Проблема с инициализацией сети Caffe

К концу приложен вывод инициализации Caffe Training.

Моя сеть состоит из 4 сверточных слоев, за которыми следуют 3 полностью связанных слоя.

Я рассчитал размеры фильтра и карты объектов, и мои расчеты показывают, что они согласованы.

1. Вход представляет собой изображение размеров (195 х 65).
2. Фильтры первого слоя conv1 (32 X 13 X 5), где 32 — количество фильтров. Шаг 1 по обеим осям.
3. Каждая выходная карта conv1 должна иметь размер (183 X 61).
4. МАКС. Объединяющий слой размером (2 X 2) с шагом 2.
5. Выход пула должен быть размером (92 х 31).
6. За этим следует слой conv2 размера (48 X 7 X 3).
7. Каждая карта объектов слоя conv2 должна иметь размер (86 X 29)
8. Затем следует средний пул с размером окна (2 X 2).
9. Выход пула слоя имеет размер (43 х 15)
10. Далее следует слой conv3 размера (64 X 7 X 3), где 64 — количество фильтров.
11. Вывод conv3 должен иметь размер (37 X 13)
12. За ним сразу следует слой conv4 размера (64 X 5 X 3),
13. Каждая карта характеристик вывода conv4 должна иметь размер (33 X 11)
14. За Conv4 следует средний пул размера окна (2 X 2)
15. Выход этого имеет размер (17 X 6)
16. Далее следуют 3 полностью связанных слоя, каждый с 300 выходными нейронами.

Никакое дополнение не сделано на любом слое. Ниже приведен результат инициализации обучения кофе. Это показывает некоторые ошибки в conv2, conv3 и затем в fc6.
Я не могу понять источник этих ошибок. Может кто-нибудь уточнить, пожалуйста?

I1119 17:55:45.301447 14339 caffe.cpp:156] Using GPUs 0
I1119 17:55:45.447502 14339 solver.cpp:33] Initializing solver from parameters:
test_iter: 970
test_interval: 2908
base_lr: 0.01
display: 50
max_iter: 87240
lr_policy: "step"gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 28790
snapshot: 2908
snapshot_prefix: "snapshot"solver_mode: GPU
device_id: 0
net: "train_val.prototxt"solver_type: SGD
I1119 17:55:45.447551 14339 solver.cpp:81] Creating training net from net file: train_val.prototxt
I1119 17:55:45.448559 14339 net.cpp:316] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I1119 17:55:45.448586 14339 net.cpp:316] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I1119 17:55:45.448849 14339 net.cpp:47] Initializing net from parameters:
state {
phase: TRAIN
}
layer {
name: "data"type: "Data"top: "data"top: "label"include {
phase: TRAIN
}
transform_param {
mirror: true
mean_file: "/home/uujjwal/libraries/digits/DIGITS/digits/jobs/20151118-160932-e0c7/mean.binaryproto"crop_h: 195
crop_w: 65
}
data_param {
source: "/home/uujjwal/libraries/digits/DIGITS/digits/jobs/20151118-160932-e0c7/train_db"batch_size: 100
backend: LMDB
prefetch: 4
}
}
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: 32
stride: 1
weight_filler {
type: "gaussian"std: 0.01
}
bias_filler {
type: "constant"value: 0
}
kernel_h: 13
kernel_w: 5
}
}
layer {
name: "relu1"type: "ReLU"bottom: "conv1"top: "conv1"}
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
pad: 0
}
}
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: 48
stride: 1
weight_filler {
type: "gaussian"std: 0.01
}
bias_filler {
type: "constant"value: 0.1
}
kernel_h: 7
kernel_w: 3
}
}
layer {
name: "relu2"type: "ReLU"bottom: "conv2"top: "conv2"}
layer {
