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Cherry-pick batch normalization PR #51

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Nov 3, 2015
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28 changes: 28 additions & 0 deletions examples/cifar10/cifar10_full_sigmoid_solver.prototxt
Original file line number Diff line number Diff line change
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# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
# then another factor of 10 after 10 more epochs (5000 iters)

# The train/test net protocol buffer definition
net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 10
# Carry out testing every 1000 training iterations.
test_interval: 1000
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001
momentum: 0.9
#weight_decay: 0.004
# The learning rate policy
lr_policy: "step"
gamma: 1
stepsize: 5000
# Display every 200 iterations
display: 100
# The maximum number of iterations
max_iter: 60000
# snapshot intermediate results
snapshot: 10000
snapshot_prefix: "examples/cifar10_full_sigmoid"
# solver mode: CPU or GPU
solver_mode: GPU
28 changes: 28 additions & 0 deletions examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
Original file line number Diff line number Diff line change
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# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
# then another factor of 10 after 10 more epochs (5000 iters)

# The train/test net protocol buffer definition
net: "examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 10
# Carry out testing every 1000 training iterations.
test_interval: 1000
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001
momentum: 0.9
#weight_decay: 0.004
# The learning rate policy
lr_policy: "step"
gamma: 1
stepsize: 5000
# Display every 200 iterations
display: 100
# The maximum number of iterations
max_iter: 60000
# snapshot intermediate results
snapshot: 10000
snapshot_prefix: "examples/cifar10_full_sigmoid_bn"
# solver mode: CPU or GPU
solver_mode: GPU
212 changes: 212 additions & 0 deletions examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
Original file line number Diff line number Diff line change
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name: "CIFAR10_full"
layer {
name: "cifar"
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: 111
backend: LMDB
}
}
layer {
name: "cifar"
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: 1000
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: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}



layer {
name: "Sigmoid1"
type: "Sigmoid"
bottom: "pool1"
top: "Sigmoid1"
}

layer {
name: "conv2"
type: "Convolution"
bottom: "Sigmoid1"
top: "conv2"
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: 0.01
}
bias_filler {
type: "constant"
}
}
}


layer {
name: "Sigmoid2"
type: "Sigmoid"
bottom: "conv2"
top: "Sigmoid2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "Sigmoid2"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
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"
}
}
param {
lr_mult: 1
}
param {
lr_mult: 1
}

}

layer {
name: "Sigmoid3"
type: "Sigmoid"
bottom: "conv3"
top: "Sigmoid3"
}

layer {
name: "pool3"
type: "Pooling"
bottom: "Sigmoid3"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 3
stride: 2
}
}

layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool3"
top: "ip1"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 10
weight_filler {
type: "gaussian"
std: 0.01
}
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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