嗨，大家好我是深度学习和caffe的新手，但我正在尝试使用Caffe构建汽车，货车，公共汽车和卡车的车辆分类架构。 我使用了已经训练过的模型，但损失值是恒定的以及准确性。因此，我决定从头开始，从2个卷积层2池化层和2个完全连接的层开始，但我仍然有同样的问题，即恒定损耗和常数准确性。 我已经微调了改变大多数值的模型，但无济于事。我在文本文件中设置的数据也被洗牌，因此我对我的错误感到困惑。因此，我在问是否有人可以提供帮助 下面是我的my_train_val.prototxt和解算器文件
**`my_train_val.prototxt`**
name: "CaffeNet"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_value: 104
mean_value: 117
mean_value: 123
scale: 0.00390625
}
# mean pixel / channel-wise mean instead of mean image
# transform_param {
# crop_size: 127
# mean_value: 104
# mean_value: 117
# mean_value: 123
# mirror: true
# }
image_data_param {
source: "/home/kay/caffe/models/my_models/train_files.txt"
batch_size: 10
new_height: 256
new_width: 256
}
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 227
mean_value: 104
mean_value: 117
mean_value: 123
scale: 0.00390625
}
# mean pixel / channel-wise mean instead of mean image
# transform_param {
# crop_size: 127
# mean_value: 104
# mean_value: 117
# mean_value: 123
# mirror: false
# }
image_data_param {
source: "/home/kay/caffe/models/my_models/train_files.txt"
batch_size: 10
new_height: 256
new_width: 256
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
# decay_mult: 1
}
param {
lr_mult: 2
#decay_mult: 0
}
convolution_param {
num_output: 20
kernel_size: 3
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.7
}
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
#layer {
# name: "pool2"
# type: "Pooling"
# bottom: "pool1"
# top: "pool2"
# pooling_param {
# pool: MAX
# kernel_size:2
# stride: 2
# }
#}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
# decay_mult: 1
}
param {
lr_mult: 2
# decay_mult: 0
}
convolution_param {
num_output: 40
kernel_size: 5
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.7
}
}
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv2"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "pool5"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 50
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.7
}
}
}
layer {
name: "fc9"
type: "InnerProduct"
bottom: "fc7"
top: "fc9"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.7
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc9"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc9"
bottom: "label"
top: "loss"
}
net: "/home/kay/caffe/models/my_models/train.prototxt"
test_iter: 1000
test_interval: 500
base_lr: 0.0001
lr_policy: "inv"
gamma: 0.01
power: 0.75
display: 100
max_iter: 2000
momentum: 0.9
weight_decay: 0.0005
snapshot: 100
snapshot_prefix: "models/bvlc_reference_caffenet/caffenet_train"
solver_mode: CPU
**Output**
I0204 01:02:46.402113 11660 blocking_queue.cpp:49] Waiting for data
I0204 01:09:49.701264 11660 solver.cpp:398] Test net output #0: accuracy = 0.5
I0204 01:09:49.701395 11660 solver.cpp:398] Test net output #1: loss = 0.694733 (* 1 = 0.694733 loss)
I0204 01:09:50.594509 11660 solver.cpp:219] Iteration 0 (0 iter/s, 424.196s/100 iters), loss = 0.705802
I0204 01:09:50.594566 11660 solver.cpp:238] Train net output #0: loss = 0.705802 (* 1 = 0.705802 loss)
I0204 01:09:50.594581 11660 sgd_solver.cpp:105] Iteration 0, lr = 0.0001
I0204 01:11:15.426913 11660 solver.cpp:448] Snapshotting to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_100.caffemodel
I0204 01:11:15.434830 11660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_100.solverstate
I0204 01:11:16.299131 11660 solver.cpp:219] Iteration 100 (1.16681 iter/s, 85.704s/100 iters), loss = 0.693157
I0204 01:11:16.299195 11660 solver.cpp:238] Train net output #0: loss = 0.693157 (* 1 = 0.693157 loss)
I0204 01:11:16.299207 11660 sgd_solver.cpp:105] Iteration 100, lr = 9.31012e-05
I0204 01:12:40.570622 11660 solver.cpp:448] Snapshotting to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_200.caffemodel
I0204 01:12:40.577692 11660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_200.solverstate
I0204 01:12:41.434355 11660 solver.cpp:219] Iteration 200 (1.17461 iter/s, 85.135s/100 iters), loss = 0.693197
I0204 01:12:41.434409 11660 solver.cpp:238] Train net output #0: loss = 0.693197 (* 1 = 0.693197 loss)
I0204 01:12:41.434420 11660 sgd_solver.cpp:105] Iteration 200, lr = 8.72196e-05
I0204 01:14:05.489266 11660 solver.cpp:448] Snapshotting to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_300.caffemodel
I0204 01:14:05.510332 11660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_300.solverstate
I0204 01:14:06.364553 11660 solver.cpp:219] Iteration 300 (1.17744 iter/s, 84.93s/100 iters), loss = 0.693178
I0204 01:14:06.364609 11660 solver.cpp:238] Train net output #0: loss = 0.693178 (* 1 = 0.693178 loss)
I0204 01:14:06.364626 11660 sgd_solver.cpp:105] Iteration 300, lr = 8.21377e-05
I0204 01:15:31.256232 11660 solver.cpp:448] Snapshotting to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_400.caffemodel
I0204 01:15:31.262976 11660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_400.solverstate
I0204 01:15:32.126720 11660 solver.cpp:219] Iteration 400 (1.16602 iter/s, 85.762s/100 iters), loss = 0.69315
I0204 01:15:32.126785 11660 solver.cpp:238] Train net output #0: loss = 0.69315 (* 1 = 0.69315 loss)
I0204 01:15:32.126797 11660 sgd_solver.cpp:105] Iteration 400, lr = 7.76969e-05
I0204 01:16:57.399161 11660 solver.cpp:448] Snapshotting to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_500.caffemodel
I0204 01:16:57.406093 11660 sgd_solver.cpp:273] Snapshotting solver state to binary proto file models/bvlc_reference_caffenet/caffenet_train_iter_500.solverstate