deploy.prototxt
这个文件和训练文件 .prototxt 很相似，但是他是在模型生成后，测试模型使用的配置文件
内容上，除了数据层，其他层都是一模一样的
caffe提供示例的地址：/home/apple/caffe/models/bvlc_reference_caffenet/deploy.prototxt
知识点：
在图像训练阶段如果进行了减均值的操作，那么使用模型的时候，对测试数据也要进行减均值操作
测试模型时，我们可以使用自己数据集的均值文件，可以使用caffe提供的均值文件(为什么使用caffe的均值文件也可以？因为caffe1的均值文件是在超大数据集上做的均值，很具有普遍性，可以近似拿来用)
训练做了哪些预处理
测试也要做那些预处理
cv2等操作读入的是 H*W*C
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caffe读入的是 batch * C * H * W
import numpy as np
import sys
caffe_root = '/home/apple/caffe/' # this file should be run from {caffe_root}/examples (otherwise change this line)
sys.path.insert(0, caffe_root + 'python')
import caffe
caffe.set_mode_cpu()
model_def = '/home/apple/caffe/models/bvlc_reference_caffenet/deploy.prototxt'
model_weights = '/home/apple/caffe_case/bvlc_reference_caffenet.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)
mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values
print 'mean-subtracted values:', zip('BGR', mu)
# create transformer for the input called 'data'
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
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
net.blobs['data'].reshape(1, # batch size
3, # 3-channel (BGR) images
227, 227) # image size is 227x227
image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')
transformed_image = transformer.preprocess('data', 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()运行结果：
apple@apple:~/caffe_case$ python test_model.py
WARNING: Logging before InitGoogleLogging() is written to STDERR
W1216 19:10:03.724741 7088 _caffe.cpp:139] DEPRECATION WARNING - deprecated use of Python interface
W1216 19:10:03.725069 7088 _caffe.cpp:140] Use this instead (with the named "weights" parameter):
W1216 19:10:03.725086 7088 _caffe.cpp:142] Net('/home/apple/caffe/models/bvlc_reference_caffenet/deploy.prototxt', 1, weights='/home/apple/caffe_case/bvlc_reference_caffenet.caffemodel')
I1216 19:10:03.767719 7088 net.cpp:53] Initializing net from parameters:
name: "CaffeNet"
state {
phase: TEST
level: 0
}
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 10
dim: 3
dim: 227
dim: 227
}
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
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"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
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"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
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"
inner_product_param {
num_output: 4096
}
}
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"
inner_product_param {
num_output: 4096
}
}
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: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 1000
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8"
top: "prob"
}
I1216 19:10:03.767989 7088 layer_factory.hpp:77] Creating layer data
I1216 19:10:03.768115 7088 net.cpp:86] Creating Layer data
I1216 19:10:03.768229 7088 net.cpp:382] data -> data
I1216 19:10:03.768355 7088 net.cpp:124] Setting up data
I1216 19:10:03.768426 7088 net.cpp:131] Top shape: 10 3 227 227 (1545870)
I1216 19:10:03.768441 7088 net.cpp:139] Memory required for data: 6183480
I1216 19:10:03.768450 7088 layer_factory.hpp:77] Creating layer conv1
I1216 19:10:03.768465 7088 net.cpp:86] Creating Layer conv1
I1216 19:10:03.768476 7088 net.cpp:408] conv1 <- data
I1216 19:10:03.768486 7088 net.cpp:382] conv1 -> conv1
