto
@@ -0,0 +1,852 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package singa;
+
+/// \file layer.proto is adapted from [Caffe](https://github.com/BVLC/caffe/)'s
+/// proto file with commit id c419f8517b1e1b3d7a07fe212fc6c90a70b519ea. We
+/// use caffe's protocol for configuring layer hyper-parameters for easy
+/// transporting Caffe model into SINGA. Specifically, we do the following
+/// changes:
+/// 1. we rename LayerParameter to LayerConf to differentiate model parameters
+/// 2. we rename xxxParameter to xxxConf for fields of LayerParameter
+/// 3. we comment out some fields (using /*...*/) not used in SINGA layer but
+///    reserve their tags.
+/// 4. we add new fields (commented like 'singa field..') to support our own
+///   functionalities.
+/// TODO(wangwei) write a proto converter to automatically load caffe models
+/// using Python (or C++/Java).
+
+// Specifies the shape (dimensions) of a Blob.
+message BlobShape {
+  repeated int64 dim = 1 [packed = true];
+}
+
+message BlobProto {
+  optional BlobShape shape = 7;
+  repeated float data = 5 [packed = true];
+  repeated float diff = 6 [packed = true];
+  repeated double double_data = 8 [packed = true];
+  repeated double double_diff = 9 [packed = true];
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
+  optional int32 num = 1 [default = 0];
+  optional int32 channels = 2 [default = 0];
+  optional int32 height = 3 [default = 0];
+  optional int32 width = 4 [default = 0];
+}
+
+message FillerConf {
+  // The filler type.
+  optional string type = 1 [default = 'constant'];
+  optional float value = 2 [default = 0]; // the value in constant filler
+  optional float min = 3 [default = 0]; // the min value in uniform filler
+  optional float max = 4 [default = 1]; // the max value in uniform filler
+  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
+  optional float std = 6 [default = 1]; // the std value in Gaussian filler
+  // The expected number of non-zero output weights for a given input in
+  // Gaussian filler -- the default -1 means don't perform sparsification.
+  /* optional int32 sparse = 7 [default = -1]; */
+  // Normalize the filler variance by fan_in, fan_out, or their average.
+  // Applies to 'xavier' and 'msra' fillers.
+  enum VarianceNorm {
+    FAN_IN = 0;
+    FAN_OUT = 1;
+    AVERAGE = 2;
+  }
+  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
+}
+
+// Specifies training parameters (multipliers on global learning constants,
+// and the name and other settings used for weight sharing).
+message ParamSpec {
+  // The names of the parameter blobs -- useful for sharing parameters among
+  // layers, but never required otherwise.  To share a parameter between two
+  // layers, give it a (non-empty) name.
+  optional string name = 1;
+
+  // Whether to require shared weights to have the same shape, or just the same
+  // count -- defaults to STRICT if unspecified.
+  /*
+  optional DimCheckMode share_mode = 2;
+  enum DimCheckMode {
+    // STRICT (default) requires that num, channels, height, width each match.
+    STRICT = 0;
+    // PERMISSIVE requires only the count (num*channels*height*width) to match.
+    PERMISSIVE = 1;
+  }
+  */
+
+  // The multiplier on the global learning rate for this parameter.
+  optional float lr_mult = 3 [default = 1.0];
+
+  // The multiplier on the global weight decay for this parameter.
+  optional float decay_mult = 4 [default = 1.0];
+
+  // SINGA uses this filed internally. Users just configure the fillers in
+  // Layer specific conf message as caffe (style).
+  optional FillerConf filler = 20;
+}
+
+enum Phase {
+  kTrain = 4;
+  kEval = 8;
+}
+// NOTE
+// Update the next available ID when you add a new LayerConf field.
+//
+// LayerConf next available layer-specific ID: 139 (last added: tile_param)
+message LayerConf {
+  optional string name = 1; // the layer name
+  optional string type = 2; // the layer type
+  /* repeated string bottom = 3; // the name of each bottom blob */
+  /* repeated string top = 4; // the name of each top blob */
+
+  // The train / test phase for computation.
