python - 线程 Thread-2 中的异常：Python 机器学习错误：Tensorflow 列表超出范围

Question

我正在尝试为我的数据制作分类器。但作为机器学习和 Python 方面的新手，我不断收到一个我无法弄清楚的奇怪错误。我的代码是

代码

from sklearn.preprocessing import OneHotEncoder
import tensorflow as tf
import numpy as np
import scipy.io as cio
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpg
from random import shuffle
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import cv2

a = cio.loadmat("D:/compCarsThesisData/data/misc/make_model_name.mat")

images = "D:/compCarsThesisData/data/image/"
IMG_SIZE = 64
MODEL_NAME = 'Classification'
LR = 1e-3

b = a['make_names']

d = []

for i in range(b.size):
  d.append(b[i][0][0])
  print(d)

labels_dic = {v: k for v, k in enumerate(d)}
print(labels_dic)
indices = np.arange(163)
depth = 163

y = tf.one_hot(indices,depth)

sess = tf.Session()

result = sess.run(y)#,feed_dict=None,options=None, run_metadata=labels_dic)
print(result)
labels = []

labels.append((result,labels_dic))
print(labels)


for root, _, files in os.walk(images):
 cdp = os.path.abspath(root)
 for f in files:
     name,ext = os.path.splitext(f)
     if ext == ".jpg":
       cip = os.path.join(cdp,f)
       ci = mpg.imread(cip)
       image = cv2.cv2.resize(ci,(IMG_SIZE,IMG_SIZE))
       image = np.array(image)
       print(image)


 training_data = []

 training_data.append((image,labels))
 shuffle(training_data)
 np.save('training_data_make_model', training_data)

 testing_data = []
 testing_data.append((image,labels))
 print("TestingDATA",testing_data)

 shuffle(testing_data)

# if the data already created
# training_data = np.load('training_data_make_model.npy')
# testing_data = np.load('training_data_make_model.npy')

train = training_data[:-50000]
test = testing_data[-50000:]

X_train = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)

y_train = [i[1] for i in train]

X_test = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
y_test = [i[1] for i in test]
print("YTEST",y_test)



tf.reset_default_graph()
convnet = input_data(shape=[None,IMG_SIZE,IMG_SIZE,3],name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log', tensorboard_verbose=0)
model.fit({'input': X_train}, {'targets': y_train}, n_epoch=10,
          validation_set=0.1,
          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

