Use Droupout layer in Caffe to improve accuracy of Cifar10 picture set 10% (0.62 to 0.72)

# The Train/test net protocol buffer definition
NET: "D:\\caffeinfo\\d_trainval\\cifar10_full_train_test.prototxt "
# Test_iter Specifies how many forward passes the test should carry out.
# in the case of MNIST, we have test batch size and test iterations,
# covering the full testing images .
test_iter:200
# Carry out testing every training iterations.
test_interval:200
# The base learning rate, momentum and the weight decay of the network.
base_lr:0.01
momentum:0.9
weight_decay:0.004
# The Learning rate policy
lr_policy: "Step"
gamma:0.1
stepsize:10000
# Display every iterations
display:200
# The maximum number of Iterati ONS
max_iter:100000
# Snapshot Intermediate results
snapshot:10000
snapshot_format:hdf5
Snapshot_prefix: "D:\\caffeinfo\\d_trainval\\cifar10_full"
# Solver mode:cpu or GPU
Solver_mode:gpu

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dropout_ratio:0.1

Name: "Cifar10_full" Layer {name: "Cifar" type: "Data" Top: "Data" Top: "label" include {Phase:train} Transform_param {mean_file: "D:\\caffeinfo\\b_datacreate\\mean.binaryproto"} data_param {source: "D:\\Caf feinfo\\b_datacreate\\train_db "Batch_size:50 Backend:lmdb}}" layer {name: "Cifar" type: "Data" Top: " Data "Top:" label "include {phase:test} transform_param {mean_file:" D:\\caffeinfo\\b_datacreate\\mean
  . Binaryproto "} data_param {Source:" d:\\caffeinfo\\b_datacreate\\val_db "batch_size:50 Backend:lmdb
    }} layer {name: "CONV1" type: "Convolution" bottom: "Data" Top: "Conv1" param {lr_mult:1} param { 
      Lr_mult:2} convolution_param {num_output:32 pad:2 kernel_size:5 stride:1 Weight_filler { Type: "Gaussian" std:0.0001} bias_filler {type: "Constant"}}} "layer {name:" Pool 1 "type:" Pooling "bOttom: "Conv1" Top: "Pool1" Pooling_param {Pool:max kernel_size:3 stride:2}} layer {name: "Relu 1 "type:" ReLU "bottom:" pool1 "Top:" pool1 "} layer {name:" Norm1 "type:" LRN "bottom:" pool1 "Top:" Norm1 "Lrn_param {local_size:3 alpha:5e-05 beta:0.75 Norm_region:within_channel}}" layer {name: "C Onv2 "type:" Convolution "bottom:" Norm1 "Top:" Conv2 "param {lr_mult:1} param {Lr_mult:2} c Onvolution_param {num_output:32 pad:2 kernel_size:5 stride:1 weight_filler {type: "Gaussia n "std:0.01} bias_filler {type:" Constant "}}}} layer {name:" RELU2 "type:" ReLU "bot 
    Tom: "Conv2" Top: "conv2"} layer {name: "pool2" type: "Pooling" bottom: "conv2" Top: "Pool2" Pooling_param { Pool:ave kernel_size:3 Stride:2}} layer {name: "Norm2" type: "LRN" bottom: "pool2" Top: "norm 2 "Lrn_param {locAl_size:3 alpha:5e-05 beta:0.75 Norm_region:within_channel}} layer {name: "Conv3" type: "Convolut 
    Ion "bottom:" Norm2 "Top:" Conv3 "Convolution_param {num_output:64 pad:2 kernel_size:5 stride:1 Weight_filler {type: "Gaussian" std:0.01} bias_filler {type: "Constant"}}} Lay  ER {name: "RELU3" type: "ReLU" bottom: "conv3" Top: "conv3"} layer {name: "pool3" type: "Pooling" bottom: "Conv3" Top: "Pool3" Pooling_param {pool:ave kernel_size:3 stride:2}} layer {name: "ip1" Typ 
    E: "Innerproduct" bottom: "pool3" Top: "ip1" param {lr_mult:1 decay_mult:250} param {Lr_mult:2 decay_mult:0} inner_product_param {num_output:256 Weight_filler {type: "Gaussian" std:0 . Bias_filler} {type: ' Constant '}}} ' layer {name: ' fc7 ' type: ' innerproduct ' bottom: ' ip1 "Top:" Fc7 "param {
    Lr_mult:1 decay_mult:1} param {lr_mult:2 decay_mult:0} inner_product_param {NUM_OUTPU T:10 Weight_filler {type: "Gaussian" std:0.005} bias_filler {type: "Constant" Val UE:1}}} layer {name: "RELU7" type: "ReLU" bottom: "fc7" Top: "FC7"} layer {name: "DROP7" type: "D Ropout "bottom:" fc7 "Top:" Fc7 "Dropout_param {dropout_ratio:0.1}} layer {name:" accuracy "type:" Ac Curacy "bottom:" fc7 "bottom:" Label "Top:" accuracy "include {phase:test}} layer {name:" Loss "Typ E: "Softmaxwithloss" bottom: "fc7" bottom: "Label" Top: "Loss"}