+  // optional Phase phase = 10;
+
+  // The amount of weight to assign each top blob in the objective.
+  // Each layer assigns a default value, usually of either 0 or 1,
+  // to each top blob.
+  /* repeated float loss_weight = 5; */
+
+  // Specifies training parameters (multipliers on global learning constants,
+  // and the name and other settings used for weight sharing).
+  repeated ParamSpec param = 6;
+
+  // The blobs containing the numeric parameters of the layer.
+  repeated BlobProto blobs = 7;
+
+  // Specifies on which bottoms the backpropagation should be skipped.
+  // The size must be either 0 or equal to the number of bottoms.
+  /* repeated bool propagate_down = 11; */
+
+  // Rules controlling whether and when a layer is included in the network,
+  // based on the current NetState.  You may specify a non-zero number of rules
+  // to include OR exclude, but not both.  If no include or exclude rules are
+  // specified, the layer is always included.  If the current NetState meets
+  // ANY (i.e., one or more) of the specified rules, the layer is
+  // included/excluded.
+  /* repeated NetStateRule include = 8; */
+  /* repeated NetStateRule exclude = 9; */
+
+  // Confs for data pre-processing.
+  /* optional TransformationConf transform_param = 100; */
+
+  // Confs shared by loss layers.
+  /* optional LossConf loss_param = 101; */
+
+  // Layer type-specific parameters.
+  //
+  // Note: certain layers may have more than one computational engine
+  // for their implementation. These layers include an Engine type and
+  // engine parameter for selecting the implementation.
+  // The default for the engine is set by the ENGINE switch at compile-time.
+  //optional AccuracyConf accuracy_conf = 102;
+  optional ArgMaxConf argmax_conf = 103;
+  optional ConcatConf concat_conf = 104;
+  optional ContrastiveLossConf contrastive_loss_conf = 105;
+  optional ConvolutionConf convolution_conf = 106;
+  // optional DataConf data_conf = 107;
+  optional DropoutConf dropout_conf = 108;
+  // optional DummyDataConf dummy_data_conf = 109;
+  optional EltwiseConf eltwise_conf = 110;
+  optional EmbedConf embed_conf = 137;
+  optional ExpConf exp_conf = 111;
+  optional FlattenConf flatten_conf = 135;
+  // optional HDF5DataConf hdf5_data_conf = 112;
+  // optional HDF5OutputConf hdf5_output_conf = 113;
+  optional HingeLossConf hinge_loss_conf = 114;
+  // optional ImageDataConf image_data_conf = 115;
+  optional InfogainLossConf infogain_loss_conf = 116;
+  optional InnerProductConf inner_product_conf = 117;
+  optional LogConf log_conf = 134;
+  optional LRNConf lrn_conf = 118;
+  // Used in SINGA
+  optional MetricConf metric_conf = 200;
+  // optional MemoryDataConf memory_data_conf = 119;
+  optional MVNConf mvn_conf = 120;
+  optional PoolingConf pooling_conf = 121;
+  optional PowerConf power_conf = 122;
+  optional PReLUConf prelu_conf = 131;
+  // optional PythonConf python_conf = 130;
+  optional ReductionConf reduction_conf = 136;
+  optional ReLUConf relu_conf = 123;
+  optional ReshapeConf reshape_conf = 133;
+  optional SigmoidConf sigmoid_conf = 124;
+  optional SoftmaxConf softmax_conf = 125;
+  optional SPPConf spp_conf = 132;
+  optional SliceConf slice_conf = 126;
+  optional TanHConf tanh_conf = 127;
+  optional ThresholdConf threshold_conf = 128;
+  optional TileConf tile_conf = 138;
+  //optional WindowDataConf window_data_conf = 129;
+}
+
+// Message that stores hyper-parameters used to apply transformation
+// to the data layer's data
+/*
+message TransformationConf {
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 1 [default = 1];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 2 [default = false];
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 3 [default = 0];
+  // mean_file and mean_value cannot be specified at the same time
+  optional string mean_file = 4;
+  // if specified can be repeated once (would substract it from all the channels)
+  // or can be repeated the same number of times as channels
+  // (would subtract them from the corresponding channel)
+  repeated float mean_value = 5;
+  // Force the decoded image to have 3 color channels.