I1216 19:10:03.769486 7088 net.cpp:124] Setting up conv1
I1216 19:10:03.769539 7088 net.cpp:131] Top shape: 10 96 55 55 (2904000)
I1216 19:10:03.769547 7088 net.cpp:139] Memory required for data: 17799480
I1216 19:10:03.769565 7088 layer_factory.hpp:77] Creating layer relu1
I1216 19:10:03.769577 7088 net.cpp:86] Creating Layer relu1
I1216 19:10:03.769584 7088 net.cpp:408] relu1 <- conv1
I1216 19:10:03.769596 7088 net.cpp:369] relu1 -> conv1 (in-place)
I1216 19:10:03.769608 7088 net.cpp:124] Setting up relu1
I1216 19:10:03.769615 7088 net.cpp:131] Top shape: 10 96 55 55 (2904000)
I1216 19:10:03.769619 7088 net.cpp:139] Memory required for data: 29415480
I1216 19:10:03.769624 7088 layer_factory.hpp:77] Creating layer pool1
I1216 19:10:03.769632 7088 net.cpp:86] Creating Layer pool1
I1216 19:10:03.769637 7088 net.cpp:408] pool1 <- conv1
I1216 19:10:03.769644 7088 net.cpp:382] pool1 -> pool1
I1216 19:10:03.769721 7088 net.cpp:124] Setting up pool1
I1216 19:10:03.769767 7088 net.cpp:131] Top shape: 10 96 27 27 (699840)
I1216 19:10:03.769774 7088 net.cpp:139] Memory required for data: 32214840
I1216 19:10:03.769780 7088 layer_factory.hpp:77] Creating layer norm1
I1216 19:10:03.769799 7088 net.cpp:86] Creating Layer norm1
I1216 19:10:03.769806 7088 net.cpp:408] norm1 <- pool1
I1216 19:10:03.769815 7088 net.cpp:382] norm1 -> norm1
I1216 19:10:03.769888 7088 net.cpp:124] Setting up norm1
I1216 19:10:03.769904 7088 net.cpp:131] Top shape: 10 96 27 27 (699840)
I1216 19:10:03.769909 7088 net.cpp:139] Memory required for data: 35014200
I1216 19:10:03.769914 7088 layer_factory.hpp:77] Creating layer conv2
I1216 19:10:03.769927 7088 net.cpp:86] Creating Layer conv2
I1216 19:10:03.769932 7088 net.cpp:408] conv2 <- norm1
I1216 19:10:03.769942 7088 net.cpp:382] conv2 -> conv2
I1216 19:10:03.884491 7088 net.cpp:124] Setting up conv2
I1216 19:10:03.884582 7088 net.cpp:131] Top shape: 10 256 27 27 (1866240)
I1216 19:10:03.884591 7088 net.cpp:139] Memory required for data: 42479160
I1216 19:10:03.884613 7088 layer_factory.hpp:77] Creating layer relu2
I1216 19:10:03.884627 7088 net.cpp:86] Creating Layer relu2
I1216 19:10:03.884634 7088 net.cpp:408] relu2 <- conv2
I1216 19:10:03.884649 7088 net.cpp:369] relu2 -> conv2 (in-place)
I1216 19:10:03.884701 7088 net.cpp:124] Setting up relu2
I1216 19:10:03.884718 7088 net.cpp:131] Top shape: 10 256 27 27 (1866240)
I1216 19:10:03.884723 7088 net.cpp:139] Memory required for data: 49944120
I1216 19:10:03.884728 7088 layer_factory.hpp:77] Creating layer pool2
I1216 19:10:03.884739 7088 net.cpp:86] Creating Layer pool2
I1216 19:10:03.884744 7088 net.cpp:408] pool2 <- conv2
I1216 19:10:03.884753 7088 net.cpp:382] pool2 -> pool2
I1216 19:10:03.884773 7088 net.cpp:124] Setting up pool2
I1216 19:10:03.884835 7088 net.cpp:131] Top shape: 10 256 13 13 (432640)
I1216 19:10:03.884845 7088 net.cpp:139] Memory required for data: 51674680
I1216 19:10:03.884855 7088 layer_factory.hpp:77] Creating layer norm2
I1216 19:10:03.884877 7088 net.cpp:86] Creating Layer norm2
I1216 19:10:03.884889 7088 net.cpp:408] norm2 <- pool2
I1216 19:10:03.884898 7088 net.cpp:382] norm2 -> norm2
I1216 19:10:03.884912 7088 net.cpp:124] Setting up norm2
I1216 19:10:03.884953 7088 net.cpp:131] Top shape: 10 256 13 13 (432640)
I1216 19:10:03.884960 7088 net.cpp:139] Memory required for data: 53405240
