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我想设计一个网络，需要一个IMAGE（224x224x3）和3个参数（X，Y，R）学习关系。Caffe：检查失败：形状[i]> = 0（-1与0）错误
我的输入是一个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 < - 数据 I0612 17：17：38.315107 9991 net.cpp：380] CONV1 - > conv1 F0612 17：17：38.352540 9991 blob.cpp：32]检查失败：形状[i]> = 0（-1与0）“
我已经创建了一个输入到caffe的HDF5数据集。我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
我创建一个数据（224,224,3）领域，并在HDF5数据集的标签（3,1）。
现在我的朱古力模型如下：
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"
}
详细的错误如下： -
“创建图层CONV1 I0612 17：17：38.315083 9991 net.cpp：406] CONV1 < - data I0612 17：17：38.315107 9991 net.cpp：380] conv1 - > conv1 F0612 17：17：38.352540 9991 blob.cpp：32] Check failed：shape [i]> = 0（-1 vs. 0）“
任何人都可以帮忙吗？
当我试图改变无17 数据= np.zeros（（split_after，3，尺寸，尺寸），D型细胞=“F4”） 我得到一个错误 无法从广播形状输入阵列线（ 227,227,3）成形（3,227,227） –
您需要转置输入 – Shai
它现在可以工作！但是在运行之后，在验证过程中总是会为所有测试图像生成相同的输出。 –