+  optional bool force_color = 6 [default = false];
+  // Force the decoded image to have 1 color channels.
+  optional bool force_gray = 7 [default = false];
+}
+*/
+
+// Message that stores hyper-parameters shared by loss layers
+message LossConf {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+  // If true, normalize each batch across all instances (including spatial
+  // dimesions, but not ignored instances); else, divide by batch size only.
+  optional bool normalize = 2 [default = true];
+}
+
+message MetricConf {
+  // When computing accuracy, count as correct by comparing the true label to
+  // the top k scoring classes.  By default, only compare to the top scoring
+  // class (i.e. argmax).
+  optional uint32 top_k = 1 [default = 1];
+
+  // The "label" axis of the prediction blob, whose argmax corresponds to the
+  // predicted label -- may be negative to index from the end (e.g., -1 for the
+  // last axis).  For example, if axis == 1 and the predictions are
+  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+  // labels with integer values in {0, 1, ..., C-1}.
+  optional int32 axis = 2 [default = 1];
+
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 3;
+}
+// Messages that store hyper-parameters used by individual layer types follow, in
+// alphabetical order.
+
+
+
+message ArgMaxConf {
+  // If true produce pairs (argmax, maxval)
+  optional bool out_max_val = 1 [default = false];
+  optional uint32 top_k = 2 [default = 1];
+  // The axis along which to maximise -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+  // for each index of the first / num dimension.
+  optional int32 axis = 3;
+}
+
+message ConcatConf {
+  // The axis along which to concatenate -- may be negative to index from the
+  // end (e.g., -1 for the last axis).  Other axes must have the
+  // same dimension for all the bottom blobs.
+  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 2 [default = 1];
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 concat_dim = 1 [default = 1];
+}
+
+message ContrastiveLossConf {
+  // margin for dissimilar pair
+  optional float margin = 1 [default = 1.0];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [default = false];
+}
+
+message ConvolutionConf {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
+  optional uint32 group = 5 [default = 1]; // The group size for group conv
+
+  optional FillerConf weight_filler = 7; // The filler for the weight
+  optional FillerConf bias_filler = 8; // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
+}
+
+/*
+message DataConf {
+  enum DB {
+    LEVELDB = 0;
+    LMDB = 1;
+  }
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  // DEPRECATED. Each solver accesses a different subset of the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  optional DB backend = 8 [default = LEVELDB];
+  // DEPRECATED. See TransformationConf. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationConf. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationConf. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  // Force the encoded image to have 3 color channels
+  optional bool force_encoded_color = 9 [default = false];
+  // Prefetch queue (Number of batches to prefetch to host memory, increase if
+  // data access bandwidth varies).
+  optional uint32 prefetch = 10 [default = 4];
+}
+*/
+
+message DropoutConf {
+  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
+}
+
+// DummyDataLayer fills any number of arbitrarily shaped blobs with random
+// (or constant) data generated by "Fillers" (see "message FillerConf").
+message DummyDataConf {
+  // This layer produces N >= 1 top blobs.  DummyDataConf must specify 1 or N
+  // shape fields, and 0, 1 or N data_fillers.
+  //
+  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
+  // specified, the ith is applied to the ith top blob.
+  repeated FillerConf data_filler = 1;
+  repeated BlobShape shape = 6;
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
+  repeated uint32 num = 2;
+  repeated uint32 channels = 3;
+  repeated uint32 height = 4;
+  repeated uint32 width = 5;
+}
+
+message EltwiseConf {
+  enum EltwiseOp {
+    PROD = 0;
+    SUM = 1;
+    MAX = 2;
+  }
+  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
+  repeated float coeff = 2; // blob-wise coefficient for SUM operation
+
+  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
+  // of computing the gradient for the PROD operation. (No effect for SUM op.)
+  optional bool stable_prod_grad = 3 [default = true];
+}
+
+// Message that stores hyper-parameters used by EmbedLayer
+message EmbedConf {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  // The input is given as integers to be interpreted as one-hot
+  // vector indices with dimension num_input.  Hence num_input should be
+  // 1 greater than the maximum possible input value.
