machine-learning - Tflearn 中的分类器质量差？

Question

我是机器学习新手，正在尝试TFlearn，因为它很简单。

我正在尝试制作一个我觉得有趣的基本分类器。我的目标是训练系统预测一个点所在的方向。

例如，如果我输入两个 2D 坐标 (50,50) 和 (51,51)，系统必须预测方向为 NE(北)东方)。如果我输入 (50,50) 和 (49,49) 系统必须预测方向为 SW(西南)

输入: X1,Y1,X2,Y2,标签
输出:0到8。对于8个方向。

这是我写的小代码，

from __future__ import print_function
import numpy as np
import tflearn
import tensorflow as tf
import time
from tflearn.data_utils import load_csv

#Sample input 50,50,51,51,5
data, labels = load_csv(filename, target_column=4,
                        categorical_labels=True, n_classes=8)

my_optimizer = tflearn.SGD(learning_rate=0.1)
net = tflearn.input_data(shape=[None, 4])
net = tflearn.fully_connected(net, 32) #input 4, output 32
net = tflearn.fully_connected(net, 32) #input 32, output 32
net = tflearn.fully_connected(net, 8, activation='softmax')
net = tflearn.regression(net,optimizer=my_optimizer)

model = tflearn.DNN(net)

model.fit(data, labels, n_epoch=100, batch_size=100000, show_metric=True)

model.save("direction-classifier.tfl")

我面临的问题是，即使在我传递了大约 4000 万个输入样本之后，系统的准确率仍低至 20%。
我将输入限制为 40-x-60 和 40-y-60

我无法理解我是否过度拟合了样本，因为在总共 4000 万个输入的整个训练期间，准确率一直不高

为什么这个简单示例的准确度如此之低？

编辑:我降低了学习率并减小了批量大小。然而，结果仍然相同，但准确性非常差。我已包含前 25 个步骤的输出。

--
Training Step: 100000  | total loss: 6.33983 | time: 163.327s
| SGD | epoch: 001 | loss: 6.33983 - acc: 0.0663 -- iter: 999999/999999
--
Training Step: 200000  | total loss: 6.84055 | time: 161.981ss
| SGD | epoch: 002 | loss: 6.84055 - acc: 0.1568 -- iter: 999999/999999
--
Training Step: 300000  | total loss: 5.90203 | time: 158.853ss
| SGD | epoch: 003 | loss: 5.90203 - acc: 0.1426 -- iter: 999999/999999
--
Training Step: 400000  | total loss: 5.97782 | time: 157.607ss
| SGD | epoch: 004 | loss: 5.97782 - acc: 0.1465 -- iter: 999999/999999
--
Training Step: 500000  | total loss: 5.97215 | time: 155.929ss
| SGD | epoch: 005 | loss: 5.97215 - acc: 0.1234 -- iter: 999999/999999
--
Training Step: 600000  | total loss: 6.86967 | time: 157.299ss
| SGD | epoch: 006 | loss: 6.86967 - acc: 0.1230 -- iter: 999999/999999
--
Training Step: 700000  | total loss: 6.10330 | time: 158.137ss
| SGD | epoch: 007 | loss: 6.10330 - acc: 0.1242 -- iter: 999999/999999
--
Training Step: 800000  | total loss: 5.81901 | time: 157.464ss
| SGD | epoch: 008 | loss: 5.81901 - acc: 0.1464 -- iter: 999999/999999
--
Training Step: 900000  | total loss: 7.09744 | time: 157.486ss
| SGD | epoch: 009 | loss: 7.09744 - acc: 0.1359 -- iter: 999999/999999
--
Training Step: 1000000  | total loss: 7.19259 | time: 158.369s
| SGD | epoch: 010 | loss: 7.19259 - acc: 0.1248 -- iter: 999999/999999
--
Training Step: 1100000  | total loss: 5.60177 | time: 157.221ss
| SGD | epoch: 011 | loss: 5.60177 - acc: 0.1378 -- iter: 999999/999999
--
Training Step: 1200000  | total loss: 7.16676 | time: 158.607ss
| SGD | epoch: 012 | loss: 7.16676 - acc: 0.1210 -- iter: 999999/999999
--
Training Step: 1300000  | total loss: 6.19163 | time: 163.711ss
| SGD | epoch: 013 | loss: 6.19163 - acc: 0.1635 -- iter: 999999/999999
--
Training Step: 1400000  | total loss: 7.46101 | time: 162.091ss
| SGD | epoch: 014 | loss: 7.46101 - acc: 0.1216 -- iter: 999999/999999
--
Training Step: 1500000  | total loss: 7.78055 | time: 158.468ss
| SGD | epoch: 015 | loss: 7.78055 - acc: 0.1122 -- iter: 999999/999999
--
Training Step: 1600000  | total loss: 6.03101 | time: 158.251ss
| SGD | epoch: 016 | loss: 6.03101 - acc: 0.1103 -- iter: 999999/999999
--
Training Step: 1700000  | total loss: 5.59769 | time: 158.083ss
| SGD | epoch: 017 | loss: 5.59769 - acc: 0.1182 -- iter: 999999/999999
--
Training Step: 1800000  | total loss: 5.45591 | time: 158.088ss
| SGD | epoch: 018 | loss: 5.45591 - acc: 0.0868 -- iter: 999999/999999
--
Training Step: 1900000  | total loss: 6.54951 | time: 157.755ss
| SGD | epoch: 019 | loss: 6.54951 - acc: 0.1353 -- iter: 999999/999999
--
Training Step: 2000000  | total loss: 6.18566 | time: 157.408ss
| SGD | epoch: 020 | loss: 6.18566 - acc: 0.0551 -- iter: 999999/999999
--
Training Step: 2100000  | total loss: 4.95146 | time: 157.572ss
| SGD | epoch: 021 | loss: 4.95146 - acc: 0.1114 -- iter: 999999/999999
--
Training Step: 2200000  | total loss: 5.97208 | time: 157.279ss
| SGD | epoch: 022 | loss: 5.97208 - acc: 0.1277 -- iter: 999999/999999
--
Training Step: 2300000  | total loss: 6.75645 | time: 157.201ss
| SGD | epoch: 023 | loss: 6.75645 - acc: 0.1507 -- iter: 999999/999999
--
Training Step: 2400000  | total loss: 7.04119 | time: 157.346ss
| SGD | epoch: 024 | loss: 7.04119 - acc: 0.1512 -- iter: 999999/999999
--
Training Step: 2500000  | total loss: 5.95451 | time: 157.722ss
| SGD | epoch: 025 | loss: 5.95451 - acc: 0.1421 -- iter: 999999/999999

