为什么我的CNN返回总是相同的结果？

我正在尝试 Build 一个CNN，它将对象分为3个主要类别 . 这三个对象包括兰博基尼，圆柱头和一块平面 . 我的数据集包含6580张图片，每个类几乎有2200张图片 . 您可以在google drive dataset上查看我的数据集 . 我的CNN的架构是AlexNet，但我已将完全连接的第8层的输出从1000修改为3.我已将这些设置用于训练

test_iter:1000
test_interval:1000
base_lr:0.001
lr_policy:"step"
gamma:0.1
stepsize:2500
max_iter:40000
momentum:0.9
weight_decay:0.0005

但是，问题是当我在训练后部署我的模型时，结果总是如下 {'prob': array([[ 0.33333334, 0.33333334, 0.33333334]], dtype=float32)} .

下面的代码是我加载模型并输出概率向量的脚本 .

import numpy as np
import matplotlib.pyplot as plt
import sys
import caffe
import cv2

MODEL_FILE ='deploy_ex0.prototxt'
PRETRAINED='snapshot_ex0_1_model_iter_40000.caffemodel'

caffe.set_mode_cpu()
net = caffe.Net(MODEL_FILE, PRETRAINED, caffe.TEST)

#preprocessing 

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})

#mean substraction 

mean_file = np.array([104,117,123]) 
transformer.set_mean('data', mean_file)

transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0))
transformer.set_raw_scale('data', 255.0)

#batch size 
net.blobs['data'].reshape(1,3,227,227)

#load image in data layer 

im=cv2.imread('test.jpg', cv2.IMREAD_COLOR)
img =cv2.resize(im, (227,227))

net.blobs['data'].data[...] = transformer.preprocess('data', img)

#compute 

out=net.forward()

print out

我想知道为什么我有这样的结果？你能帮我调试我的CNN吗？

此外，经过培训，我得到了这些结果

I0421 06:56:12.285953  2224 solver.cpp:317] Iteration 40000, loss = 5.06557e-05
I0421 06:56:12.286027  2224 solver.cpp:337] Iteration 40000, Testing net (#0)
I0421 06:58:32.159469  2224 solver.cpp:404]     Test net output #0: accuracy = 0.99898
I0421 06:58:32.159626  2224 solver.cpp:404]     Test net output #1: loss = 0.00183688 (* 1 = 0.00183688 loss)
I0421 06:58:32.159643  2224 solver.cpp:322] Optimization Done.
I0421 06:58:32.159654  2224 caffe.cpp:222] Optimization Done.

谢谢

EDIT AFTER ANSWER OF 11 MAY :

我使用了一个简单的模型1 conv，1 reul，1个池和2个完全连接的层 . 下面的代码是架构规范：

name:"CNN"
layer {
  name: "convnet"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror:true
    crop_size:227
    mean_value:87.6231
    mean_value:87.6757

    mean_value:87.1677
    #mean_file:"/home/jaba/caffe/data/diota_model/mean.binaryproto"
  }
  data_param {
    source: "/home/jaba/caffe/data/diota_model/train_lmdb"
    batch_size: 32
    backend: LMDB
  }
}

layer {
  name: "convnet"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror:true
    crop_size:227
    mean_value:87.6231
    mean_value:87.6757

    mean_value:87.1677
    #mean_file:"/home/jaba/caffe/data/diota_model/mean.binaryproto"
  }
  data_param {
    source: "/home/jaba/caffe/data/diota_model/val_lmdb"
    batch_size: 20
    backend: LMDB
  }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

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: "ip1"
  type: "InnerProduct"
  bottom: "pool1"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 300
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer 
{
   name:"ip2"
   type:"InnerProduct"
   bottom:"ip1"
   top:"ip2"
   param
   {
    lr_mult:1
   }
   param
   {
    lr_mult:2
   }
   inner_product_param 
   {
    num_output: 3
        weight_filler {
          type: "xavier"
        }
        bias_filler {
          type: "constant"
        }
   }

}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip1"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}

layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}

我训练了这个CNN 22个时代，我的准确率达到了86％ . 对于我使用的求解器参数：

net: "/home/jaba/caffe/data/diota_model/simple_model/train_val.prototxt"
test_iter: 50
test_interval: 100
base_lr: 0.00001
momentum: 0.9
weight_decay: 0.0005
lr_policy: "inv"
gamma: 0.0001
power: 0.75
display: 100
max_iter: 3500
snapshot: 100
snapshot_prefix: "/home/jaba/caffe/data/diota_model/simple_model/snap_shot_model"
solver_mode: GPU

现在，当我部署模型时，它不会返回相同的概率向量 . 但是，有一个问题，就是当我加载模型并在validation_lmdb文件夹上测试它时，我没有得到相同的准确度值，我得到了近56％ .

我使用下面的脚本来计算准确度：

import os
import glob
#import cv2
import caffe
import lmdb
import numpy as np
from caffe.proto import caffe_pb2

MODEL_FILE ='deploy.prototxt'
PRETRAINED='snap_shot_model_iter_3500.caffemodel'

caffe.set_mode_cpu()
#load_model

net = caffe.Net(MODEL_FILE, PRETRAINED, caffe.TEST)

#load input and configure preprocessing



#mean_file = np.array([104,117,123])

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
#transformer.set_mean('data', mean_file)
transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0))
transformer.set_raw_scale('data', 255.0)


#fixing the batch size

net.blobs['data'].reshape(1,3,227,227)

lmdb_env=lmdb.open('/home/jaba/caffe/data/diota_model/val1_lmdb')

lmdb_txn=lmdb_env.begin()

lmdb_cursor=lmdb_txn.cursor()

datum=caffe_pb2.Datum()


correct_predictions=0

for key,value in lmdb_cursor:

    datum.ParseFromString(value)

    label=datum.label
    data=caffe.io.datum_to_array(datum)

    image=np.transpose(data,(1,2,0))


    net.blobs['data'].data[...]=transformer.preprocess('data',image)

    out=net.forward()
    out_put=out['prob'].argmax()
    if label==out_put:
    correct_predictions=correct_predictions+1



print 'accuracy :'
print correct_predictions/1002.0

我改变了数据集1002的大小用于测试，4998用于学习 . 你能给我一些解决问题的建议吗？

谢谢 ！