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name: "alexnet"
input: "data"
input_dim: 128
input_dim: 3
input_dim: 224
input_dim: 224
force_backward: true
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1/11x11_s4"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 11
stride: 4
pad: 2
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
name: "conv1/relu"
type: RELU
bottom: "conv1/11x11_s4"
top: "conv1/11x11_s4"
}
layers {
name: "pool1/3x3_s2"
type: POOLING
bottom: "conv1/11x11_s4"
top: "pool1/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "conv2/5x5_s1"
type: CONVOLUTION
bottom: "pool1/3x3_s2"
top: "conv2/5x5_s1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
kernel_size: 5
stride: 1
pad: 2
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
name: "conv2/relu"
type: RELU
bottom: "conv2/5x5_s1"
top: "conv2/5x5_s1"
}
layers {
name: "pool2/3x3_s2"
type: POOLING
bottom: "conv2/5x5_s1"
top: "pool2/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "conv3/3x3_s1"
type: CONVOLUTION
bottom: "pool2/3x3_s2"
top: "conv3/3x3_s1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
kernel_size: 3
stride: 1
pad: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
name: "conv3/relu"
type: RELU
bottom: "conv3/3x3_s1"
top: "conv3/3x3_s1"
}
layers {
name: "conv4/3x3_s1"
type: CONVOLUTION
bottom: "conv3/3x3_s1"
top: "conv4/3x3_s1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 3
stride: 1
pad: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
name: "conv4/relu"
type: RELU
bottom: "conv4/3x3_s1"
top: "conv4/3x3_s1"
}
layers {
name: "conv5/3x3_s1"
type: CONVOLUTION
bottom: "conv4/3x3_s1"
top: "conv5/3x3_s1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 3
stride: 1
pad: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
name: "conv5/relu"
type: RELU
bottom: "conv5/3x3_s1"
top: "conv5/3x3_s1"
}
layers {
name: "pool5/3x3_s2"
type: POOLING
bottom: "conv5/3x3_s1"
top: "pool5/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5/3x3_s2"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layers {
name: "conv1/relu"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}
layers {
name: "conv1/relu"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 1000
}
}