name: "norm2"type: "LRN"bottom: "conv2"top: "norm2"lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"type: "Pooling"bottom: "norm2"top: "pool2"pooling_param {
pool: AVE
kernel_size: 2
stride: 2
pad: 0
}
}
layer {
name: "conv3"type: "Convolution"bottom: "pool2"top: "conv3"param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
stride: 1
weight_filler {
type: "gaussian"std: 0.01
}
bias_filler {
type: "constant"value: 0
}
kernel_h: 7
kernel_w: 3
}
}
layer {
name: "relu3"type: "ReLU"bottom: "conv3"top: "conv3"}
layer {
name: "conv4"type: "Convolution"bottom: "conv3"top: "conv4"param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
stride: 1
weight_filler {
type: "gaussian"std: 0.01
}
bias_filler {
type: "constant"value: 0
}
kernel_h: 5
kernel_w: 3
}
}
layer {
name: "relu4"type: "ReLU"bottom: "conv4"top: "conv4"}
layer {
name: "norm4"type: "LRN"bottom: "conv4"top: "norm4"lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool4"type: "Pooling"bottom: "norm4"top: "pool4"pooling_param {
pool: AVE
kernel_size: 2
stride: 2
pad: 0
}
}
layer {
name: "fc6"type: "InnerProduct"bottom: "pool4"top: "fc6"param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 300
weight_filler {
type: "gaussian"std: 0.005
}
bias_filler {
type: "constant"value: 0.1
}
}
}
layer {
name: "relu6"type: "ReLU"bottom: "fc6"top: "fc6"}
layer {
name: "drop6"type: "Dropout"bottom: "fc6"top: "fc6"dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"type: "InnerProduct"bottom: "fc6"top: "fc7"param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 300
weight_filler {
type: "gaussian"std: 0.005
}
bias_filler {
type: "constant"value: 0.1
}
}
}
layer {
name: "relu7"type: "ReLU"bottom: "fc7"top: "fc7"}
layer {
name: "drop7"type: "Dropout"bottom: "fc7"top: "fc7"dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8_retrain_retrain_retrain"type: "InnerProduct"bottom: "fc7"top: "fc8"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
}
}
}
layer {
name: "loss"type: "SoftmaxWithLoss"bottom: "fc8"bottom: "label"top: "loss"}
I1119 17:55:45.448966 14339 layer_factory.hpp:75] Creating layer data
I1119 17:55:45.449409 14339 net.cpp:99] Creating Layer data
I1119 17:55:45.449422 14339 net.cpp:409] data -> data
I1119 17:55:45.449450 14339 net.cpp:409] data -> label
I1119 17:55:45.449457 14339 net.cpp:131] Setting up data
I1119 17:55:45.453604 14349 db.cpp:34] Opened lmdb /home/uujjwal/libraries/digits/DIGITS/digits/jobs/20151118-160932-e0c7/train_db
I1119 17:55:45.454463 14339 data_layer.cpp:37] Decoding Datum
I1119 17:55:45.454485 14339 data_layer.cpp:65] output data size: 100,1,195,65
I1119 17:55:45.454495 14339 data_transformer.cpp:28] Loading mean file from: /home/uujjwal/libraries/digits/DIGITS/digits/jobs/20151118-160932-e0c7/mean.binaryproto
I1119 17:55:45.466743 14339 net.cpp:140] Top shape: 100 1 195 65 (1267500)
I1119 17:55:45.466761 14339 net.cpp:140] Top shape: 100 (100)
I1119 17:55:45.466770 14339 layer_factory.hpp:75] Creating layer conv1
I1119 17:55:45.466789 14339 net.cpp:99] Creating Layer conv1
I1119 17:55:45.466796 14339 net.cpp:453] conv1 <- data
I1119 17:55:45.466811 14339 net.cpp:409] conv1 -> conv1
I1119 17:55:45.466825 14339 net.cpp:131] Setting up conv1
I1119 17:55:45.468318 14339 net.cpp:140] Top shape: 100 32 183 61 (35721600)
