我有存储在 HDF5 文件中的灰度格式的 96x96 像素图像。我正在尝试使用 caffe 进行多输出回归，但是卷积不起作用。这里到底有什么问题？为什么卷积不起作用？
I0122 17:18:39.474860 5074 net.cpp:67] Creating Layer fkp
I0122 17:18:39.474889 5074 net.cpp:356] fkp -> data
I0122 17:18:39.474930 5074 net.cpp:356] fkp -> label
I0122 17:18:39.474967 5074 net.cpp:96] Setting up fkp
I0122 17:18:39.474987 5074 hdf5_data_layer.cpp:57] Loading filename from train.txt
I0122 17:18:39.475103 5074 hdf5_data_layer.cpp:69] Number of files: 1
I0122 17:18:39.475131 5074 hdf5_data_layer.cpp:29] Loading HDF5 filefacialkp-train.hd5
I0122 17:18:40.337786 5074 hdf5_data_layer.cpp:49] Successully loaded 4934 rows
I0122 17:18:40.337862 5074 hdf5_data_layer.cpp:81] output data size: 100,9216,1,1
I0122 17:18:40.337906 5074 net.cpp:103] Top shape: 100 9216 1 1 (921600)
I0122 17:18:40.337929 5074 net.cpp:103] Top shape: 100 30 1 1 (3000)
I0122 17:18:40.337971 5074 net.cpp:67] Creating Layer conv1
I0122 17:18:40.338001 5074 net.cpp:394] conv1 <- data
I0122 17:18:40.338069 5074 net.cpp:356] conv1 -> conv1
I0122 17:18:40.338109 5074 net.cpp:96] Setting up conv1
F0122 17:18:40.599761 5074 blob.cpp:13] Check failed: height >= 0 (-3 vs. 0)
我的prototxt层文件是这样的
name: "LogReg"
layers {
top: "data"
top: "label"
name: "fkp"
type: HDF5_DATA
hdf5_data_param {
source: "train.txt"
batch_size: 100
}
include {
phase: TRAIN
}
}
layers {
bottom: "data"
top: "conv1"
name: "conv1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 64
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layers {
bottom: "conv1"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool1"
top: "conv2"
name: "conv2"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 256
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layers {
bottom: "conv2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool2"
top: "ip1"
name: "ip1"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layers {
bottom: "ip1"
top: "ip1"
name: "relu1"
type: RELU
}
layers {
bottom: "ip1"
top: "ip2"
name: "ip2"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
inner_product_param {
num_output: 30
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layers {
bottom: "ip2"
bottom: "label"
top: "loss"
name: "loss"
type: EUCLIDEAN_LOSS
}