I1216 19:10:03.884995 7088 layer_factory.hpp:77] Creating layer conv3
I1216 19:10:03.885018 7088 net.cpp:86] Creating Layer conv3
I1216 19:10:03.885025 7088 net.cpp:408] conv3 <- norm2
I1216 19:10:03.885103 7088 net.cpp:382] conv3 -> conv3
I1216 19:10:04.065801 7088 net.cpp:124] Setting up conv3
I1216 19:10:04.065944 7088 net.cpp:131] Top shape: 10 384 13 13 (648960)
I1216 19:10:04.065963 7088 net.cpp:139] Memory required for data: 56001080
I1216 19:10:04.065999 7088 layer_factory.hpp:77] Creating layer relu3
I1216 19:10:04.066082 7088 net.cpp:86] Creating Layer relu3
I1216 19:10:04.066152 7088 net.cpp:408] relu3 <- conv3
I1216 19:10:04.066187 7088 net.cpp:369] relu3 -> conv3 (in-place)
I1216 19:10:04.066215 7088 net.cpp:124] Setting up relu3
I1216 19:10:04.066319 7088 net.cpp:131] Top shape: 10 384 13 13 (648960)
I1216 19:10:04.066345 7088 net.cpp:139] Memory required for data: 58596920
I1216 19:10:04.066365 7088 layer_factory.hpp:77] Creating layer conv4
I1216 19:10:04.066390 7088 net.cpp:86] Creating Layer conv4
I1216 19:10:04.066459 7088 net.cpp:408] conv4 <- conv3
I1216 19:10:04.066601 7088 net.cpp:382] conv4 -> conv4
I1216 19:10:04.228473 7088 net.cpp:124] Setting up conv4
I1216 19:10:04.228603 7088 net.cpp:131] Top shape: 10 384 13 13 (648960)
I1216 19:10:04.228621 7088 net.cpp:139] Memory required for data: 61192760
I1216 19:10:04.228643 7088 layer_factory.hpp:77] Creating layer relu4
I1216 19:10:04.228667 7088 net.cpp:86] Creating Layer relu4
I1216 19:10:04.228679 7088 net.cpp:408] relu4 <- conv4
I1216 19:10:04.228781 7088 net.cpp:369] relu4 -> conv4 (in-place)
I1216 19:10:04.228868 7088 net.cpp:124] Setting up relu4
I1216 19:10:04.228936 7088 net.cpp:131] Top shape: 10 384 13 13 (648960)
I1216 19:10:04.229012 7088 net.cpp:139] Memory required for data: 63788600
I1216 19:10:04.229075 7088 layer_factory.hpp:77] Creating layer conv5
I1216 19:10:04.229113 7088 net.cpp:86] Creating Layer conv5
I1216 19:10:04.229135 7088 net.cpp:408] conv5 <- conv4
I1216 19:10:04.229151 7088 net.cpp:382] conv5 -> conv5
I1216 19:10:04.337782 7088 net.cpp:124] Setting up conv5
I1216 19:10:04.337918 7088 net.cpp:131] Top shape: 10 256 13 13 (432640)
I1216 19:10:04.337935 7088 net.cpp:139] Memory required for data: 65519160
I1216 19:10:04.337975 7088 layer_factory.hpp:77] Creating layer relu5
I1216 19:10:04.338062 7088 net.cpp:86] Creating Layer relu5
I1216 19:10:04.338130 7088 net.cpp:408] relu5 <- conv5
I1216 19:10:04.338157 7088 net.cpp:369] relu5 -> conv5 (in-place)
I1216 19:10:04.338183 7088 net.cpp:124] Setting up relu5
I1216 19:10:04.338197 7088 net.cpp:131] Top shape: 10 256 13 13 (432640)
I1216 19:10:04.338207 7088 net.cpp:139] Memory required for data: 67249720
I1216 19:10:04.338215 7088 layer_factory.hpp:77] Creating layer pool5
I1216 19:10:04.338232 7088 net.cpp:86] Creating Layer pool5
I1216 19:10:04.338241 7088 net.cpp:408] pool5 <- conv5
I1216 19:10:04.338266 7088 net.cpp:382] pool5 -> pool5
I1216 19:10:04.338296 7088 net.cpp:124] Setting up pool5
I1216 19:10:04.338310 7088 net.cpp:131] Top shape: 10 256 6 6 (92160)
I1216 19:10:04.338320 7088 net.cpp:139] Memory required for data: 67618360
I1216 19:10:04.338327 7088 layer_factory.hpp:77] Creating layer fc6
I1216 19:10:04.338404 7088 net.cpp:86] Creating Layer fc6
I1216 19:10:04.338421 7088 net.cpp:408] fc6 <- pool5
I1216 19:10:04.338438 7088 net.cpp:382] fc6 -> fc6
