我正在尝试设计一个采用IMAGE（224x224x3）和3个参数（X，Y，R）的网络来学习这种关系。
我的输入是HDF5数据集。我收到以下错误：
“创建图层conv1 I0612 17：17：38.315083 9991 net.cpp：406] conv1＆lt; - data I0612 17：17：38.315107 9991 net.cpp：380] conv1 - ＆gt; CONV1 F0612 17：17：38.352540 9991 blob.cpp：32]检查失败：shape [i]＆gt; = 0（-1 vs. 0）“
我创建了一个HDF5数据集，用于输入caffe。我的create_dataset代码如下： -
import h5py, os
import caffe
import numpy as np
SIZE = 224
with open( 'val.txt', 'r' ) as T :
lines = T.readlines()
count_files = 0
split_after = 199
count = -1
# If you do not have enough memory split data into
# multiple batches and generate multiple separate h5 files
data = np.zeros( (split_after,SIZE, SIZE,3), dtype='f4' )
label = np.zeros( (split_after,3, 1), dtype='f4' )
for i,l in enumerate(lines):
count += 1
sp = l.split(' ')
img = caffe.io.load_image( sp[0] )
data[count] = img
label[count][0] = float(sp[1])
label[count][1] = float(sp[2])
label[count][2] = float(sp[3])
#print y1[count]
if (count+1) == split_after:
with h5py.File('val_' + str(count_files) + '.h5','w') as H:
H.create_dataset( 'data', data=data ) # note the name X given to the dataset!
H.create_dataset( 'label', data=label )
data = np.zeros( (split_after, SIZE, SIZE, 3), dtype='f4' )
label = np.zeros( (split_after,3, 1), dtype='f4' )
with open('val1.txt','a') as L:
L.write( 'val_' + str(count_files) + '.h5') # list all h5 files you are going to use
count_files += 1
count = 0
我在HDF5数据集中创建数据（224,224,3）字段和标签（3,1）。
现在我的caffe模型如下：
name: "CaffeNet"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
hdf5_data_param {
source: "/home/arijit/Downloads/caffe/Circle/test1.txt"
batch_size: 256
shuffle: true
}
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TEST
}
hdf5_data_param {
source: "/home/arijit/Downloads/caffe/Circle/val1.txt"
batch_size: 16
}
}
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: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
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: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 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: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 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: "fc8ft"
type: "InnerProduct"
bottom: "fc7"
top: "fc8ft"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "fc8ft"
bottom: "label"
top: "loss"
}
详细错误如下： -
“创建图层conv1 I0612 17：17：38.315083 9991 net.cpp：406] conv1＆lt; - data I0612 17：17：38.315107 9991 net.cpp：380] conv1 - ＆gt; CONV1 F0612 17：17：38.352540 9991 blob.cpp：32]检查失败：shape [i]＆gt; = 0（-1 vs. 0）“
有人可以帮忙吗？
答案 0 :(得分：1)
我收到了相同的错误消息。但是看来我的情况与发问者不同。
遇到此错误是因为我在网络中添加了太多的卷积和池化层，并且图像最终缩小为1x1，在这种情况下，如果我继续添加卷积层，将无法执行卷积，并且出现此错误
以防其他人遇到我的问题，我将其发布在这里。
答案 1 :(得分：0)
你的输入图像的形状向后：
caffe不是H x W x 3，而是3 x W x H。
有关在hdf5文件中为caffe排列数组的更多详细信息，请参阅this answer。
PS，
您的label数组中不需要Singleton维度。