Answer 1

正如我在上面的评论中所讨论的，这里是使用 a MLP helper class I created 训练多层感知器分类器模型的代码。该类使用 TensorFlow 实现，并遵循 scikit-learn 拟合、预测、评分接口(interface)。

基本思想是生成随机起点和终点，然后使用字典根据方向创建标签。我用过np.unique查找生成数据中的类标签数量，因为它可能会变化(某些方向可能会丢失)。当起点和终点相同时，我还添加了一个空字符串标签。

代码

使用下面的代码，我能够在某些运行中实现 100% 的交叉验证准确性。 将 numpy 导入为 np 从 sklearn.model_selection 导入 ShuffleSplit 从 TFANN 导入 MLPC

#Dictionary to lookup direction ()
DM = {(-1, -1):'SW', (-1, 0):'W', (-1,  1):'NW', (0,  1):'N', 
      ( 1,  1):'NE', ( 1, 0):'E', ( 1, -1):'SE', (0, -1):'S',
      ( 0,  0):''}

NR = 4096       #Number of rows in sample matrix
A1 = np.random.randint(40, 61, size = (NR, 2))      #Random starting point
A2 = np.random.randint(40, 61, size = (NR, 2))      #Random ending point
A = np.hstack([A1, A2])         #Concat start and end point as feature vector
#Create label from direction vector
Y = np.array([DM[(x, y)] for x, y in (A2 - A1).clip(-1, 1)])
NC = len(np.unique(Y))          #Number of classes
ss = ShuffleSplit(n_splits = 1)
trn, tst = next(ss.split(A))    #Make a train/test split for cross-validation
#%% Create and train Multi-Layer Perceptron for Classification (MLPC)
l = [4, 6, 6, NC]       #Neuron counts in each layer
mlpc = MLPC(l, batchSize = 64, maxIter = 128, verbose = True)
mlpc.fit(A[trn], Y[trn])
s1 = mlpc.score(A[trn], Y[trn])     #Training accuracy
s2 = mlpc.score(A[tst], Y[tst])     #Testing accuracy
s3 = mlpc.score(A, Y)               #Total accuracy
print('Trn: {:05f}\tTst: {:05f}\tAll: {:05f}'.format(s1, s2, s3))

结果

这是上述代码在我的机器上运行的示例:

Iter     1            2.59423236 (Batch Size:    64)
Iter     2            2.25392553 (Batch Size:    64)
Iter     3            2.02569708 (Batch Size:    64)
...
Iter    12            1.53575111 (Batch Size:    64)
Iter    13            1.47963311 (Batch Size:    64)
Iter    14            1.42776408 (Batch Size:    64)
...
Iter    83            0.23911642 (Batch Size:    64)
Iter    84            0.22893350 (Batch Size:    64)
Iter    85            0.23644384 (Batch Size:    64)
...
Iter    94            0.21170238 (Batch Size:    64)
Iter    95            0.20718799 (Batch Size:    64)
Iter    96            0.21230888 (Batch Size:    64)
...
Iter   126            0.17334313 (Batch Size:    64)
Iter   127            0.16970796 (Batch Size:    64)
Iter   128            0.15931854 (Batch Size:    64)
Trn: 0.995659   Tst: 1.000000   All: 0.996094