Question

我正在尝试训练神经网络来学习函数y = x1 + x2 + x3。目标是与Caffe一起玩，以便更好地学习和理解它。所需数据在python中综合生成，并作为lmdb数据库文件写入内存。

数据生成代码：

import numpy as np
import lmdb
import caffe

Ntrain = 100
Ntest = 20
K = 3
H = 1
W = 1

Xtrain = np.random.randint(0,1000, size = (Ntrain,K,H,W))
Xtest = np.random.randint(0,1000, size = (Ntest,K,H,W))

ytrain = Xtrain[:,0,0,0] + Xtrain[:,1,0,0] + Xtrain[:,2,0,0]
ytest = Xtest[:,0,0,0] + Xtest[:,1,0,0] + Xtest[:,2,0,0]

env = lmdb.open('expt/expt_train')

for i in range(Ntrain):
    datum = caffe.proto.caffe_pb2.Datum()
    datum.channels = Xtrain.shape[1]
    datum.height = Xtrain.shape[2]
    datum.width = Xtrain.shape[3]
    datum.data = Xtrain[i].tobytes()
    datum.label = int(ytrain[i])
    str_id = '{:08}'.format(i)

    with env.begin(write=True) as txn:
        txn.put(str_id.encode('ascii'), datum.SerializeToString())


env = lmdb.open('expt/expt_test')

for i in range(Ntest):
    datum = caffe.proto.caffe_pb2.Datum()
    datum.channels = Xtest.shape[1]
    datum.height = Xtest.shape[2]
    datum.width = Xtest.shape[3]
    datum.data = Xtest[i].tobytes()
    datum.label = int(ytest[i])
    str_id = '{:08}'.format(i)

    with env.begin(write=True) as txn:
        txn.put(str_id.encode('ascii'), datum.SerializeToString())

Solver.prototext文件：

net: "expt/expt.prototxt"

display: 1
max_iter: 200
test_iter: 20
test_interval: 100

base_lr: 0.000001
momentum: 0.9
# weight_decay: 0.0005

lr_policy: "inv"
# gamma: 0.5
# stepsize: 10
# power: 0.75

snapshot_prefix: "expt/expt"
snapshot_diff: true

solver_mode: CPU
solver_type: SGD

debug_info: true

Caffe模特：

name: "expt"


layer {
    name: "Expt_Data_Train"
    type: "Data"
    top: "data"
    top: "label"    

    include {
        phase: TRAIN
    }

    data_param {
        source: "expt/expt_train"
        backend: LMDB
        batch_size: 1
    }
}


layer {
    name: "Expt_Data_Validate"
    type: "Data"
    top: "data"
    top: "label"    

    include {
        phase: TEST
    }

    data_param {
        source: "expt/expt_test"
        backend: LMDB
        batch_size: 1
    }
}


layer {
    name: "IP"
    type: "InnerProduct"
    bottom: "data"
    top: "ip"

    inner_product_param {
        num_output: 1

        weight_filler {
            type: 'constant'
        }

        bias_filler {
            type: 'constant'
        }
    }
}


layer {
    name: "Loss"
    type: "EuclideanLoss"
    bottom: "ip"
    bottom: "label"
    top: "loss"
}

我得到的测试数据丢失是233,655。这是令人震惊的，因为损失比训练和测试数据集中的数字大三个数量级。此外，要学习的功能是简单的线性函数。我似乎无法弄清楚代码中的错误。任何建议/意见都非常感谢。

Answer 1

在这种情况下产生的损失很多，因为Caffe只接受datum.data格式的数据（即uint8）和datum.label格式的标签（int32）。但是，对于标签，numpy.int64格式似乎也有效。我认为datum.data仅以uint8格式被接受，因为Caffe主要是为计算机视觉任务开发的，其中输入是图像，其RGB值在[0,255]范围内。 uint8可以使用最少的内存来捕获它。我对数据生成代码进行了以下更改：

Xtrain = np.uint8(np.random.randint(0,256, size = (Ntrain,K,H,W)))
Xtest = np.uint8(np.random.randint(0,256, size = (Ntest,K,H,W)))

ytrain = int(Xtrain[:,0,0,0]) + int(Xtrain[:,1,0,0]) + int(Xtrain[:,2,0,0])
ytest = int(Xtest[:,0,0,0]) + int(Xtest[:,1,0,0]) + int(Xtest[:,2,0,0])

在玩了净参数（学习率，迭代次数等）之后，我得到了10 ^（ - 6）的错误，我觉得这很不错！

Caffe：学习简单线性函数时损失极高

1 个答案: