我正在使用 Caffe 使用非常简单的 CNN 结构对非图像数据进行分类。我在使用尺寸为 nx 1 x 156 x 12 的 HDF5 数据上训练我的网络时没有问题。但是，我在分类新数据时遇到了困难。
如何在没有任何预处理的情况下进行简单的前向传递？我的数据已经过标准化，并且对于 Caffe 具有正确的尺寸（它已被用于训练网络）。下面是我的代码和 CNN 结构。
编辑：我已将问题隔离到 pycaffe.py 中的函数“_Net_forward”，并发现问题出现在 self.input dict 为空时。谁能解释这是为什么？该集合应该等于来自新测试数据的集合：
if set(kwargs.keys()) != set(self.inputs):
raise Exception('Input blob arguments do not match net inputs.')
我的代码发生了一些变化，因为我现在使用 IO 方法将数据转换为数据（见下文）。这样我就用正确的数据填充了 kwargs 变量。
即使是很小的提示也将不胜感激！
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# Make sure that caffe is on the python path:
caffe_root = '' # this file is expected to be run from {caffe_root}
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
import os
import subprocess
import h5py
import shutil
import tempfile
import sklearn
import sklearn.datasets
import sklearn.linear_model
import skimage.io
def LoadFromHDF5(dataset='test_reduced.h5', path='Bjarke/hdf5_classification/data/'):
f = h5py.File(path + dataset, 'r')
dat = f['data'][:]
f.close()
return dat;
def runModelPython():
model_file = 'Bjarke/hdf5_classification/conv_v2_simple.prototxt'
pretrained = 'Bjarke/hdf5_classification/data/train_iter_10000.caffemodel'
test_data = LoadFromHDF5()
net = caffe.Net(model_file, pretrained)
caffe.set_mode_cpu()
caffe.set_phase_test()
user = test_data[0,:,:,:]
datum = caffe.io.array_to_datum(user.astype(np.uint8))
user_dat = caffe.io.datum_to_array(datum)
user_dat = user_dat.astype(np.uint8)
out = net.forward_all(data=np.asarray([user_dat]))
if __name__ == '__main__':
runModelPython()
CNN 原型
name: "CDR-CNN"
layers {
name: "data"
type: HDF5_DATA
top: "data"
top: "label"
hdf5_data_param {
source: "Bjarke/hdf5_classification/data/train.txt"
batch_size: 10
}
include: { phase: TRAIN }
}
layers {
name: "data"
type: HDF5_DATA
top: "data"
top: "label"
hdf5_data_param {
source: "Bjarke/hdf5_classification/data/test.txt"
batch_size: 10
}
include: { phase: TEST }
}
layers {
name: "feature_conv"
type: CONVOLUTION
bottom: "data"
top: "feature_conv"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 10
kernel_w: 12
kernel_h: 1
stride_w: 1
stride_h: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "feature_conv"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 14
kernel_w: 1
kernel_h: 4
stride_w: 1
stride_h: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_w: 1
kernel_h: 3
stride_w: 1
stride_h: 3
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "pool1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
convolution_param {
num_output: 120
kernel_w: 1
kernel_h: 5
stride_w: 1
stride_h: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
}
}
}
layers {
name: "fc1"
type: INNER_PRODUCT
bottom: "conv2"
top: "fc1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 84
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "fc1"
bottom: "label"
top: "accuracy"
include: { phase: TEST }
}
layers {
name: "loss"
type: SOFTMAX_LOSS
bottom: "fc1"
bottom: "label"
top: "loss"
}