+  optional uint32 input_dim = 2;
+
+  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+  optional FillerConf weight_filler = 4; // The filler for the weight
+  optional FillerConf bias_filler = 5; // The filler for the bias
+
+}
+
+// Message that stores hyper-parameters used by ExpLayer
+message ExpConf {
+  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = exp(shift + scale * x).
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
+
+/// Message that stores hyper-parameters used by FlattenLayer
+message FlattenConf {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [default = 1];
+
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [default = -1];
+}
+
+/*
+// Message that stores hyper-parameters used by HDF5DataLayer
+message HDF5DataConf {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 2;
+
+  // Specify whether to shuffle the data.
+  // If shuffle == true, the ordering of the HDF5 files is shuffled,
+  // and the ordering of data within any given HDF5 file is shuffled,
+  // but data between different files are not interleaved; all of a file's
+  // data are output (in a random order) before moving onto another file.
+  optional bool shuffle = 3 [default = false];
+}
+
+message HDF5OutputConf {
+  optional string file_name = 1;
+}
+*/
+
+message HingeLossConf {
+  enum Norm {
+    L1 = 1;
+    L2 = 2;
+  }
+  // Specify the Norm to use L1 or L2
+  optional Norm norm = 1 [default = L1];
+}
+
+/*
+message ImageDataConf {
+  // Specify the data source.
+  optional string source = 1;
+  // Specify the batch size.
+  optional uint32 batch_size = 4 [default = 1];
+  // The rand_skip variable is for the data layer to skip a few data points
+  // to avoid all asynchronous sgd clients to start at the same point. The skip
+  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
+  // be larger than the number of keys in the database.
+  optional uint32 rand_skip = 7 [default = 0];
+  // Whether or not ImageLayer should shuffle the list of files at every epoch.
+  optional bool shuffle = 8 [default = false];
+  // It will also resize images if new_height or new_width are not zero.
+  optional uint32 new_height = 9 [default = 0];
+  optional uint32 new_width = 10 [default = 0];
+  // Specify if the images are color or gray
+  optional bool is_color = 11 [default = true];
+  // DEPRECATED. See TransformationConf. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformationConf. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformationConf. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
+  optional string root_folder = 12 [default = ""];
+}
+*/
+
+message InfogainLossConf {
+  // Specify the infogain matrix source.
+  optional string source = 1;
+}
+
+message InnerProductConf {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  optional bool bias_term = 2 [default = true]; // whether to have bias terms
+  optional FillerConf weight_filler = 3; // The filler for the weight
+  optional FillerConf bias_filler = 4; // The filler for the bias
+
+  // The first axis to be lumped into a single inner product computation;
+  // all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 5 [default = 1];
+}
+
+// Message that stores hyper-parameters used by LogLayer
+message LogConf {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
+
+// Message that stores hyper-parameters used by LRNLayer
+message LRNConf {
+  optional uint32 local_size = 1 [default = 5];
+  optional float alpha = 2 [default = 1.];
+  optional float beta = 3 [default = 0.75];
+  enum NormRegion {
+    ACROSS_CHANNELS = 0;
+    WITHIN_CHANNEL = 1;
+  }
+  optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
+  optional float k = 5 [default = 1.];
+}
+
+message MemoryDataConf {
+  optional uint32 batch_size = 1;
+  optional uint32 channels = 2;
+  optional uint32 height = 3;
+  optional uint32 width = 4;
+}
+
+message MVNConf {
+  // This parameter can be set to false to normalize mean only
+  optional bool normalize_variance = 1 [default = true];
+
+  // This parameter can be set to true to perform DNN-like MVN
+  optional bool across_channels = 2 [default = false];
+
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [default = 1e-9];
+}
+
+message PoolingConf {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 2; // The kernel size (square)
+  optional uint32 kernel_h = 5; // The kernel height
+  optional uint32 kernel_w = 6; // The kernel width
+  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 7; // The stride height
+  optional uint32 stride_w = 8; // The stride width
+  /*
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 11 [default = DEFAULT];
+  */
+  // If global_pooling then it will pool over the size of the bottom by doing
+  // kernel_h = bottom->height and kernel_w = bottom->width
+  optional bool global_pooling = 12 [default = false];
+}
+
+message PowerConf {
+  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
+  optional float power = 1 [default = 1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
+/*
+message PythonConf {
+  optional string module = 1;
+  optional string layer = 2;
+  // This value is set to the attribute `param_str` of the `PythonLayer` object
+  // in Python before calling the `setup()` method. This could be a number,
+  // string, dictionary in Python dict format, JSON, etc. You may parse this
+  // string in `setup` method and use it in `forward` and `backward`.