I1119 17:55:45.468335 14339 layer_factory.hpp:75] Creating layer relu1
I1119 17:55:45.468343 14339 net.cpp:99] Creating Layer relu1
I1119 17:55:45.468348 14339 net.cpp:453] relu1 <- conv1
I1119 17:55:45.468354 14339 net.cpp:396] relu1 -> conv1 (in-place)
I1119 17:55:45.468360 14339 net.cpp:131] Setting up relu1
I1119 17:55:45.468371 14339 net.cpp:140] Top shape: 100 32 183 61 (35721600)
I1119 17:55:45.468375 14339 layer_factory.hpp:75] Creating layer norm1
I1119 17:55:45.468382 14339 net.cpp:99] Creating Layer norm1
I1119 17:55:45.468386 14339 net.cpp:453] norm1 <- conv1
I1119 17:55:45.468391 14339 net.cpp:409] norm1 -> norm1
I1119 17:55:45.468399 14339 net.cpp:131] Setting up norm1
I1119 17:55:45.468408 14339 net.cpp:140] Top shape: 100 32 183 61 (35721600)
I1119 17:55:45.468412 14339 layer_factory.hpp:75] Creating layer pool1
I1119 17:55:45.468422 14339 net.cpp:99] Creating Layer pool1
I1119 17:55:45.468426 14339 net.cpp:453] pool1 <- norm1
I1119 17:55:45.468431 14339 net.cpp:409] pool1 -> pool1
I1119 17:55:45.468438 14339 net.cpp:131] Setting up pool1
I1119 17:55:45.468456 14339 net.cpp:140] Top shape: 100 32 92 31 (9126400)
I1119 17:55:45.468461 14339 layer_factory.hpp:75] Creating layer conv2
I1119 17:55:45.468468 14339 net.cpp:99] Creating Layer conv2
I1119 17:55:45.468472 14339 net.cpp:453] conv2 <- pool1
I1119 17:55:45.468478 14339 net.cpp:409] conv2 -> conv2
I1119 17:55:45.468509 14339 net.cpp:131] Setting up conv2
OpenCV Error: Assertion failed (0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows) in Mat, file /home/uujjwal/libraries/opencv-3.0.0/modules/core/src/matrix.cpp, line 495
terminate called after throwing an instance of 'cv::Exception'
what():  /home/uujjwal/libraries/opencv-3.0.0/modules/core/src/matrix.cpp:495: error: (-215) 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows in function Mat
*** Aborted at 1447952145 (unix time) try "date -d @1447952145" if you are using GNU date ***
PC: @     0x7f9446ec75d7 __GI_raise
*** SIGABRT (@0x9f71700003803) received by PID 14339 (TID 0x7f9430a6f700) from PID 14339; stack trace: ***
I1119 17:55:45.470507 14339 net.cpp:140] Top shape: 100 48 86 29 (11971200)
I1119 17:55:45.470520 14339 layer_factory.hpp:75] Creating layer relu2
I1119 17:55:45.470535 14339 net.cpp:99] Creating Layer relu2
I1119 17:55:45.470541 14339 net.cpp:453] relu2 <- conv2
I1119 17:55:45.470547 14339 net.cpp:396] relu2 -> conv2 (in-place)
I1119 17:55:45.470553 14339 net.cpp:131] Setting up relu2
I1119 17:55:45.470559 14339 net.cpp:140] Top shape: 100 48 86 29 (11971200)
I1119 17:55:45.470563 14339 layer_factory.hpp:75] Creating layer norm2
I1119 17:55:45.470569 14339 net.cpp:99] Creating Layer norm2
I1119 17:55:45.470573 14339 net.cpp:453] norm2 <- conv2
I1119 17:55:45.470580 14339 net.cpp:409] norm2 -> norm2
I1119 17:55:45.470587 14339 net.cpp:131] Setting up norm2
I1119 17:55:45.470593 14339 net.cpp:140] Top shape: 100 48 86 29 (11971200)
I1119 17:55:45.470598 14339 layer_factory.hpp:75] Creating layer pool2
I1119 17:55:45.470604 14339 net.cpp:99] Creating Layer pool2
I1119 17:55:45.470614 14339 net.cpp:453] pool2 <- norm2
I1119 17:55:45.470619 14339 net.cpp:409] pool2 -> pool2
I1119 17:55:45.470625 14339 net.cpp:131] Setting up pool2
I1119 17:55:45.470633 14339 net.cpp:140] Top shape: 100 48 43 15 (3096000)