I1216 19:10:10.817589 7088 net.cpp:124] Setting up fc6
I1216 19:10:10.817629 7088 net.cpp:131] Top shape: 10 4096 (40960)
I1216 19:10:10.817632 7088 net.cpp:139] Memory required for data: 67782200
I1216 19:10:10.817641 7088 layer_factory.hpp:77] Creating layer relu6
I1216 19:10:10.817648 7088 net.cpp:86] Creating Layer relu6
I1216 19:10:10.817651 7088 net.cpp:408] relu6 <- fc6
I1216 19:10:10.817656 7088 net.cpp:369] relu6 -> fc6 (in-place)
I1216 19:10:10.817662 7088 net.cpp:124] Setting up relu6
I1216 19:10:10.817677 7088 net.cpp:131] Top shape: 10 4096 (40960)
I1216 19:10:10.817690 7088 net.cpp:139] Memory required for data: 67946040
I1216 19:10:10.817693 7088 layer_factory.hpp:77] Creating layer drop6
I1216 19:10:10.817706 7088 net.cpp:86] Creating Layer drop6
I1216 19:10:10.817709 7088 net.cpp:408] drop6 <- fc6
I1216 19:10:10.817713 7088 net.cpp:369] drop6 -> fc6 (in-place)
I1216 19:10:10.817718 7088 net.cpp:124] Setting up drop6
I1216 19:10:10.817731 7088 net.cpp:131] Top shape: 10 4096 (40960)
I1216 19:10:10.817734 7088 net.cpp:139] Memory required for data: 68109880
I1216 19:10:10.817736 7088 layer_factory.hpp:77] Creating layer fc7
I1216 19:10:10.817744 7088 net.cpp:86] Creating Layer fc7
I1216 19:10:10.817745 7088 net.cpp:408] fc7 <- fc6
I1216 19:10:10.817772 7088 net.cpp:382] fc7 -> fc7
I1216 19:10:12.145171 7088 net.cpp:124] Setting up fc7
I1216 19:10:12.145304 7088 net.cpp:131] Top shape: 10 4096 (40960)
I1216 19:10:12.145321 7088 net.cpp:139] Memory required for data: 68273720
I1216 19:10:12.145349 7088 layer_factory.hpp:77] Creating layer relu7
I1216 19:10:12.145380 7088 net.cpp:86] Creating Layer relu7
I1216 19:10:12.145444 7088 net.cpp:408] relu7 <- fc7
I1216 19:10:12.145470 7088 net.cpp:369] relu7 -> fc7 (in-place)
I1216 19:10:12.145493 7088 net.cpp:124] Setting up relu7
I1216 19:10:12.145506 7088 net.cpp:131] Top shape: 10 4096 (40960)
I1216 19:10:12.145514 7088 net.cpp:139] Memory required for data: 68437560
I1216 19:10:12.145521 7088 layer_factory.hpp:77] Creating layer drop7
I1216 19:10:12.145536 7088 net.cpp:86] Creating Layer drop7
I1216 19:10:12.145545 7088 net.cpp:408] drop7 <- fc7
I1216 19:10:12.145668 7088 net.cpp:369] drop7 -> fc7 (in-place)
I1216 19:10:12.145766 7088 net.cpp:124] Setting up drop7
I1216 19:10:12.145831 7088 net.cpp:131] Top shape: 10 4096 (40960)
I1216 19:10:12.145843 7088 net.cpp:139] Memory required for data: 68601400
I1216 19:10:12.145853 7088 layer_factory.hpp:77] Creating layer fc8
I1216 19:10:12.145871 7088 net.cpp:86] Creating Layer fc8
I1216 19:10:12.145880 7088 net.cpp:408] fc8 <- fc7
I1216 19:10:12.145895 7088 net.cpp:382] fc8 -> fc8
I1216 19:10:12.464874 7088 net.cpp:124] Setting up fc8
I1216 19:10:12.464916 7088 net.cpp:131] Top shape: 10 1000 (10000)
I1216 19:10:12.464920 7088 net.cpp:139] Memory required for data: 68641400
I1216 19:10:12.464928 7088 layer_factory.hpp:77] Creating layer prob
I1216 19:10:12.464936 7088 net.cpp:86] Creating Layer prob
I1216 19:10:12.464939 7088 net.cpp:408] prob <- fc8
I1216 19:10:12.464944 7088 net.cpp:382] prob -> prob
I1216 19:10:12.464979 7088 net.cpp:124] Setting up prob
I1216 19:10:12.464996 7088 net.cpp:131] Top shape: 10 1000 (10000)
I1216 19:10:12.465001 7088 net.cpp:139] Memory required for data: 68681400
I1216 19:10:12.465003 7088 net.cpp:202] prob does not need backward computation.