我的 .mat 文件的快照是

.mat 文件包含单元格数组，因为现在我使用 ['make_names'] 将汽车名称作为标签并将它们转换为热编码并将它们与训练数据一起附加，如上所示。

.mat 文件

带有标签和 .mat 文件的附加数据如下所示

带有一个热编码的标签

[[1. 0. 0. ... 0. 0. 0.]
 [0. 1. 0. ... 0. 0. 0.]
 [0. 0. 1. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 1. 0. 0.]
 [0. 0. 0. ... 0. 1. 0.]
 [0. 0. 0. ... 0. 0. 1.]]
[(array([[1., 0., 0., ..., 0., 0., 0.],
       [0., 1., 0., ..., 0., 0., 0.],
       [0., 0., 1., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 1., 0., 0.],
       [0., 0., 0., ..., 0., 1., 0.],
       [0., 0., 0., ..., 0., 0., 1.]], dtype=float32), {0: 'ABT', 1: 'BAC', 2: 'Conquest', 3: 'DS', 4: 'Dacia', 5: 'Fisker', 6: 'GMC', 7: 'Gumpert', 8: 'Hennessey', 9: 'Icona', 10: 'Jeep', 11: 'KTM', 12: 'MELKUS', 13: 'MG', 14: 'MINI', 15: 'Mazzanti', 16: 'Noble', 17: 'PGO', 18: 'SPIRRA', 19: 'SSC', 20: 'Scion', 21: 'TESLA', 22: 'TVR', 23: 'Tramontana', 24: 'Zenvo', 25: 'smart', 26: 'Yiqi', 27: 'Mitsubishi', 28: 'Shangqidatong', 29: 'Spyker N.V.', 30: 'Dongnan', 31: 'Dongfeng', 32: 'Dongfengxiaokang', 33: 'Dongfengfengdu', 34: 'Dongfengfengshen', 35: 'Dongfengfengxing', 36: 'Zxauto', 37: 'Zhonghua', 38: 'Toyota', 39: 'Zinoro', 40: 'Jiulong', 41: 'Isuzu', 42: 'Wuling', 43: 'AC Chnitzer', 44: 'Zoyte', 45: 'Iveco', 46: 'Bufori', 47: 'Porsche', 48: 'Mitsuoka', 49: 'Chrysler', 50: 'Lamorghini ', 51: 'Kombat', 52: 'Cadillac', 53: 'Buck', 54: 'Lifan', 55: 'Lorinser', 56: 'Rolls-Royce', 57: 'BAW', 58: 'Baihc', 59: 'Beiqiweiwang', 60: 'Beiqihuansu', 61: 'Beiqi New Energy', 62: 'Huapu', 63: 'Huatai', 64: 'Huaqi', 65: 'Carlsson', 66: 'Shuanghuan', 67: 'Shuanglong', 68: 'Geely', 69: 'Venucia', 70: 'Haval', 71: 'Hafei', 72: 'Volkswagen', 73: 'Daihatsu', 74: 'Chrey', 75: 'Besturn', 76: 'Benz', 77: 'Audi', 78: 'Wisemann', 79: 'Wealeak', 80: 'BWM', 81: 'Baojun', 82: 'Bentley', 83: 'Brabus', 84: 'Bugatti', 85: 'Pagani', 86: 'Guangqichuanqi', 87: 'GAC', 88: 'Karry', 89: 'Ciimo', 90: 'CHTC', 91: 'Jaguar', 92: 'Morgan', 93: 'Subaru', 94: 'Skoda', 95: 'Xinkai', 96: 'Nissan', 97: 'Changhe', 98: 'RANZ', 99: 'Honda', 100: 'Lincoln', 101: 'Peugeot', 102: 'Opel', 103: 'Oley', 104: 'BYD', 105: 'Jonway', 106: 'Huizhong', 107: 'Jianghuai', 108: 'Jiangling', 109: 'Vauxhall', 110: 'Volvo', 111: 'Ferrari', 112: 'Haige', 113: 'Haima', 114: 'Haima(Zhengzhou)', 115: 'Cheetah', 116: 'Maserati', 117: 'Hyundai ', 118: 'Everus', 119:
'Ruiqi', 120: 'Fuqiqiteng', 121: 'Ford', 122: 'Futian', 123: 'Fudi', 124: 'Koenigsegg', 125: 'HongQi', 126: 'Luxgen', 127: 'SAAB', 128: 'Denza', 129: 'Yingzhi', 130: 'Infiniti', 131: 'Roewe', 132: 'Lotus', 133: 'FIAT', 134: 'Saab', 135: 'Lancia', 136: 'Seat', 137: 'Qoros', 138: 'Acura', 139: 'KIA', 140: 'Lotus', 141: 'LAND-ROVER', 142: 'McLaren', 143: 'Maybach', 144: 'Dodge', 145: 'Mustang', 146: 'Jinlv', 147: 'Jinbei', 148: 'Suzuki', 149: 'GreatWall', 150: 'Changan Business', 151: 'Changan', 152: 'Alfa Romeo', 153: 'Aston Martin', 154: 'Lufeng', 155: 'Shanqitongjia', 156: 'Chevy', 157: 'Citroen', 158: 'Lexus', 159: 'Renault', 160: 'Shouwang', 161: 'MAZDA', 162: 'Huanghai'})]

但是每次我尝试运行它时，它都会给我一个错误。

错误

Run id: Classification
Log directory: log/
Exception in thread Thread-2:
Traceback (most recent call last):
  File "C:\Users\zeele\Miniconda3\lib\threading.py", line 916, in _bootstrap_inner

    self.run()
  File "C:\Users\zeele\Miniconda3\lib\threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)                                     1, in fill_batch_ids_queue
  File "C:\Users\zeele\Miniconda3\lib\site-packages\tflearn\data_flow.py", line 201, in fill_batch_ids_queue                                                        5, in next_batch_ids
    ids = self.next_batch_ids()
  File "C:\Users\zeele\Miniconda3\lib\site-packages\tflearn\data_flow.py", line 215, in next_batch_ids
    batch_start, batch_end = self.batches[self.batch_index]
IndexError: list index out of range

标签： pythontensorflowmachine-learningclassificationtflearn