Machine learning Tflearn中的低质量分类器？

我对机器学习和尝试TFlearn是新手，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点所在的方向

例如，如果我输入两个二维坐标

（50,50）

和

（51,51）

，系统必须预测方向为NE（东北）。 如果输入

（50,50）

和

（49,49）

，系统必须预测方向为西南（西南）

输入：X1，Y1，X2，Y2，标签
输出：0到8。八个方向

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，系统的准确度也低至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4000万次输入的整个培训期间，准确度从来都不高

为什么这个简单例子的准确度这么低

编辑： 我已经降低了学习速度，并使批量变小。然而，结果仍然相同，但精确度很低。 我已经包括了前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

---

正如我在上面的评论中所讨论的，下面的代码使用。该类使用TensorFlow实现，并遵循scikit学习拟合、预测和评分界面

基本思想是生成一个随机的起点和终点，然后使用字典根据方向创建标签。我曾经在生成的数据中查找类标签的数量，因为它可能会变化（某些方向可能会丢失）。我还包括了一个空字符串标签，用于开始点和结束点相同的情况

代码 使用下面的代码，我能够在一些运行中实现100%的交叉验证准确性。 将numpy作为np导入 从sklearn.model_选择导入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 这是在我的机器上运行上述代码的示例：

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

---

结果是优化器导致了所有问题。当自定义优化器被移除时，损失开始正确下降，精确度提高到99%

必须修改以下两行

my_optimizer = tflearn.SGD(learning_rate=0.1)
net = tflearn.regression(net,optimizer=my_optimizer)

当替换为

net = tflearn.regression(net)

---

产生了完美的结果。

包含几个时期的损失函数可能会有所帮助。是否在稳步下降？还是在振荡？有几件事要尝试（如果你还没有）：减少隐藏层的数量。减少批量大小。降低学习速度。@nickandross根据要求进行了更改并包含了数据。可悲的是，结果依然如此。看起来损失并没有稳步减少。也许数据中有问题？我不熟悉TFlearn，但我使用（类似于TFlearn）重现了这个问题，并且我能够获得>90%的准确率。如果你感兴趣，我可以发布我的代码。@nickandross 90%很好。请分享你的工作示例作为答案。我将尝试相应地移植和编辑我的帖子。请看我的答案。希望你能把它弄清楚！请选择此答案作为完成正在进行的线程的最佳答案。