カフェでの画像分類に問題があります。作成したデータの分類に imagenet モデル (caffe チュートリアルから) を使用していますが、常に同じ分類結果 (同じクラス、つまりクラス 3) が得られます。これが私が進める方法です：
Windows には Caffe を使用し、インターフェイスとして Python を使用しています
(1) データを収集します。私のサンプル画像 (トレーニングとテスト) は、サイズが 5x5x3 (RGB) uint8 の画像であるため、そのピクセル値は 0 ～ 255 に達します。
(2) imagenet が必要とするサイズ (256x256x3) にサイズを変更します。そのため、matlab のサイズ変更機能 (最近傍補間) を使用します。
(3) LevelDB と image_mean を作成します。
(4) ネットワークをトレーニングします (3000 回の反復)。imagenet 定義で変更する唯一のパラメーターは、平均画像と LevelDB へのパスです。得られる結果は次のとおりです。
I0428 12:38:04.350100 3236 solver.cpp:245] Train net output #0: loss = 1.91102 (* 1 = 1.91102 loss)
I0428 12:38:04.350100 3236 sgd_solver.cpp:106] Iteration 2900, lr = 0.0001
I0428 12:38:30.353361 3236 solver.cpp:229] Iteration 2920, loss = 2.18008
I0428 12:38:30.353361 3236 solver.cpp:245] Train net output #0: loss = 2.18008 (* 1 = 2.18008 loss)
I0428 12:38:30.353361 3236 sgd_solver.cpp:106] Iteration 2920, lr = 0.0001
I0428 12:38:56.351630 3236 solver.cpp:229] Iteration 2940, loss = 1.90925
I0428 12:38:56.351630 3236 solver.cpp:245] Train net output #0: loss = 1.90925 (* 1 = 1.90925 loss)
I0428 12:38:56.351630 3236 sgd_solver.cpp:106] Iteration 2940, lr = 0.0001
I0428 12:39:22.341891 3236 solver.cpp:229] Iteration 2960, loss = 1.98917
I0428 12:39:22.341891 3236 solver.cpp:245] Train net output #0: loss = 1.98917 (* 1 = 1.98917 loss)
I0428 12:39:22.341891 3236 sgd_solver.cpp:106] Iteration 2960, lr = 0.0001
I0428 12:39:48.334151 3236 solver.cpp:229] Iteration 2980, loss = 2.45919
I0428 12:39:48.334151 3236 solver.cpp:245] Train net output #0: loss = 2.45919 (* 1 = 2.45919 loss)
I0428 12:39:48.334151 3236 sgd_solver.cpp:106] Iteration 2980, lr = 0.0001
I0428 12:40:13.040398 3236 solver.cpp:456] Snapshotting to binary proto file Z:/DeepLearning/S1S2/Stockholm/models_iter_3000.caffemodel
I0428 12:40:15.080418 3236 sgd_solver.cpp:273] Snapshotting solver state to binary proto file Z:/DeepLearning/S1S2/Stockholm/models_iter_3000.solverstate
I0428 12:40:15.820426 3236 solver.cpp:318] Iteration 3000, loss = 2.08741
I0428 12:40:15.820426 3236 solver.cpp:338] Iteration 3000, Testing net (#0)
I0428 12:41:50.398375 3236 solver.cpp:406] Test net output #0: accuracy = 0.11914
I0428 12:41:50.398375 3236 solver.cpp:406] Test net output #1: loss = 2.71476 (* 1 = 2.71476 loss)
I0428 12:41:50.398375 3236 solver.cpp:323] Optimization Done.
I0428 12:41:50.398375 3236 caffe.cpp:222] Optimization Done.
(5) Python で次のコードを実行して、単一の画像を分類します。
# set up Python environment: numpy for numerical routines, and matplotlib for plotting
import numpy as np
import matplotlib.pyplot as plt
# display plots in this notebook
# set display defaults
plt.rcParams['figure.figsize'] = (10, 10) # large images
plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels
plt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap
# The caffe module needs to be on the Python path;
# we'll add it here explicitly.
import sys
caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)
sys.path.insert(0, caffe_root + 'python')
import caffe
# If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.
caffe.set_mode_cpu()
model_def = 'C:/Caffe/caffe-windows-master/models/bvlc_reference_caffenet/deploy.prototxt'
model_weights = 'Z:/DeepLearning/S1S2/Stockholm/models_iter_3000.caffemodel'
net = caffe.Net(model_def, # defines the structure of the model
model_weights, # contains the trained weights
caffe.TEST) # use test mode (e.g., don't perform dropout)
#load mean image file and convert it to a .npy file--------------------------------
blob = caffe.proto.caffe_pb2.BlobProto()
data = open('Z:/DeepLearning/S1S2/Stockholm/S1S2train256.binaryproto',"rb").read()
blob.ParseFromString(data)
nparray = caffe.io.blobproto_to_array(blob)
f = file('Z:/DeepLearning/PythonCalssification/imgmean.npy',"wb")
np.save(f,nparray)
f.close()
# load the mean ImageNet image (as distributed with Caffe) for subtraction
mu1 = np.load('Z:/DeepLearning/PythonCalssification/imgmean.npy')
mu1 = mu1.squeeze()
mu = mu1.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values
print 'mean-subtracted values:', zip('BGR', mu)
print 'mean shape: ',mu1.shape
print 'data shape: ',net.blobs['data'].data.shape
# create transformer for the input called 'data'
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
# set the size of the input (we can skip this if we're happy
transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension
transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel
transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]
transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR
# set the size of the input (we can skip this if we're happy
# with the default; we can also change it later, e.g., for different batch sizes)
net.blobs['data'].reshape(50, # batch size
3, # 3-channel (BGR) images
227, 227) # image size is 227x227
#load image
image = caffe.io.load_image('Z:/DeepLearning/PythonCalssification/380.tiff')
transformed_image = transformer.preprocess('data', image)
#plt.imshow(image)
# copy the image data into the memory allocated for the net
net.blobs['data'].data[...] = transformed_image
### perform classification
output = net.forward()
output_prob = output['prob'][0] # the output probability vector for the first image in the batch
print 'predicted class is:', output_prob.argmax()
どの入力画像を使用しても、分類結果として常にクラス「3」が得られます。これが私がトレーニング/分類するサンプル画像です:
誰かが何が悪いのか考えてくれたらとても嬉しいです? 前もって感謝します！