Dropout layer ignored - "Warning: had one or more problems upgrading V1LayerParameter"
60 views
Skip to first unread message
Bjarke Felbo
Apr 7, 2015, 9:34:42 PM4/7/15
to caffe...@googlegroups.com
I'm trying to add a dropout layer based on the Imagenet example (see code below). However, it seems to be ignored it's not printed as being part of the network when I train the model and I get the warning message below. I have the newest version of caffe installed. What should I do to include it properly?
Dropout layer:
layer {
name: "drop4"
type: "Dropout"
bottom: "fc4"
top: "fc4"
dropout_param {
dropout_ratio: 0.5
}
}
Training log:
I0407 21:05:06.809166 5962 caffe.cpp:117] Use CPU.
I0407 21:05:06.809468 5962 caffe.cpp:121] Starting Optimization
I0407 21:05:06.809532 5962 solver.cpp:32] Initializing solver from parameters:
test_iter: 100
test_interval: 500
base_lr: 0.01
display: 100
max_iter: 10000
lr_policy: "fixed"
gamma: 0.0001
power: 0.75
weight_decay: 0.0005
snapshot: 1000
snapshot_prefix: "hdf5_classification/data/train"
solver_mode: CPU
net: "hdf5_classification/cnn_train.prototxt"
solver_type: ADAGRAD
I0407 21:05:06.809566 5962 solver.cpp:70] Creating training net from net file: hdf5_classification/cnn_train.prototxt
E0407 21:05:06.809836 5962 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: hdf5_classification/cnn_train.prototxt
E0407 21:05:06.809968 5962 upgrade_proto.cpp:636] Input NetParameter to be upgraded already specifies 'layer' fields; these will be ignored for the upgrade.
E0407 21:05:06.810035 5962 upgrade_proto.cpp:623] Warning: had one or more problems upgrading V1LayerParameter (see above); continuing anyway.
I0407 21:05:06.810108 5962 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0407 21:05:06.810132 5962 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 21:05:06.810143 5962 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer pred
I0407 21:05:06.810266 5962 net.cpp:42] Initializing net from parameters:
name: "CDR-CNN"
state {
phase: TRAIN
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "hdf5_classification/data/train.txt"
batch_size: 10
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 16
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
kernel_h: 1
kernel_w: 3
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_h: 1
kernel_w: 2
stride_h: 1
stride_w: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
kernel_h: 1
kernel_w: 11
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "conv3"
type: "Convolution"
bottom: "conv2"
top: "conv3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 120
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
kernel_h: 7
kernel_w: 1
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "fc4"
type: "InnerProduct"
bottom: "conv3"
top: "fc4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 84
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "fc4"
top: "fc4"
}
layer {
name: "fc5"
type: "InnerProduct"
bottom: "fc4"
top: "fc5"
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: "fc5"
bottom: "label"
top: "loss"
include {
phase: TRAIN
}
}
I0407 21:05:06.810355 5962 layer_factory.hpp:74] Creating layer data
I0407 21:05:06.811199 5962 net.cpp:84] Creating Layer data
I0407 21:05:06.811228 5962 net.cpp:338] data -> data
I0407 21:05:06.811259 5962 net.cpp:338] data -> label
I0407 21:05:06.811285 5962 net.cpp:113] Setting up data
I0407 21:05:06.811301 5962 hdf5_data_layer.cpp:80] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt
I0407 21:05:06.811357 5962 hdf5_data_layer.cpp:94] Number of HDF5 files: 1
I0407 21:05:06.853078 5962 net.cpp:120] Top shape: 10 14 7 24 (23520)
I0407 21:05:06.853113 5962 net.cpp:120] Top shape: 10 (10)
I0407 21:05:06.853132 5962 layer_factory.hpp:74] Creating layer conv1
I0407 21:05:06.853159 5962 net.cpp:84] Creating Layer conv1
I0407 21:05:06.853175 5962 net.cpp:380] conv1 <- data
I0407 21:05:06.853199 5962 net.cpp:338] conv1 -> conv1
I0407 21:05:06.853221 5962 net.cpp:113] Setting up conv1
I0407 21:05:06.853638 5962 net.cpp:120] Top shape: 10 16 7 22 (24640)
I0407 21:05:06.853665 5962 layer_factory.hpp:74] Creating layer relu1
I0407 21:05:06.853684 5962 net.cpp:84] Creating Layer relu1
I0407 21:05:06.853698 5962 net.cpp:380] relu1 <- conv1
I0407 21:05:06.853713 5962 net.cpp:327] relu1 -> conv1 (in-place)
I0407 21:05:06.853727 5962 net.cpp:113] Setting up relu1
I0407 21:05:06.853744 5962 net.cpp:120] Top shape: 10 16 7 22 (24640)
I0407 21:05:06.853757 5962 layer_factory.hpp:74] Creating layer pool1
I0407 21:05:06.853772 5962 net.cpp:84] Creating Layer pool1