I1119 17:55:45.470636 14339 layer_factory.hpp:75] Creating layer conv3
I1119 17:55:45.470643 14339 net.cpp:99] Creating Layer conv3
I1119 17:55:45.470648 14339 net.cpp:453] conv3 <- pool2
I1119 17:55:45.470654 14339 net.cpp:409] conv3 -> conv3
I1119 17:55:45.470661 14339 net.cpp:131] Setting up conv3
@     0x7f945ba27130 (unknown)
@     0x7f9446ec75d7 __GI_raise
@     0x7f9446ec8cc8 __GI_abort
@     0x7f94477cb9b5 (unknown)
@     0x7f94477c9926 (unknown)
@     0x7f94477c9953 (unknown)
@     0x7f94477c9b73 (unknown)
I1119 17:55:45.473515 14339 net.cpp:140] Top shape: 100 64 37 13 (3078400)
I1119 17:55:45.473528 14339 layer_factory.hpp:75] Creating layer relu3
I1119 17:55:45.473542 14339 net.cpp:99] Creating Layer relu3
I1119 17:55:45.473546 14339 net.cpp:453] relu3 <- conv3
I1119 17:55:45.473552 14339 net.cpp:396] relu3 -> conv3 (in-place)
I1119 17:55:45.473558 14339 net.cpp:131] Setting up relu3
I1119 17:55:45.473565 14339 net.cpp:140] Top shape: 100 64 37 13 (3078400)
I1119 17:55:45.473569 14339 layer_factory.hpp:75] Creating layer conv4
I1119 17:55:45.473577 14339 net.cpp:99] Creating Layer conv4
I1119 17:55:45.473580 14339 net.cpp:453] conv4 <- conv3
I1119 17:55:45.473587 14339 net.cpp:409] conv4 -> conv4
I1119 17:55:45.473592 14339 net.cpp:131] Setting up conv4
@     0x7f945269b58d cv::error()
@     0x7f945269b6f0 cv::error()
I1119 17:55:45.475775 14339 net.cpp:140] Top shape: 100 64 33 11 (2323200)
I1119 17:55:45.475786 14339 layer_factory.hpp:75] Creating layer relu4
I1119 17:55:45.475793 14339 net.cpp:99] Creating Layer relu4
I1119 17:55:45.475797 14339 net.cpp:453] relu4 <- conv4
I1119 17:55:45.475803 14339 net.cpp:396] relu4 -> conv4 (in-place)
I1119 17:55:45.475810 14339 net.cpp:131] Setting up relu4
I1119 17:55:45.475816 14339 net.cpp:140] Top shape: 100 64 33 11 (2323200)
I1119 17:55:45.475819 14339 layer_factory.hpp:75] Creating layer norm4
I1119 17:55:45.475826 14339 net.cpp:99] Creating Layer norm4
I1119 17:55:45.475829 14339 net.cpp:453] norm4 <- conv4
I1119 17:55:45.475834 14339 net.cpp:409] norm4 -> norm4
I1119 17:55:45.475841 14339 net.cpp:131] Setting up norm4
I1119 17:55:45.475847 14339 net.cpp:140] Top shape: 100 64 33 11 (2323200)
I1119 17:55:45.475852 14339 layer_factory.hpp:75] Creating layer pool4
I1119 17:55:45.475858 14339 net.cpp:99] Creating Layer pool4
I1119 17:55:45.475862 14339 net.cpp:453] pool4 <- norm4
I1119 17:55:45.475867 14339 net.cpp:409] pool4 -> pool4
I1119 17:55:45.475873 14339 net.cpp:131] Setting up pool4
I1119 17:55:45.475880 14339 net.cpp:140] Top shape: 100 64 17 6 (652800)
I1119 17:55:45.475884 14339 layer_factory.hpp:75] Creating layer fc6
I1119 17:55:45.475894 14339 net.cpp:99] Creating Layer fc6
I1119 17:55:45.475899 14339 net.cpp:453] fc6 <- pool4
I1119 17:55:45.475905 14339 net.cpp:409] fc6 -> fc6
I1119 17:55:45.475914 14339 net.cpp:131] Setting up fc6
@     0x7f945253219a cv::Mat::Mat()
@     0x7f945c14a3ea caffe::DataTransformer<>::Transform()
@     0x7f945c1cb478 caffe::DataLayer<>::load_batch()
@     0x7f945c1b0f98 caffe::BasePrefetchingDataLayer<>::InternalThreadEntry()
@     0x7f945c144d64 caffe::InternalThread::entry()
@     0x7f945bc4124a (unknown)
@     0x7f945ba1fdf5 start_thread
@     0x7f9446f881ad __clone