I1216 19:10:12.465006 7088 net.cpp:202] fc8 does not need backward computation.
I1216 19:10:12.465008 7088 net.cpp:202] drop7 does not need backward computation.
I1216 19:10:12.465013 7088 net.cpp:202] relu7 does not need backward computation.
I1216 19:10:12.465014 7088 net.cpp:202] fc7 does not need backward computation.
I1216 19:10:12.465016 7088 net.cpp:202] drop6 does not need backward computation.
I1216 19:10:12.465019 7088 net.cpp:202] relu6 does not need backward computation.
I1216 19:10:12.465023 7088 net.cpp:202] fc6 does not need backward computation.
I1216 19:10:12.465025 7088 net.cpp:202] pool5 does not need backward computation.
I1216 19:10:12.465028 7088 net.cpp:202] relu5 does not need backward computation.
I1216 19:10:12.465041 7088 net.cpp:202] conv5 does not need backward computation.
I1216 19:10:12.465059 7088 net.cpp:202] relu4 does not need backward computation.
I1216 19:10:12.465070 7088 net.cpp:202] conv4 does not need backward computation.
I1216 19:10:12.465072 7088 net.cpp:202] relu3 does not need backward computation.
I1216 19:10:12.465075 7088 net.cpp:202] conv3 does not need backward computation.
I1216 19:10:12.465077 7088 net.cpp:202] norm2 does not need backward computation.
I1216 19:10:12.465080 7088 net.cpp:202] pool2 does not need backward computation.
I1216 19:10:12.465083 7088 net.cpp:202] relu2 does not need backward computation.
I1216 19:10:12.465086 7088 net.cpp:202] conv2 does not need backward computation.
I1216 19:10:12.465088 7088 net.cpp:202] norm1 does not need backward computation.
I1216 19:10:12.465090 7088 net.cpp:202] pool1 does not need backward computation.
I1216 19:10:12.465095 7088 net.cpp:202] relu1 does not need backward computation.
I1216 19:10:12.465096 7088 net.cpp:202] conv1 does not need backward computation.
I1216 19:10:12.465098 7088 net.cpp:202] data does not need backward computation.
I1216 19:10:12.465101 7088 net.cpp:244] This network produces output prob
I1216 19:10:12.465112 7088 net.cpp:257] Network initialization done.
I1216 19:10:18.484563 7088 upgrade_proto.cpp:46] Attempting to upgrade input file specified using deprecated transformation parameters: /home/apple/caffe_case/bvlc_reference_caffenet.caffemodel
I1216 19:10:18.484611 7088 upgrade_proto.cpp:49] Successfully upgraded file specified using deprecated data transformation parameters.
W1216 19:10:18.484616 7088 upgrade_proto.cpp:51] Note that future Caffe releases will only support transform_param messages for transformation fields.
I1216 19:10:18.484617 7088 upgrade_proto.cpp:55] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/apple/caffe_case/bvlc_reference_caffenet.caffemodel
I1216 19:10:56.732518 7088 upgrade_proto.cpp:63] Successfully upgraded file specified using deprecated V1LayerParameter
I1216 19:10:56.795819 7088 net.cpp:746] Ignoring source layer loss
mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]
/usr/local/lib/python2.7/dist-packages/skimage/io/_io.py:49: UserWarning: `as_grey` has been deprecated in favor of `as_gray`
warn('`as_grey` has been deprecated in favor of `as_gray`')
/usr/local/lib/python2.7/dist-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.
warn("Anti-aliasing will be enabled by default in skimage 0.15 to "
predicted class is: 281