I0407 21:05:06.853785 5962 net.cpp:380] pool1 <- conv1
I0407 21:05:06.853799 5962 net.cpp:338] pool1 -> pool1
I0407 21:05:06.853814 5962 net.cpp:113] Setting up pool1
I0407 21:05:06.853837 5962 net.cpp:120] Top shape: 10 16 7 11 (12320)
I0407 21:05:06.853849 5962 layer_factory.hpp:74] Creating layer conv2
I0407 21:05:06.853867 5962 net.cpp:84] Creating Layer conv2
I0407 21:05:06.853878 5962 net.cpp:380] conv2 <- pool1
I0407 21:05:06.853893 5962 net.cpp:338] conv2 -> conv2
I0407 21:05:06.853909 5962 net.cpp:113] Setting up conv2
I0407 21:05:06.854030 5962 net.cpp:120] Top shape: 10 20 7 1 (1400)
I0407 21:05:06.854048 5962 layer_factory.hpp:74] Creating layer relu2
I0407 21:05:06.854063 5962 net.cpp:84] Creating Layer relu2
I0407 21:05:06.854074 5962 net.cpp:380] relu2 <- conv2
I0407 21:05:06.854087 5962 net.cpp:327] relu2 -> conv2 (in-place)
I0407 21:05:06.854100 5962 net.cpp:113] Setting up relu2
I0407 21:05:06.854113 5962 net.cpp:120] Top shape: 10 20 7 1 (1400)
I0407 21:05:06.854125 5962 layer_factory.hpp:74] Creating layer conv3
I0407 21:05:06.854140 5962 net.cpp:84] Creating Layer conv3
I0407 21:05:06.854152 5962 net.cpp:380] conv3 <- conv2
I0407 21:05:06.854166 5962 net.cpp:338] conv3 -> conv3
I0407 21:05:06.854179 5962 net.cpp:113] Setting up conv3
I0407 21:05:06.854748 5962 net.cpp:120] Top shape: 10 120 1 1 (1200)
I0407 21:05:06.854771 5962 layer_factory.hpp:74] Creating layer relu3
I0407 21:05:06.854785 5962 net.cpp:84] Creating Layer relu3
I0407 21:05:06.854797 5962 net.cpp:380] relu3 <- conv3
I0407 21:05:06.854811 5962 net.cpp:327] relu3 -> conv3 (in-place)
I0407 21:05:06.854825 5962 net.cpp:113] Setting up relu3
I0407 21:05:06.854838 5962 net.cpp:120] Top shape: 10 120 1 1 (1200)
I0407 21:05:06.854851 5962 layer_factory.hpp:74] Creating layer fc4
I0407 21:05:06.854871 5962 net.cpp:84] Creating Layer fc4
I0407 21:05:06.854883 5962 net.cpp:380] fc4 <- conv3
I0407 21:05:06.854897 5962 net.cpp:338] fc4 -> fc4
I0407 21:05:06.854912 5962 net.cpp:113] Setting up fc4
I0407 21:05:06.855232 5962 net.cpp:120] Top shape: 10 84 (840)
I0407 21:05:06.855252 5962 layer_factory.hpp:74] Creating layer relu4
I0407 21:05:06.855267 5962 net.cpp:84] Creating Layer relu4
I0407 21:05:06.855278 5962 net.cpp:380] relu4 <- fc4
I0407 21:05:06.855406 5962 net.cpp:327] relu4 -> fc4 (in-place)
I0407 21:05:06.855432 5962 net.cpp:113] Setting up relu4
I0407 21:05:06.855447 5962 net.cpp:120] Top shape: 10 84 (840)
I0407 21:05:06.855458 5962 layer_factory.hpp:74] Creating layer fc5
I0407 21:05:06.855582 5962 net.cpp:84] Creating Layer fc5
I0407 21:05:06.855614 5962 net.cpp:380] fc5 <- fc4
I0407 21:05:06.855631 5962 net.cpp:338] fc5 -> fc5
I0407 21:05:06.855648 5962 net.cpp:113] Setting up fc5
I0407 21:05:06.855674 5962 net.cpp:120] Top shape: 10 2 (20)
I0407 21:05:06.855690 5962 layer_factory.hpp:74] Creating layer loss
I0407 21:05:06.855710 5962 net.cpp:84] Creating Layer loss
I0407 21:05:06.855721 5962 net.cpp:380] loss <- fc5
I0407 21:05:06.855734 5962 net.cpp:380] loss <- label
I0407 21:05:06.855751 5962 net.cpp:338] loss -> loss
I0407 21:05:06.855768 5962 net.cpp:113] Setting up loss
I0407 21:05:06.855785 5962 layer_factory.hpp:74] Creating layer loss
I0407 21:05:06.855813 5962 net.cpp:120] Top shape: (1)
I0407 21:05:06.855825 5962 net.cpp:122] with loss weight 1
I0407 21:05:06.855854 5962 net.cpp:167] loss needs backward computation.
I0407 21:05:06.855865 5962 net.cpp:167] fc5 needs backward computation.
I0407 21:05:06.855877 5962 net.cpp:167] relu4 needs backward computation.
I0407 21:05:06.855890 5962 net.cpp:167] fc4 needs backward computation.
I0407 21:05:06.855901 5962 net.cpp:167] relu3 needs backward computation.
I0407 21:05:06.855912 5962 net.cpp:167] conv3 needs backward computation.
I0407 21:05:06.855924 5962 net.cpp:167] relu2 needs backward computation.
I0407 21:05:06.855937 5962 net.cpp:167] conv2 needs backward computation.
I0407 21:05:06.855947 5962 net.cpp:167] pool1 needs backward computation.
I0407 21:05:06.855959 5962 net.cpp:167] relu1 needs backward computation.
I0407 21:05:06.855970 5962 net.cpp:167] conv1 needs backward computation.
I0407 21:05:06.855983 5962 net.cpp:169] data does not need backward computation.
I0407 21:05:06.855994 5962 net.cpp:205] This network produces output loss
I0407 21:05:06.856011 5962 net.cpp:447] Collecting Learning Rate and Weight Decay.
I0407 21:05:06.856029 5962 net.cpp:217] Network initialization done.
I0407 21:05:06.856041 5962 net.cpp:218] Memory required for data: 368124
Reply all
Reply to author
Forward
0 new messages