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No droupout.

Name: "Cifar10_full" Layer {name: "Cifar" type: "Data" Top: "Data" Top: "label" include {Phase:train} Transform_param {mean_file: "D:\\caffeinfo\\b_datacreate\\mean.binaryproto"} data_param {source: "D:\\Caf feinfo\\b_datacreate\\train_db "Batch_size:50 Backend:lmdb}}" layer {name: "Cifar" type: "Data" Top: " Data "Top:" label "include {phase:test} transform_param {mean_file:" D:\\caffeinfo\\b_datacreate\\mean
  . Binaryproto "} data_param {Source:" d:\\caffeinfo\\b_datacreate\\val_db "batch_size:50 Backend:lmdb
    }} layer {name: "CONV1" type: "Convolution" bottom: "Data" Top: "Conv1" param {lr_mult:1} param { 
      Lr_mult:2} convolution_param {num_output:32 pad:2 kernel_size:5 stride:1 Weight_filler { Type: "Gaussian" std:0.0001} bias_filler {type: "Constant"}}} "layer {name:" Pool 1 "type:" Pooling "bOttom: "Conv1" Top: "Pool1" Pooling_param {Pool:max kernel_size:3 stride:2}} layer {name: "Relu 1 "type:" ReLU "bottom:" pool1 "Top:" pool1 "} layer {name:" Norm1 "type:" LRN "bottom:" pool1 "Top:" Norm1 "Lrn_param {local_size:3 alpha:5e-05 beta:0.75 Norm_region:within_channel}}" layer {name: "C Onv2 "type:" Convolution "bottom:" Norm1 "Top:" Conv2 "param {lr_mult:1} param {Lr_mult:2} c Onvolution_param {num_output:32 pad:2 kernel_size:5 stride:1 weight_filler {type: "Gaussia n "std:0.01} bias_filler {type:" Constant "}}}} layer {name:" RELU2 "type:" ReLU "bot 
    Tom: "Conv2" Top: "conv2"} layer {name: "pool2" type: "Pooling" bottom: "conv2" Top: "Pool2" Pooling_param { Pool:ave kernel_size:3 Stride:2}} layer {name: "Norm2" type: "LRN" bottom: "pool2" Top: "norm 2 "Lrn_param {locAl_size:3 alpha:5e-05 beta:0.75 Norm_region:within_channel}} layer {name: "Conv3" type: "Convolut 
    Ion "bottom:" Norm2 "Top:" Conv3 "Convolution_param {num_output:64 pad:2 kernel_size:5 stride:1 Weight_filler {type: "Gaussian" std:0.01} bias_filler {type: "Constant"}}} Lay  ER {name: "RELU3" type: "ReLU" bottom: "conv3" Top: "conv3"} layer {name: "pool3" type: "Pooling" bottom: "Conv3" Top: "Pool3" Pooling_param {pool:ave kernel_size:3 stride:2}} layer {name: "ip1" Typ 
    E: "Innerproduct" bottom: "pool3" Top: "ip1" param {lr_mult:1 decay_mult:250} param {Lr_mult:2 decay_mult:0} inner_product_param {num_output:10 Weight_filler {type: "Gaussian" std:0. Bias_filler {type: "Constant"}}}} layer {name: "accuracy" type: "Accuracy" bottom: "ip1 "Bottom:" Label "toP: "Accuracy" include {phase:test}} layer {name: "Loss" type: "Softmaxwithloss" bottom: "ip1" bottom:

 "Label" Top: "Loss"}

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