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私は、（ログファイルに基づいて）の精度が63％のcaffeで訓練されたモデルを持っています。しかし、Pythonでスクリプトを実行して精度をテストしようとすると、予測値は非常に似ていますが、全く同じではなく、同じクラスのすべての予測が得られます。私の目標は、クラスごとの精度を計算することです。ここCaffeはPythonで常に同じ予測を与えますが、訓練の精度は良いです
は、予測のいくつかの例である：ここ
[ 0.20748076 0.20283087 0.04773897 0.28503627 0.04591063 0.21100247] (label 0)
[ 0.21177764 0.20092578 0.04866471 0.28302929 0.04671735 0.20888527] (label 4)
[ 0.19711637 0.20476575 0.04688895 0.28988105 0.0465695 0.21477833] (label 3)
[ 0.21062914 0.20984225 0.04802448 0.26924771 0.05020727 0.21204917] (label 1)
予測スクリプト（特定画像の予測を与える部分のみ）である：
import numpy as np
import matplotlib.pyplot as plt
import sys
import os
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
# Prepare Network
MODEL_FILE = 'finetune_deploy.prototxt'
PRETRAINED = 'finetune_lr3_iter_25800.caffemodel.h5'
MEAN_FILE = 'balanced_dataset_256/Training/labels_mean/trainingMean_original.binaryproto'
blob = caffe.proto.caffe_pb2.BlobProto()
dataBlob = open(MEAN_FILE , 'rb').read()
blob.ParseFromString(dataBlob)
dataMeanArray = np.array(caffe.io.blobproto_to_array(blob))
mu = dataMeanArray[0].mean(1).mean(1)
net = caffe.Classifier(MODEL_FILE, PRETRAINED,
mean=mu,
channel_swap=(2,1,0),
raw_scale=255,
image_dims=(256, 256))
PREFIX='balanced_dataset_256/PrivateTest/'
LABEL = '1'
imgName = '33408.jpg'
IMAGE_PATH = PREFIX + LABEL + '/' + imgName
input_image = caffe.io.load_image(IMAGE_PATH)
plt.imshow(input_image)
prediction = net.predict([input_image]) # predict takes any number of images, and formats them for the Caffe net automatically
print 'prediction shape:', prediction[0].shape
plt.plot(prediction[0])
print 'predicted class:', prediction[0].argmax()
print prediction[0]
入力データが階調でありますですが、チャンネルを複製してRGBに変換します。
name: "FlickrStyleCaffeNetTest"
layer {
name: "data"
type: "Input"
top: "data"
# top: "label"
input_param { shape: { dim: 1 dim: 3 dim: 256 dim: 256 } }
}
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"
# Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
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: "fc8_flickr"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_flickr"
# lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 6
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8_flickr"
top: "prob"
}
どのようにして.h5形式のcaffemodelファイルを取得しましたか？ – malreddysid
これは、トレーニング中にsnapshot_format：solfでHDF5を指定することによって生成された方法です。 – alinafdima
この形式はPythonスクリプトで読み込むことができますか？あなたはそれが適切であることを確認するために体重をチェックできますか？ – malreddysid