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我有一個訓練有素的caffe模型（通過命令行），我得到了一個的準確率（根據日誌文件）的63％。但是，當我嘗試在Python中運行腳本來測試準確性時，我得到了同一類中的所有預測值，但預測值非常相似，但不完全相同。我的目標是計算每班的準確度。Caffe在Python中總是給出相同的預測，但是訓練的準確性還是不錯的
下面是預測的一些例子：
[ 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)
這裏是預測腳本（只有一部分給出了特定圖像的預測）：
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]
輸入數據是灰度，但我通過複製頻道將其轉換爲RGB。
這裏的架構文件finetune_deploy.prototxt：
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"
}
你是如何得到.h5格式的caffemodel文件的？ – malreddysid
在培訓過程中，通過在求解器中指定snapshot_format：HDF5來生成它是如何產生的 – alinafdima
您確定可以通過python腳本讀入這種格式嗎？你可以檢查重量，看看它是適當的嗎？ – malreddysid