+  optional string param_str = 3 [default = ''];
+  // Whether this PythonLayer is shared among worker solvers during data parallelism.
+  // If true, each worker solver sequentially run forward from this layer.
+  // This value should be set true if you are using it as a data layer.
+  optional bool share_in_parallel = 4 [default = false];
+}
+*/
+
+// Message that stores hyper-parameters used by ReductionLayer
+message ReductionConf {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+  }
+
+  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [default = 0];
+
+  optional float coeff = 3 [default = 1.0]; // coefficient for output
+}
+
+// Message that stores hyper-parameters used by ReLULayer
+message ReLUConf {
+  // Allow non-zero slope for negative inputs to speed up optimization
+  // Described in:
+  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+  // improve neural network acoustic models. In ICML Workshop on Deep Learning
+  // for Audio, Speech, and Language Processing.
+  optional float negative_slope = 1 [default = 0];
+  /*
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 2 [default = DEFAULT];
+  */
+}
+
+message ReshapeConf {
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
+  //
+  optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [default = 0];
+  optional int32 num_axes = 3 [default = -1];
+}
+
+message SigmoidConf {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
+
+message SliceConf {
+  // The axis along which to slice -- may be negative to index from the end
+  // (e.g., -1 for the last axis).
+  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 3 [default = 1];
+  repeated uint32 slice_point = 2;
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 slice_dim = 1 [default = 1];
+}
+
+// Message that stores hyper-parameters used by SoftmaxLayer, SoftmaxWithLossLayer
+message SoftmaxConf {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
+}
+
+message TanHConf {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
+
+// Message that stores hyper-parameters used by TileLayer
+message TileConf {
+  // The index of the axis to tile.
+  optional int32 axis = 1 [default = 1];
+
+  // The number of copies (tiles) of the blob to output.
+  optional int32 tiles = 2;
+}
+
+// Message that stores hyper-parameters used by ThresholdLayer
+message ThresholdConf {
+  optional float threshold = 1 [default = 0]; // Strictly positive values
+}
+
+/*
+message WindowDataConf {
+  // Specify the data source.
+  optional string source = 1;
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // Specify the batch size.
+  optional uint32 batch_size = 4;
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 6 [default = false];
+  // Foreground (object) overlap threshold
+  optional float fg_threshold = 7 [default = 0.5];
+  // Background (non-object) overlap threshold
+  optional float bg_threshold = 8 [default = 0.5];
+  // Fraction of batch that should be foreground objects
+  optional float fg_fraction = 9 [default = 0.25];
+  // Amount of contextual padding to add around a window
+  // (used only by the window_data_layer)
+  optional uint32 context_pad = 10 [default = 0];
+  // Mode for cropping out a detection window
+  // warp: cropped window is warped to a fixed size and aspect ratio
+  // square: the tightest square around the window is cropped
+  optional string crop_mode = 11 [default = "warp"];
+  // cache_images: will load all images in memory for faster access
+  optional bool cache_images = 12 [default = false];
+  // append root_folder to locate images
+  optional string root_folder = 13 [default = ""];
+}
+*/
+
+message SPPConf {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+  /*
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+  */
+}
+
+message PReLUConf {
+  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+  // Initial value of a_i. Default is a_i=0.25 for all i.
+  optional FillerConf filler = 1;
+  // Whether or not slope paramters are shared across channels.
+  optional bool channel_shared = 2 [default = false];
+}


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